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Processing of Coconut Water with High-Pressure Carbon Dioxide Technology


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PROCESSING OF COCONUT WATER WITH HIGH PRESSURE CARBON DIOXIDE TECHNOLOGY By SIBEL DAMAR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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To my Mom and Dad

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iii ACKNOWLEDGMENTS I would like to thank my advisor Dr. Murat O. Balaban for his guidance and invaluable support in all stages of my resear ch. He taught me how to do research, how to work in a team, how to be productive and also prepared me for a professional environment. I would also like to thank Dr Marty R. Marshall, Dr. Russell L. Rouseff and Dr. Bruce A. Welt for their guidance in in strumental analysis. My special thanks go to Dr. Charles A. Sims, Dr. Robert P. Bate s and Dr. Ramon C. Litte ll for sharing their expertise and contributing to my dissertation. I also would like to thank my dear fr iends Gogce and Stefan for their help throughout my research. My speci al thanks go to my dad a nd mom for their invaluable support and making life easier for me, thr ough all stages of my doctoral work. El Salvador Farms (Homestead, FL) provi ded the coconuts, th eir contribution is appreciated.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................xi ABSTRACT.....................................................................................................................xi v CHAPTER 1 INTRODUCTION...........................................................................................................1 2 LITERATURE REVIEW................................................................................................4 Coconut Water: Compositi on and Characteristics........................................................4 Flavor Analysis.............................................................................................................8 Introduction...........................................................................................................8 Instrumental Analysis..........................................................................................10 Gas chromatography/olfactometry (GC/O)..................................................10 Solid phase microextraction (SPME)...........................................................11 Sensory Analysis.................................................................................................12 Coconut Flavors...................................................................................................13 Thermal Processing Methods.....................................................................................15 Pasteurization......................................................................................................15 Ultrapasteurization..............................................................................................15 Ultra High Temperature (UHT)...........................................................................15 Heat Pasteurization of Juices...............................................................................16 Non-thermal Processing Methods...............................................................................17 Dense Phase Carbon Dioxide Technology.................................................................19 Mechanisms of Microbial Inactivation by DPCD...............................................19 pH lowering effect........................................................................................20 Inhibitory effect of molecular CO2 and bicarbonate ion..............................22 Physical disruption of cells..........................................................................23 Modification of cell membrane and extraction of cellular components.......25 Inactivation of Vegetative Cells by DPCD.........................................................27 Inactivation of Spores by DPCD.........................................................................34 Inactivation of Enzymes by DPCD.....................................................................37 DPCD Treatment Systems...................................................................................41

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v DPCD Food Applications and Quality Effects....................................................45 Objectives of the Study...............................................................................................48 3 MATERIALS AND METHODS...................................................................................49 Preliminary Experiments with Coconuts....................................................................49 Juice Extraction and Initial Quality Tests...........................................................49 Pinking of Coconut Water...................................................................................49 Tests with Commercial Coconut Water Drinks...................................................51 Extraction of Coconut Water from Coconuts.............................................................51 Formulation of Coconut Water Beverage...................................................................52 DPCD Processing Equipment.....................................................................................53 Continuous-flow DPCD System..........................................................................53 Cleaning of the Equipment..................................................................................53 Heat Pasteurization Equipment...................................................................................54 Carbonation Equipment..............................................................................................55 Optimization of DPCD Treatment C onditions for Microbial Reduction...................56 Aging of Coconut Water.....................................................................................56 Experimental Design...........................................................................................56 Storage Study..............................................................................................................58 Microbial Tests....................................................................................................59 pH........................................................................................................................60 Titratable Acidity (%TA)....................................................................................60 oBrix.....................................................................................................................60 Color....................................................................................................................60 Sensory Evaluation..............................................................................................61 Flavor Analysis....................................................................................................62 Data Analysis..............................................................................................................64 4 RESULTS AND DISCUSSION....................................................................................65 Formulation of Coconut Water Beverage...................................................................65 Objective 1: Quantification of Microb ial Reduction in Coconut Water as a Function of Treatment Conditions.........................................................................65 Objective 2: Evaluation of Physical, Ch emical and Microbial Quality of DPCD Treated Coconut Water Beverage during Storage.................................................70 Objective 3: Comparison of Untreated C ontrol, DPCD and Heat Treated Coconut Water by Sensory Evaluation.................................................................................78 Objective 4: To Identify Flavor Co mpounds in Coconut Water and Compare Flavor Profile of DPCD and Heat Treated Coconut Water...................................87 5 CONCLUSIONS............................................................................................................94 APPENDIX A RESULTS OF PRELIMINARY TE STS WITH COCONUT WATER.......................98 B BOX-BEHNKEN EXPERIMENTAL DESIGN, DATA and ANALYSIS................102

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vi C GC/O AND GC/MS FLAVOR ANALYSIS DATA AND RESULTS......................106 D STORAGE STUDY: MICROBIAL, CHEMICAL AND PHYSICAL QUALITY DATA.......................................................................................................................117 E STORAGE STUDY TASTE PANELS: DATA AND ANALYSIS...........................125 LIST OF REFERENCES.................................................................................................151 BIOGRAPHICAL SKETCH...........................................................................................160

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vii LIST OF TABLES Table page 2-1. A summary of contents for co conut water and human blood plasma.........................7 2-2. Chemical and physicochemical composition of green coconuts.................................7 2-3. Mineral composition of tender coconut water.............................................................8 2-4. Volatile compound classes a nd their sensory characteristics......................................9 2-5. Non-volatile compound classes a nd their sensory characteristics.............................10 2-6. Summary of the studies on inac tivation of various microorganisms.........................32 2-7. Summary of studies on spore inactivation by DPCD................................................36 2-8. Summary of studies on in activation of en zymes by DPCD......................................39 3-1. Three factor-3 level Box-Behnken experimental run coded variables and conditions.................................................................................................................57 3-2. Temperature programming conditions used for GC/O runs with DB-5 and Carbowax columns...................................................................................................64 4-1. Log microbial reducti ons at each experimental point determined by BoxBehnken design........................................................................................................67 4-2. Comparison of overall mean values for sensory attributes from different treatments ( =0.05)..................................................................................................86 4-3. The percentages of panelists answ ering “yes” to the question: Would you buy that product?.............................................................................................................86 4-4. The percentages of panelists answeri ng “no” the first purch ase intent question and answering still “no” the second pur chase intent question: Would you buy this product if you knew coconut water had rehydrating properties?......................87 4-5. The list of flavor compounds that we re identified in untr eated fresh coconut water.........................................................................................................................8 9

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viii 4-6. Standard chemicals (10 ppm of each in a mixture) that were run in GC/O with DB-5 column............................................................................................................91 4-7. Standard chemicals (100 ppm each in a mixture) that were run in GC/O with Carbowax column....................................................................................................92 4-8. The descriptors given by sniffers for th e flavor compounds identified in coconut water.........................................................................................................................9 2 A-1. Initial aerobic plate count (APC) and yeast and mold (YM) counts for coconut water from eight immature green coconuts..............................................................98 A-2. Day 9 aerobic plate count (APC) and yeast and mold (YM) counts for coconut water from selected coconuts of eight immature green coconuts............................98 A-3. Preliminary pinking test 1: Visual obs ervation of the color of coconut water after different treatments during storage at 4oC in glass tubes.........................................99 A-4. Preliminary pinking test 2: Visual obs ervation of the color of coconut water after different treatments during storage at 4oC in opaque plastic cups...........................99 A-5. Preliminary pinking test 3: Visual colo r observation of untreated, heat treated or aerated coconut water during st orage in glass tubes at 4oC...................................100 A-6. The pH, oBrix and ingredients of commer cially available coconut water beverages................................................................................................................100 B-1. The average initial and final aerobic plate counts (APC ) standard deviations at 15 experimental runs from 3-factor, 3-level Box-Be hnken experimental design..102 B-2. SAS software code used for the response surface methodology (RSM) analysis of 15 experimental runs determined by Box-Behnken experimental design.........103 B-3. SAS software output of the res ponse surface methodology (RSM) regression analysis of 15 experimental-run data determined by Box-Behnken experimental design including variables X1 (coded va riable for Temperature), X2 (coded variable for Pressure) and X3 (coded variable for %CO2 level)............................103 B-4. SAS software output of the res ponse surface methodology (RSM) regression analysis of 15 experimental-run data determined by Box-Behnken experimental design including variables X1 (coded vari able for Temperature) and X3 (coded variable for %CO2 level)........................................................................................104 C-1. Excel output of alkane standards’ linear retention index (LRI) calculations in GC/O with a Carbowax column.............................................................................106 C-2. Excel output of alkane standards’ linear retention index (LRI) calculations in GC/O with a DB-5 column.....................................................................................107

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ix C-3. Retention times (RT), linear retenti on indices (Wax LRI) and GC/MS degree of match values of four mixed group of st andard chemicals that were run in GC/MS for possible confirmation..........................................................................111 C-4. Flavor compounds identified in co conut water through GC/O runs: Retention times, calculated Linear Retention Indices (LRI’s) and aroma descriptors given by sniffers in GC/O runs with DB-5 and Carbowax columns................................113 C-5. Peak areas of the sniffed compounds (olfactory port res ponses) and the aroma descriptors given by sniffe rs for DPCD treated (25oC, 34.5 MPa, 13% CO2, 6 min) and carbonated coconut water samples in GC/O with Carbowax column....114 C-6. Peak areas of the sniffed compounds (olfactory port res ponses) and the aroma descriptors given by sniffe rs for heat treated (74oC, 15 s) and carbonated coconut water in GC/O with Carbowax column....................................................115 D-1. Total aerobic plate counts (APC) of untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC).......................................................................................................................117 D-2. Excel outputs of one-ta il t tests conducted for compar ison of mean aerobic plate counts (APC) and yeast and mold (YM) counts for week 0 and week 9 samples.117 D-3. Aerobic plate counts (APC) and yeast a nd mold (YM) counts of sterile distilled water before and after carbona tion with the Zalhm carbonator.............................120 D-4. Yeast and mold (YM) counts of untreated, heat treated (74oC, 15 s) and DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) coconut water beverages during storage....................................................................................................................120 D-5. The pH of untreated, DPCD treated a nd heat pasteurized samples during storage120 D-6. SAS software output of analysis of variance (ANOVA) for the pH data of different treatments from the storage study............................................................121 D-7. The oBrix of untreated, DPCD treated an d heat pasteurized samples during storage....................................................................................................................121 D-8. SAS software output of an alysis of variance (ANOVA)for oBrix data of different treatments from the storage study..........................................................................122 D-9. Titratable acidity (as % malic acid (w /v)) of untreated, DPCD treated and heat pasteurized coconut water beverages during storage.............................................123 D-10. SAS software output of analysis of variance (ANOVA) for % titratable acidity data of different treatm ents from storage study.....................................................123

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x D-11. The mean L*, a*, b* values of untreated, DPCD (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water be verages during storage.........124 E-1. Taste panel data output obtained by Compusense software: Sensory evaluation scores of treatments during the storag e study (Evaluation score scales: Overall likeability: 9 point scale; Aroma differen ce and taste difference from control: 15 cm line scale; Off flavor: 6 point scale; Purchase intent and ask again: 1=Yes and 2=No)...............................................................................................................125 E-2. SAS software output of analysis of variance (ANOVA) for “overall likeability” data for untreated, DPCD and heat treated coconut water by panelists.................144 E-3. The weekly mean “overall likeability ” scores for untreated, DPCD and heat pasteurized samples during storage........................................................................144 E-4. SAS software output of analysis of variance (ANOVA) for “aroma difference from control scores” (corrected data) of different treatments during storage study145 E-5. The weekly mean “aroma difference fr om control” scores for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC).....................................................................................145 E-6. SAS software output for analysis of variance (ANOVA) for “taste difference from control scores” (corrected data) of different treatments during the storage study.......................................................................................................................146 E-7. The weekly mean “taste difference fr om control” scores for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC).....................................................................................146 E-8. SAS software output for analysis of va riance (ANOVA) of “off flavor” scores of different treatments during storage study...............................................................147 E-9. The weekly mean “off flavor” scores for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC)...........................................................................................................147 E-10. Sample ballots that we re used in sensory panels throughout the storage study (Output obtained by Compusense software)..........................................................148

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xi LIST OF FIGURES Figure page 2-1. Cross section of coconut ( Cocos nucifera ) fruit.........................................................4 2-2. Coconut producing areas of the world.........................................................................5 2-3. Measured and calculated pH of pure water pressurized with CO2 up to 34.5 MPa..20 2-4. Scanning electron micrographs (SEM) of untreated (a) and DPCD treated (b) S.cerevisiae cells.....................................................................................................25 2-5. Transmission electron micrographs ( TEM) of untreated (a) and DPCD (b,c) treated L.plantarum cells at 7 MPa, 30oC, 1 h.........................................................26 2-6. A typical batch DPCD system...................................................................................42 2-7. A continuous micro-bubble DPCD system...............................................................43 2-8. A continuous CO2 membrane contactor system........................................................44 2-9. A continuous flow DPCD system..............................................................................45 3.1. Schematic drawing of heat pasteurization equipment...............................................55 3-2. Schematic drawing of steps followed in preparation of storage study samples........59 4-1. Geometry of the 3-fact or 3-level Box-Behnken design.............................................66 4-2. Plots of the response surface for the quadratic model with the variables X1: Temperature (coded) and X3: %CO2 level (coded).................................................69 4-3. Total aerobic plate counts (APC) of unt reated control, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa,13% CO2 for 6 min; Heat treatment at 74oC for 15 s)....................................................................71 4-4. Yeast counts of untreated control, DP CD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s).......................................................................................71

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xii 4-5. The pH of untreated, DPCD and h eat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s)...........................................................................................................73 4-6. The oBrix of untreated, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s)...........................................................................................................74 4-7. Titratable acidity (as % malic acid (w /v)) of untreated, DPCD treated and heat pasteurized samples during st orage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s)......................................................76 4-8. Mean L* values of untreated contro l, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s).......................................................................................77 4-9. Mean a* values of untreated contro l, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s).......................................................................................77 4-10. Mean b* values of untreated contro l, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s).......................................................................................78 4-11. Comparison of overall likeability of each treatment during storage.......................80 4-12.The frequency histograms of storage study aroma difference from control scores of untreated (control) samples..................................................................................82 4-13.The frequency histograms of storage study taste difference from control scores of untreated (control) samples..................................................................................83 4-14. Comparison of treatments for arom a difference from control scores during storage......................................................................................................................84 4-15. Comparison of treatments for taste diffe rence from control scores during storage.84 4-16. Comparison of treatments for off flavor scores during storage...............................85 4-17. Comparison of aromagrams of DPCD (25oC, 34.5 MPa, 13% CO2, 6 min) and heat (74oC, 15 s) treated carbonated coconut water beverages obtained from olfactory port responses (2 weeks storage at 4oC)...................................................93 A-1. Pictures of coconut water from eight immature green coconuts at day 0 (left) and day 9 (right)..............................................................................................................99 A-2. Pictures showing the steps of extr action of coconut water from coconuts.............101

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xiii C-1.Plot of the formula rela ting the LRI’s to the retenti on times for aroma compounds in GC/O with a Carbowax column.........................................................................106 C-2. Plot of the formula relating the LRI’s to the retention times for aroma compounds in GC/O with a DB-5 column.............................................................107 C-3. An example of GC/MS peak identific ation using National Institute of Science and Technology (NIST) library database...............................................................108 C-4. GC/MS chromatograms of the four mi xed groups of standard chemicals that were run in GC/MS for a possible confirmation....................................................110 C-5. Sample GC/MS chromatograms obt ained by running fresh coconut water samples...................................................................................................................112 C-6. GC/MS chromatograms of DPCD tr eated (coconut0011 and coconut0013) and heat treated (coconut0013 and coco nut0014) coconut water beverages (carbonated)............................................................................................................116

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xiv Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PROCESSING OF COCONUT WATER WITH HIGH PRESSURE CARBON DIOXIDE TECHNOLOGY By Sibel Damar August 2006 Chair: Murat O. Balaban Major Department: Food Science and Human Nutrition Coconut water, the clear liquid inside im mature green coconuts, is highly valued due to its nutritional and ther apeutic properties. It has been successfully used in several parts of the world for oral rehydration, treatment of chil dhood diarrhea, gastroenteritis and cholera. This juice is mostly consumed locally as fresh in tropical areas since it deteriorates easily once exposed to air. Commercially, it is thermally processed using ultra high temperature (UHT) technology. Howeve r, coconut water lose s its delicate fresh flavor and some of its nutrients during h eating. A non-thermal process is desirable to protect the fresh flavor and nutrient content of coconut wa ter, which would increase marketability of this healthy drink and avai lability to consumers throughout the world. This study evaluated the effects of dense phase CO2 (DPCD) pasteurization on sensory, physical and chemical quality of a coconut water beverage. The coconut water beverage was formulated by acidification with ma lic acid to pH around 4.30, sweetened with Splenda (0.7% w/w) and carbonated at 1.82 atm CO2 at 4oC. Microbial reduction was

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xv quantified as a function of pr essure, temperature and % CO2 level. Optimum DPCD treatment conditions for microbial inactiv ation were determined to be 13% CO2, 25oC, 34.5 MPa for 6 min. Quality attributes such as pH, oBrix, % titratable acidity (%TA) and color of DPCD treated, fres h and heat pasteurized (74oC for 15 s) coconut water beverages were measured and compared throughout refrigerated storage (4oC for 9 weeks). DPCD treatment did not cause a change in pH or oBrix. The color of coconut water eventually turned pink during storage, independent of treat ment. Sensory panels showed that DPCD treated coconut water wa s liked as much as fresh coconut water; whereas heat pasteurized coconut wa ter was significantly less liked ( =0.05) at the beginning of storage. Flavor compounds of immature coconut water were identified. Flavor profiles showed that heat treated co conut water had more aroma active compounds than DPCD treated coconut water. This study showed that a fresh-like tasti ng coconut water beverage can be produced by DPCD technology with an extended shel f-life of more than 9 weeks at 4oC.

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1 CHAPTER 1 INTRODUCTION Coconut water, as a tropical fruit juice, is highly valued and consumed in tropical areas since it is tasty and has desirable nut ritional and therapeutic properties. The total world coconut cultivation area was estimated in 1996 at 11 million hectares (ha), and around 93% was found in the Asian and Paci fic regions (Punchihewa and Arancon 2005). Indonesia, the Philippines, and India ar e the largest producers of coconut in the world. Coconut ( Cocos nucifera Linn.) fruit is filled with the sweet clear liquid “coconut water” when the coconut is about 5 to 6 m onths old. Coconut water has been called the “fluid of life” due to its me dicinal benefits such as or al rehydration, treatment of childhood diarrhea, gastroenteritis and chol era (Kuberski 1980, Carpenter and others 1964). It is high in electrolyte content and ha s been reported as an isotonic beverage due to its balanced electrolytes like sodium and potassium that help restore losses of electrolytes through skin and urinary pathways. Coconut water was claimed as a natural contender in the sports drink market with its delicate aroma, taste and nutritional characteristics together with the functional characteristics required in a sports drink (Food and Agricultural Organization [FAO] 2005). The constituents of coconut water are wate r 94% (w/v), sugars such as glucose, fructose and sucrose around 5% (w/v), prot eins around 0.02% (w/v) and lipids only about 0.01% (w/v). It is rich in minerals su ch as potassium, calcium, magnesium and manganese, and low in sodium.

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2 Most coconut water is consumed fresh in tropical coastal areas due to its short shelf-life. Once exposed to air, it loses most of its sensory and nutri tional characteristics and deteriorates. Commercially, juice production is carried out mostly in Indonesia, the Philippines, and Thailand using ultra high temp erature (UHT) sterilization while some of coconut water’s nutrients and its delicate flav or are lost during this thermal processing (FAO 2005), which limits the product’s marketability. Usually juices are pasteu rized by a low temperature long time (LTLT) process at about 145oF (63oC) for 30 min or a high temperature short time (HTST) process at about 162oF (72oC) for 15 s. Resulting shelf-life is about 2 to 3 weeks under refrigeration (lower than 7oC). Heat treatment can cause signifi cant reduction in physical, nutritive and sensory quality of foods. Flavor changes in foods due to heating have been reported by many studies (Shreirer and others 19 77, Shaw 1982, Bell and Rouseff 2004). Nonthermal processing methods have been receivin g an increasing interest as alternative or complementary processes to traditional th ermal methods because they minimize quality degradation by keeping the food temperature below the temperatures used in thermal processing. Dense phase CO2 (DPCD) technology is a non-th ermal method emerging as an alternative to traditional ther mal pasteurization. It is a co ld pasteurization method that does not use heat to destroy microorganisms and enzymes, but instead uses the molecular effects of CO2 at pressures lower than 50 MPa. Therefore, DPCD pasteurized foods are not exposed to adverse effects of heat, and ar e expected to retain their fresh-like physical, nutritional and sensory qualities.

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3 The lethal effects of CO2 under high pressure on microorganisms have been investigated since the 1950’s. Carbon dioxide is suitable for use in foods since it is a nontoxic, non-flammable, and an inexpensive gas. It is a natural constituent of many foods, and has generally recognized as safe (GRAS) status. The study of Fraser (1951) is the first research showing that CO2 can inactivate bact erial cells under high pressure. Since then many researchers inves tigated effects of DPCD on microorganisms (pathogenic and spoilage organisms, vegetative cells and spor es, yeasts and molds), enzymes, and quality attributes of foods. Within the last two decades, the number of research studies and patents has increased, and commercialization efforts intensified. DP CD is one of the emerging non-thermal technologies that satisfi ed FDA’s requirement of 5 log pathogen reduction for juice manufacturers. DPCD technology has a great potential for us e in the fruit juice industry especially for tropical fruits that have limited availabi lity to consumers throughout the world. This study evaluated the use of DPCD technology with coconut water regarding microbial inactivation, and physical, chemical and sens ory quality evaluation. Objectives of this study included quantification of microbial in activation as a function of DPCD treatment conditions, evaluation of beverage quality dur ing storage, comparison of DPCD treated coconut water beverage with fresh and heat treated coconut water be verages, and finally the identification of fl avor compounds in coconut water and comparison of flavor profiles for heat treated and DPCD treated beverage s. The demonstrated quality retention and shelf-life extension in coconut water with DPCD technology would increase its marketability and availab ility to the consumer.

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4 CHAPTER 2 LITERATURE REVIEW Coconut Water: Composition and Characteristics The coconut ( Cocos nucifera Linn.) fruit, egg-shaped or elliptic, consists of a fibrous outer layer called coconut husk (meso carp), which covers a ha rd layer called shell (endocarp). Inside the shell is a kernel (e ndosperm), which is considered the most important part of the fruit. It is the source of various coconut products such as copra, i.e., the dried meat of mature fruit with 5% wate r content, coconut oil, coconut milk, coconut water and coconut powder. The cavity within the kernel contains coconut water (Figure 2-1) (Woodroof 1979). This part begins to form as a gel when the coconut is about 5 to 6 months old, becomes harder and whiter as coc onut matures, and the inside is filled with coconut water (Oliveira and ot hers 2003). An immature coconut between 6 to 9 months contains about 750 mL of water that ev entually becomes the flesh (FAO 2005). Figure 2-1. Cross section of coconut ( Cocos nucifera ) fruit

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5 Total world coconut cultivation area in 1996 was estimated at 11 million hectares (ha), and around 93% is found in Asian and P acific regions (Figure 2-2) (Reynolds 1988). The two biggest producers, Indonesia and the Philippines, have about 3.7 million ha and 3.1 million ha, respectively. India is the thir d largest producer. In the South Pacific countries, Papua New Guinea is the leading prod ucer. In Africa, Tanz ania is the largest producer while in Latin America Brazil account s for more than one half of the total coconut area for that region (Punchihewa and Arancon 2005). Figure 2-2. Coconut producing areas of the world Coconut water has been called the “fluid of life” in many parts of the world due to its medicinal benefits. It has been report ed as a natural isotonic beverage due to electrolytes like sodium and potassium, and its isotonic prope rties are demonstrated by its osmol (the number of moles of osmotically acti ve particles; 1 mole of glucose, which is not ionizable, forms 1 osmol, 1 mole of sodi um chloride forms 2 osmols) concentration, which lies in the range of 300-330 mOsmol/ kg (Gomes and Coelho 2005). With its high

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6 electrolyte content, it has been studied for its potential use as an oral rehydration solution. Comparison of coconut water with a “carbohydra te electrolyte beverage” resulted in similar rehydration indices (SDcoconut 2005) There are many reports of its successful use in gastroenteritis or diarrhea (Kuberski 1980 ). It is suggested as a readily available source of potassium for cholera patients (C arpenter and others 1964). Coconut water resembles blood plasma in its contents. Its successful intravenous use has been documented (Falck and others 2000). Duri ng the Pacific War of 1941-45, coconut water was siphoned directly from the nut to w ounded soldiers for emergency plasma transfusions (FAO 2005). Although its glucos e, potassium, magnesium and calcium levels are higher and sodium content is lower than blood plasma, studies on its intravenous infusion show no allergenic or se nsitivity reactions (Fries and Fries 1983). A summary of the contents of coconut water a nd normal blood plasma is given in Table 2-1. Campos and others (1996) determin ed the chemical and physicochemical composition of a pool of coconut water from 30 green coconuts. They measured water content, total solids, soluble solids, total su gars, reducing sugars, ash, protein, lipids, total phenolics, total titratable acidi ty and turbidity (Table 2-2). Carbohydrates are the main constituents of coconut water, and glucose and fructose are the most abundant soluble solids in green coconuts, while sucrose is th e main one in ripe coconuts (Oliveira and others 2003).

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7 Table 2-1. A summary of contents for coconut water and human blood plasma Study Specific gravity pH Na+ meq/ L K+ meq/ L Clmeq/ L Glucose g/L Ca+2 meq/ L PO4 meq/ L Mg+2 meq/L Pradera and others 1942* 1.018 ---5.0 64 45.5 1.2 17 2.8 ---Elseman 1954* ---5.6 4.2 53.7 57.6 1.8 9 2.4 17 Rajasurya 1954* 1.02 4.8 ---38.2 21.3 ---14.5 4.4 ---DeSilva 1959* 1.02 4.9 ------------------19 Olurin 1972* 1.02 5.6 0.7 81.8 38.6 ---3.6 3.2 25 Iqbal 1976* 1.019 5.6 5.0 49 63 2.1 12 8 4.7 Kuberski 1979* ------4.0 35.1 41 2.8 13.1 4 5.2 Msengi 1985* 1.023 6.0 2.9 49.9 ------5.3 ---13.4 Atoiffi 1997* ---4.2 9.7 43.1 39.8 1.73 ---------Normal plasma 1.027 7.4 140 4.5 105 0.1 5.0 2.0 1.8 (* Cited in Falck and others 2000) Table 2-2. Chemical and physicochemical composition of green coconuts Water (g/100 mL) 94.2 1.90 Total solids (g/100 mL) 5.80 0.12 Soluble solids (Brix, 20oC) 5.27 0.11 Total sugars (g/100 mL) 5.30 0.21 Reducing sugars (g/100 mL) 4.90 0.20 Non-reducing sugars (g/100 mL) 0.40 0.04 Ash (g/100 mL) 0.50 0.01 Protein (mg/100 mL) 19.50 0.50 Lipids (mg/100 mL) 11.00 0.60 Total phenolics (mg catechin/100 mL) 6.86 0.55 Total titratable acidity (mg citric acid/100 mL) 131.20 2.80 pH 5.20 0.10 Transmittance (%) 81.00 1.70 (Campos and others 1996)

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8 Coconut water is rich in mineral compositi on (Table 2-3). It is high in potassium, calcium, magnesium, and manganese, and low in sodium. Coconut water is low in fat and proteins. It is rich in many essential ami no acids such as lysine, leucine, cystine, phenylalanine, histidine and tryptophan (Prade ra and others 1942). It s arginine, alanine, cysteine and serine percentage is higher than those of co w’s milk (Maciel and others 1992). It contains ascorbic ac id and B complex vitamins. Ascorbic acid content of coconut water from a 7-9 month coconut has been reported to be 2.2 to 3.7 mg/100 mL (Mantena and others 2003). Coconut water is low in calories with a caloric value of 17.4 kcal/100 g (Woodroof 1979). Table 2-3. Mineral composition of tender coconut water (Krishnankutty 2005) Coconut water is mostly consumed fresh in tropical coastal area s today. In addition, commercial juice production is carried out in Indonesia, the Philippines and Thailand by heating with Ultra High Temperature (UHT). Although thermal processing eliminates bacteria, it causes loss of th e delicate flavor and some nut rients of coconut water. Flavor Analysis Introduction Flavor is a combination of the perceived aroma, taste and trigeminal sensations (Fisher and Scott 1997). Taste sensation has f our major categories; sweet, sour, bitter, Minerals (mg /100 mL) Copper 26 Potassium 290 Sodium 42 Calcium 44 Magnesium 10 Phosphorous 9.2 Iron 106

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9 and salty. Umami is included as the fifth category by some scientists. Trigeminal sensations give the pungency, cooling or as tringency. Taste and trigeminal components of flavor are polar, non-volatile and water-s oluble compounds. Aroma, on the other hand, is created by the volatile compounds. A su mmary of the volatile and non-volatile compounds and the examples of their sensory de scriptors are given in Tables 2-4 and 2-5 (Fisher and Scott 1997). Fruit flavors are a combination of sweet and sour tastes and the characteristic aroma compounds. Sugars such as glucose, fr uctose and sucrose are responsible for the sweetness of the fruit. Organic acids such as malic, citric, tartaric, etc. give sourness. These compounds are common in most fruits. Mo st volatile constituents in fruits contain aliphatic hydrocarbon chains, or their derivatives such as esters, alcohols, acids, aldehydes, ketones and cyclic compounds such as lactones. These compounds are reported as ripening products that develop from two diffe rent sources including fatty acids by several lipid oxidati on pathways, and amino acids via amino acid metabolism. Generally, aromas of citrus fruits are created by terpenoids while that of non-citrus fruits consists of esters and al dehydes (Fisher and Scott 1997). Table 2-4. Volatile compound classes a nd their sensory characteristics Compound class Sensory character Examples Aldehydes Fruity, green, oxidized, sweet Acetaldehyde, hexanal, decanal, vanillin Alcohols Bitter, medicinal, piney, caramel Linalool, menthol, -terpineol, maltol Esters Fruity Ethyl acetate, ethyl butyrate Citrus Geraniol acetate Ketones Butter, caramel Diacetyl, furanones Maillard reaction products Brown, burnt, caramel, earthy Pyrazines, pyridine, furans Phenolics Medicinal, smokey Phenols, guaiacols Terpenoids Citrus, piney Limonene, pinene, valencene (Fisher and Scott 1997)

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10 Table 2-5. Non-volatile compound classes and their sensory characteristics Compound class Sensory character Examples Acids: Amino acids Sweet, sour, bitter Organic acids Sour Citric, malic, tartaric Polyphenolic acids As tringent, bitter Chlorogenic, caffeic Flavonoids Astringent, bitter Flavonols, anthocyanins Phenolics Medicinal, smokey Guaicols, phenols Sweeteners: Sugars Sweet, body Sucrose, glucose, fructose High intensity sweeteners Sweet Aspartame, acesulfame-K (Fisher and Scott 1997) Instrumental Analysis Gas chromatography/olfactometry (GC/O) Gas chromatography (GC) is typically th e method of choice for the analysis of flavor compounds. Initial studies of flavor an alysis were conducted using packed column GC, which gave poor analytical results co mpared to today’s capillary column GC. Combining GC with mass spectrometry (GC/ MS) allowed separation and identification of numerous volatile compounds (Mistry and othe rs 1997). It is possi ble to identify more than 6900 volatile compounds by using these techniques. However, not all of these volatiles have odor impact, only a few give the characteristic odor of the foods. GC olfactometry (GC/O) is an important analytical tool in flavor research to characterize the odors emerging from a sniffing port. GC/O allo ws the separation of odor active chemicals from the volatile chemicals with no or minimal odor response. Due to the complexity of the food matrix and aromas, and low concentration levels of aroma compounds, typically in the parts pe r million (ppm), parts per billion (ppb) or parts per trillion (ppt) ranges, generally isol ation and concentration of the flavors are needed prior to the analysis with GC. The most commonly used techniques are solvent extraction, headspace sampling, and distillati on methods. Each method has advantages

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11 and disadvantages. For example, in headsp ace sampling, analytes are removed from the sample without the use of an organic solv ent. However, this method usually has low sensitivity and can give poor quantitative results (Reineccius 1984). Headspace isolates can be concentrated by the use of cryogenic or adsorbent traps. In cryogenic traps, water is the most abundant volatile isolated from the food and should be removed by additional steps that may cause sample contamination. Adsorbent traps offer advantage of waterfree isolates, but differential affi nity of analytes for adsorbent can result in low sensitivity for some chemicals. Solvent extraction is an accurate qualitative and quantitative method, however, it can be laborious and its use is limited to fat-free f oods. Although distillation is an effective method, it takes a long time and impurities from the system components or thermally induced chemical changes can be a problem. Recently, solid phase microextraction (SPME) has found applica tions and is recommended as a convenient method for sample preparation before GC analysis (Wardencki and others 2004). Solid phase microextraction (SPME) SPME is a relatively new sample prepara tion technique for rapid and solvent-free extraction or pre-concentration of volatile co mpounds before analysis with GC. The key component of SPME is the fused silica fiber co ated with an adsorbent polymeric material. This is an equilibrium technique and utili zes the partitioning of organic compounds in the sample between the aqueous or vapor phase and the thin adsorbent film coating. Adsorbed materials are thermally desorbed in a GC injection port. SPME is a simple, rapid, solvent-free and inexpensive met hod when compared with other sample preparation techniques such as solvent extraction, purge-and-trap, simultaneous distillation/extraction and conventional soli d-phase extraction (Ya ng and Peppard 1994). Each additional step in the analytical proce dure increases the possibility of analyte loss,

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12 sample contamination and analytical error. SPME minimizes the number of steps used in sample preparation by combining extraction and concentration step s. For volatile/semivolatile and non-polar/semi-polar analytes, SPME can reach detection limits of 5-50 pg/g, with an approximate sample preparation time of 15-60 min (Wardencki and others 2004). The effectiveness of the SPME depends on many factors such as type of fiber, sample volume, temperature, extraction time, mode of extraction and desorp tion of analytes from the fiber. The most commonly used comme rcially available fibers are non-polar Polydimethylsiloxane (PDMS), semipolar PDMS/divinylbenzene and polar Carbowax/divinylbenzene. Yang and Peppard (1994) used SPME liquid sampling and solvent extraction with dichloromethane to extract fl avor compounds of a fruit juice beverage and analyzed the compounds by GC/MS. They showed comparab le or higher sensitivity than solvent extraction method for most esters, terpenoids and -decalactone. They also analyzed a vegetable oil for butter flavor by SPME headspace sampling and found that this technique was effective in detection of diacetyl, -decalactone and -dodecalactone. They reported that conventional headspace sampli ng method generally was more sensitive for highly volatile compounds while the SPME h eadspace method picked up more of the less volatile compounds. Sensory Analysis Flavor research studies the effect of ch anges in foods on flavor, and characterizes these changes. Consumer acceptability or lik eability of products developed with the new technologies is a major tool for commercia lization. Sensory evalua tion of food provides

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13 guidance for the maintenance, optimization a nd improvement of these products (Lawless and Heymann 1998). Sensory methods commonly used are separa ted into three groups: discriminant, descriptive and affec tive methods. The method of choice depends on which questions are to be asked about the product during the te st. Discrimination methods answer whether any difference exists between products, whil e descriptive tests answer how products differ in specific sensory characteristics and provides quantification of these differences (Lawless and Heymann 1998). Once differences are observed by di scrimination type tests, then descriptive tests can provide further information on the reasons for the differences found. Affective tests are conduc ted to find out how well the products are liked or which products are preferred. Examples of discrimination te sts are triangle, duotrio and paired comparison tests. In some cases, difference-from-control test is used instead of triangle or duo-trio tests, when th e magnitude of difference from a control is important (Miller and others 1998). This test not only assesses difference but also quantifies the magnitude of difference. Coconut Flavors The desirable flavor of co conut water is sweet and slightly astringent, with a pH around 5.6 (Maciel and others 1992). There are a limited number of studies on the analysis of coconut flavor compounds. Lin and Wilkens (1970) identified 15 aroma compounds in coconut meat by GC/MS analysis. Among these, -C8 and –C10 lactones were the major volatile components and were described as buttery, tropical-fruity and coconut-like. The other aroma compounds were octanal, 2-heptanol, 2-octanol, 2nonanol, 2-undecanol, hexanol, octanol, 2-pheny lethanol, benzothiazole, ethyl decanoate

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14 and dodecanoic acid, that were described mostly as fruity and also as nutty, rancid, green, lemon and rose aromas. Jayalekshmy and others (1991) determined aroma compounds of roasted coconut meat by GC/MS. They suggested that roasting of coconut meat led to the formation of heterocyclic aroma compounds, especially pyrazines. The -lactones, alcohols, esters and fatty acids also contributed to the overall roasted coconut flavor They isolated acid, neutral and basic fractions from roasted co conut by selective solvent extraction and pH adjustment. They identified pyrazines and other heterocyclic co mpounds, which gave the roasted aroma, in the basic fraction. Ther e were twenty different types of pyrazines identified, and their amount increased with time of roasting. The GC profile of neutral fraction was dominated by -lactones, and their amount decreased from 80% to 60% during roasting. The basic and acid fractions were dominated by pyrazines and short chain fatty acids, respectively. Jirovetz and others (2003) identified aroma compounds in the coconut milk and meat of ripe coconuts from Cameroon. They extracted headspace volatiles by SPME, and identified more than thirty compounds usi ng GC/MS. The main components of coconut aroma were nonanal, nonanol, heptanal, ethy l octanoate, heptanol and 2-nonanol, while coconut meat was rich in -octalactone, ethyl octanoate nonanal, nonanoic acid, decanol, decanal and nonanol. Other short chain alcohols, aldehydes, ketones, lactones, acids and esters were present in lower concen trations. They did not detect any -lactones or -C14 lactone that were reported in coconut meat by previous res earchers. Although there are a few studies regarding the fla vor compounds in coconut meat or milk, there is no flavor study with coconut water from immature fruit.

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15 Thermal Processing Methods Pasteurization Pasteurization is a mild heat treatment for high-acid foods such as juices and beverages, and low-acid refrigerated foods such as milk and dairy products. It is used in order to inactivate vegetativ e cells of pathogenic microor ganisms. Usually foods are pasteurized by a low temperature lo ng time (LTLT) process at about 145oF (63oC) for 30 min or a high temperature short time (HTST) process at about 162oF (72oC) for 15 s (David and others 1996). The resulting shelflife of the product is about 2 to 3 weeks under refrigerated (lower than 7oC) conditions. The pasteu rization process does not intend to inactivate all spoilage bacteria or any he at-resistant spores, thus the product is not commercially sterile after past eurization (David and others 1996). Ultrapasteurization The objective of ultrapasteuri zation is similar to pasteu rization but it is done at higher temperatures with shorter exposure ti mes and extends the shelf-life about 6 to 8 weeks under refrigeration. Foods are ultrapasterized at 280oF (138oC) or above for 2 s or longer (David and others 1996). This process is usually used for dairy products, juices and non-dairy creamers. Ultra High Temperature (UHT) Commercially sterile products are obtaine d by a UHT process at temperatures in the range of 265 to 295oF (130 to 145oC) and holding times between 2 and 45 s. The product is aseptically packaged after UHT processing in orde r to obtain a shelf stable product with a shelf life of 1 to 2 years at ambient temperatures.

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16 Heat Pasteurization of Juices Common thermal processes used for juices and soft drinks are flash pasteurization, hot filling, in-pack pasteurization and asep tic filling (Tompsett 1998, Lea 1998). Usually flash pasteurization is done by passing ju ice rapidly through heat ed plates by HTST treatment at 96oC for 4 s, or by standard process at 80oC for 20 s. In hot filling, the product is heated in a heat exchanger above 80oC (typically 87oC) sent to the filler while hot, filled into containers and held for about 2 min. Hot fill process is adequate for acidic beverages to obtain a shelf stable product w ith a shelf-life of 6 to 12 months. In-pack pasteurization is achieved by passing completely filled closed packs through a heating and a superheated zone, and then through a pa steurizing zone for the desired period of time, and finally through a cooling zone. T ypical in-pack processing is done at 74oC for 17 min. A special in-pack process is possible by heating the product above 100oC in a retort and then cooling (Lea 1998, Tompsett 1998). Aseptic filling may involve HTST pasteurization or UHT sterilization, depending on the high-acid or lo w-acid character of the juice, which is then filled into sterile c ontainers in a sterile environment (David and others 1996). The choice of pasteurization method de pends on the level of microbial contamination of the raw mate rials and packaging, the ability of the product to withstand heat, growth potential of microorganisms a nd the pH of the product. In orange and tangerine juice processing, pasteurization does not only kill microorganisms but also inactivates pectines terase. Normally, temperatures above 71oC are enough to kill pathogens and spoilage bacteria in orange juice. However, temperatures between 86 and 99oC are required to inactivate pectinestera se. In commercial practice, orange and tangerine juices are flash pasteurized by heating the juice rapidly to about 92oC for 1 to

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17 40 s (Nordby and Nagy 1980). In lemon jui ce the pectinesterase enzyme can be inactivated at lower temperatures (69 to 74oC), and commercial pasteu rization is done at 77oC for 30 s. Thermal processing methods have been show n to change the flavor of foods. For example, the delicate flavor of fresh orange juice is easily changed by heat treatment. Citrus processors and flavorists search for methods to make processed orange juice and orange-flavored beverages taste more like fr esh orange juice (Sha w 1982). Shreirer and others (1977) reported that some volatile compounds such as -terpineol and carveol, which are formed by the oxidation of d-limone ne, increased and the amount of terpene hydrocarbons decreased in heat pasteurized orange juice. Bell and Rouseff (2004) determined changes in the flavor of grap efruit juice after heat processing. Sensory analysis of juices processed at 100C for 10 min indicated formation of a heated, pineapple, metallic and cooked off-flavor while the initial unheated juice had a typical fresh grapefruit character. Analysis of flavor compounds by GC/O showed that there was at least a 45% decrease in levels of volatile compounds associated with fresh grapef ruit juice after heat processi ng. A corresponding increase in compounds associated with flavor degradat ion such as furaneol and methional was observed after heating. Non-thermal Processing Methods Non-thermal processing methods have gained increasing interest in recent years and several emerging technologies are under inte nse research to eval uate their potential as alternatives to tr aditional thermal methods. Traditi onally, most foods are preserved by subjecting to temperatures between 60oC to 100oC for a certain period of time (BarbosaCnovas 1998). The large amount of energy tran sferred to food during heat treatment

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18 may initiate unwanted reactions and result in undesirable changes in the physical, sensory and nutritional quality of food. Quality degradation is minimized using non-thermal technologies since the food temperature is held below the temperatur es used in thermal processing. Among the emerging non-thermal technologies are ultra hi gh pressure (UHP), high intensity pulsed electric fields (PEF), irradi ation, oscillating magnetic fields, pulsed high intensity light, and dense phase CO2 (DPCD). UHP and irradiation are being used in commercial operations. One of the most important issues in the commercialization of non-thermal technologies is regulatory approval. Proce sses must comply with pasteurization or sterilization requirements of Food and Drug Administration (FDA) a nd also ensure the safety of equipment operators and consumers. Each of these technologies can be used for specific food applications; some are more suitable for liquid products whereas some are appropriate for solids. It is important to determine the qual ity of non-thermally processed foods, especially in cases wher e the nature of the food precl udes use of thermal methods. Evaluation of sensory, nutritional and physic al changes resulting from non-thermal processes is essentia l (Barbosa-Cnovas 1998). Several studies evaluated the quality of fruit juices processed by non-thermal technologies. PEF treated orange juice had si gnificantly higher (P <0.05) ascorbic acid, flavor compounds and color than thermally pr ocessed orange juice (Hye and others 2000, Min and others 2003). Jia and ot hers (1999) showed that ther e was 10 to 40% loss in the major orange juice flavor compounds after heat pasteurization while 0 to 5% losses occurred for the same compound with PEF pr ocessing. Ayhan and others (2002) reported that PEF processing did not alter sensory eval uation of flavor and color of fresh orange

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19 juice. Similarly, Min and others (2003) re ported higher sensory sc ores for flavor and overall acceptability of PEF treated tomato ju ice compared to heat pasteurization. Apple juice retained fresh like ascorbic acid leve ls and color after PEF processing (Akdemir and others 2000, Liang and others 2003). UHP processing at pressures between 100 to 800 MPa has been reported to be effective in inactivation of pathogens without affecting taste or nutri tional value of fresh juices (Morris 2000). UHP treated citrus juices retained a fresh-like flavor with no loss of vitamin C and a shelf-life of approximately 17 months (Farr 1990). Polydera and others (2003) compared shelf-life and ascorbic ac id retention of reco nstituted orange juice processed by heat at 80oC for 30 s with that of UHP processed juice (500 MPa, 35oC, 5 min). UHP processing resulted in 24% to 57% increase in the shelf-life compared to thermal pasteurization. Sensory characterist ics of UHP pasteurized juice were rated superior and ascorbic ac id retention was higher. FDA’s juice HACCP regulations require va lidation of 5 log pathogen reduction for juice manufacturers. Dense phase CO2 (DPCD) is one of the emerging non-thermal technologies that conforms to this requireme nt and has a great potential for commercial use in juice pasteurization. Dense Phase Carbon Dioxide Technology Mechanisms of Microbial Inactivation by DPCD Several hypotheses have been proposed to explain the lethal effects of DPCD on microorganisms. Although the exact means are not clear, studies show that several mechanisms may be involved. DPCD was cl aimed to inactivate microorganisms by:

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20 pH lowering effect CO2 can lower pH when dissolved in the aqueous part of a solution by forming carbonic acid. Carbonic acid further dissociates to give bicarbonate, carbonate and H+ ions lowering extra-cellular pH by the following equations: Meyssami and others (1992) predicted th e pH of simple model liquid foods under DPCD and obtained good correla tions with the experimentally measured pH values. They found that the presence of disso lved materials other than CO2 such as acids and salts had a reducing effect on the lowering of pH by DPCD treatment (Figure 2-3). 0 10 20 30 Process Pressure, P(MPa) p H 7 6 5 4 3 2 1 Measured p H Predicted pH Figure 2-3. Measured and calculate d pH of pure water pressurized with CO2 up to 34.5 MPa 3 2 2 2CO H O H CO 3 3 2HCO H CO H pKa = 6.57 2 3 3 CO H HCO pKa = 10.62

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21 However, the internal pH of microbial ce lls, not the external pH, has the largest effect on cellular destruction. When there is a sufficient amount of CO2 in the environment, it can penetrate through the ce ll membrane, which consists of phospholipid layers, and lowers internal pH by exceeding the buffering cap acity of the cell. Normally, cells have to maintain a pH gradient betw een the internal and external environments. Cellular systems actively pump hydrogen ions from the inside to the outside of the cell. These systems can be overwhelmed with sufficient CO2, reducing internal pH. It is believed that reduced internal pH may in activate microorganisms by the inhibition of essential metabolic systems including enzyme s (Daniels and others 1985, Ballestra and others 1996). Ballestra and others (1996) meas ured the activities of eight enzymes from E.coli cells before and after DPCD treatment (5 MPa, 15 min, 35oC). These enzymes were selectively inactivated. The activity of some enzymes having an acidic isoelectric point such as alkaline phosphatase and -galactosidase disappeared, whereas those with basic isoelectric points such as acid phosphatase were slightly affected. Hong and Pyun (2001) treated L.plantarum cells by DPCD under 7 MPa at 30oC for 10 min, and measured activity of 13 diffe rent enzymes. They also observed that enzymes were inactivated selectively. Some enzymes such as cystine arylamidase, galactosidase, and --glucosidase lost their activities significantly, whereas enzymes such as lipase, leucine arylamidase, and acid a nd alkaline phosphatase were little affected by DPCD treatment. At the sa me time, cell viability of L.plantarum decreased by more than 90% under these conditi ons. They concluded that it was uncertain whether the observed inactivation of some enzymes was a primary cause of cell death. Evidence in

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22 the literature does not specify which of the enzymes men tioned are critical for survival and therefore vital in their inactivation. Inhibitory effect of molecular CO2 and bicarbonate ion Another suggestion to explai n inactivation of bacterial enzymes is the inhibitory effect of CO2 itself on some enzymes (Ishikawa a nd others 1995a). Weder (1990) and Weder and others (1992) claimed that under a low pH environment, arginine could interact with CO2 to form a bicarbonate complex, a nd inactivate the enzyme containing this amino acid. Jones and Greenfield (1982) have shown that decarboxylases are inhibited by excess CO2, breaking the metabolic chain (S pilimbergo and Bertucco 2003). Ishikawa and others (1995a) obtained co mplete inactivation of alkaline protease and lipase at 35oC and 15 MPa treatment by using a micro-bubble system. They compared residual activity of th ese enzymes by supercritical CO2 (SCCO2) to that of low pH (3.0) and concluded that al kaline protease could be inactiv ated due to pH lowering by dissolved CO2; whereas lipase must have been in activated by a different mechanism. They also conducted a study with glucoamylase and acid protease, showing that a higher SCCO2 density resulted in lower residual activity of these enzymes. As a result, they claimed that inactivation of these enzyme s could be caused by the sorption of CO2 into the enzyme molecules. Another mechanism proposed is precip itation of intracellular calcium and magnesium ions by the effect of carbonate (L in and others 1993). When the applied CO2 pressure is released, bicarbona te converts to carbonate, whic h can precipitate intracellular calcium, magnesium and similar ions from the cell and cell membrane. Calcium-binding proteins are known as the most important class involved in in tracellular regulation (Aitken 1990). Certain types of calciumand magnesium-sensitive proteins could be

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23 precipitated by carbonate, depending on the binding site of the ion and chemical structure of the protein. Consequently, a lethal change to the biological system is produced (Lin and others 1993). Physical disruption of cells The first suggested mechanism of inac tivation of microorganisms by DPCD was the physical disruption of cells (Fraser 1951). E.coli cells were almost totally killed under 50.7 MPa in less than 5 min and were thought to burst due to the rapi d release of applied gas pressure and the expansion of CO2 gas within the cell during depressurization. However, the extent of bursting of cells wa s determined by the Petroff-Hauser counting method that uses a microscope for direct cell c ounting. Therefore, it is hard to conclude if the cells were actually burst without observ ation with an electron microscope. Other researchers investigated the physical ruptur e of cells by DPCD as a possible mechanism of inactivation (Lin and others 1991, Nakamu ra and others 1994, Isenschmid and others 1995, Ishikawa and others 1995b, Ballestra a nd others 1996, Dillow and others 1999, Hong and Pyun 1999, Spilimbergo and others 2003, Folkes 2004). Lin and others (1991) claimed that yeast ( S.cerevisiae ) cells could be ruptured by pressurized CO2 under 6.934.4 MPa for 5 to 15 h treatments. They meas ured total protein c oncentration in the supernatant of treated cells as an indication of cell rupture, however they did not have a direct physical observation of cells. They have shown that the amount of total proteins released in the supernatant of DPCD treated cells was about the same amount as in the supernatant of cells autolyzed by other disruption methods The leakage of the proteins into the environment depends on the size of the breach in the cell membrane. Nakamura and others (1994) demonstrated mechanical r upture of yeast cells by DPCD treatment (4 MPa, 40oC for 5 h) by scanning electron micrographs. They observed that some cells

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24 were completely burst whereas some only lo st surface smoothness and had some wrinkles or holes on the membrane surface. Folkes (2004) also observed physical disruption of yeast cells in beer by scanning electron micr ographs (Figure 2-4). The process conditions in a continuous dense phase CO2 pasteurizer were: pressure 27.5 MPa, temperature 21oC, CO2/beer ratio (10%), and re sidence time of 5 min. Although cell rupture is possible during DP CD treatment, it is not necessary for cell inactivation. For instance, Ball estra and others (1996) treated E.coli cells at 5 MPa and 35oC, and observed that more than 25% of cells had intact cell walls while the viability was only 1%. They did not observe cell rupture or burst, but only some signs of deformation in cell walls. There have been studies showing that cells were completely inactivated even when they remained intact after treatment. For example, Hong and Pyun (1999) demonstrated that L. plantarum cells treated with CO2 at 6.8 MPa and 30oC for 60 min were completely inactivated but SEM mi crographs did not show any cell rupture. The morphological changes caused by DPCD may differ based on treatment conditions, gas release rate, or the type of microorgani sm. Dillow and others (1999) observed that SEM micrographs of S.aureus (Gram(+)), P.aeruginosa (Gram(-)), and E.coli (Gram(-)) cell walls were largely unchanged before and after DPCD treatment. However, they found that Gram(-) cells had more defects on th e cell wall after treatment. They explained this by Gram(-) cells having thinner cell walls compared tocompared to Gram(+) cells.

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25 Figure 2-4. Scanning electron micrographs (SEM ) of untreated (a) and DPCD treated (b) S.cerevisiae cells It is important to note here that cells without any rupture, i.e., with intact cell walls could show modifications or damage in microstructural observations. Modification of cell membrane and extraction of cellular components Another mechanism suggested by res earchers is based on the lipoand hydrophilicity and solvent characteristics of CO2. Kamihira and others (1987) mentioned extraction of intracellular substances such as phospholipids by DPCD as one of the possible mechanisms of microbi al inactivation. Isenchmid and others (1995) proposed that molecular CO2 diffused into cell membrane and accumulated there, since the inner layer is lipophilic. Accumulated CO2 increased fluidity of the membrane due to the order loss of the lipid chains, also called the “anesth esia effect”, and the increase of fluidity causes an increase in permeability. Lin a nd others (1992) suggested that once CO2 has penetrated into the cell, it could extract cellular components and transfer extracted materials out of the cell duri ng pressure release. Upon extr action of essen tial lipids or other vital components of cells or cell me mbranes, the cells ar e inactivated. These hypotheses have been investigat ed by several researchers e ither by measuring the amount

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26 of materials in the supernatant of treated ce lls or by some microstr uctural observations on the treated cells. Hong and Pyun (1999) have shown th at although SEM observations of L.plantarum cells had demonstrated intact cell walls after DPCD treatment, microstructural observations by transmission el ectron micrographs (TEM) showed modifications in the cell membrane with possible leakage of cyt oplasm (Figure 2-5). These pictures show enlarged periplasmic space between cell wa lls and the cytoplasmic membranes, and empty spaces in the cytoplasm. In a further study in 2001, Hong and Pyun have shown that cells treated with DPCD at 7 MPa for 10 min and 30oC showed irreversible cellular damage including loss of salt tolerance, leak age of UV-absorbing s ubstances, release of intracellular ions and impaired proton perm eability. They have also used Phloxine B staining on L.plantarum cells as an indication of loss of cell membrane integrity, and shown that cell membrane has lost its integr ity immediately after be ing exposed to high pressure CO2. Figure 2-5. Transmission electr on micrographs (TEM) of untreated (a) and DPCD (b,c) treated L.plantarum cells at 7 MPa, 30oC, 1 h Although the strongest effect of the above mechanisms on microbial destruction by DPCD is still in question, research ers agree in the governing role of CO2. Several researchers have concluded that CO2 has a unique role in inac tivation of cells (Haas and

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27 others 1989, Wei and others 1991, Lin a nd others 1992, Nakamura and others 1994, Ballestra and others 1996, Dillow and others 1999, Hong and Pyun 2001). Haas and others (1989) observed that al tering external pH by acids such as phosphoric and hydrochloric did not cause as much cell inactivation as CO2. These acids cannot enter cells easily as CO2. This implied that the ability of CO2 to penetrate through the cell membrane has a key role in reducing the internal pH of cells. Similarly, Wei and others (1991) added 0.1N HCl to the Listeria suspension to decrease pH by about 1.8 units. The same amount of pH reduction wa s achieved by treatment of cells with CO2 under 6.18 MPa for 2 h. The acidification by HC l did not cause a microbial reduction whereas treatment with CO2 caused complete inactivation. Alternatively, Nakamura and othe rs (1994) have shown that N2 gas when applied under the same conditions as CO2 (4 MPa, 40oC, 4 h) did not have an effect on viability of yeast cells. Lin and others (1992) have shown that 90% of cells survived after treatment with N2 under 6.9 MPa for 20 and 40 min wher eas complete inactivation was achieved after treatment with CO2 in less than 12 min. Similarly, Dillow and others (1999) applied tetrafluoroethane (TFE) to bacterial cells at 38oC and 11 MPa for 45 min and compared the viability of cells with treatment of CO2 under the same conditions. Although TFE did not result in reduction of vi able cells, total inact ivation was achieved by CO2 treatment. Inactivation of Vegetative Cells by DPCD There are a number of studies showing that DPCD is effective in killing vegetative forms of pathogenic and spoilage bacteria, y easts and molds. A summ ary of these studies is given in Table 2-6 including the media, tr eatment conditions, microorganisms, their log reduction, and the type of system used. Th e microbial inactivation achieved by DPCD

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28 changed from 2 and 12 logs, pressures unde r 50 MPa, and temperatures between 5oC to 60oC, mostly in the 25-35oC range. Treatment times were significantly different depending on the treatment system used and c ould be as long as 6 h when batch systems were used, to as low as 2.5 min for c ontinuous or semi-continuous systems. Water activity (aw) of treatment medium and water content of the vegetative cells were shown to have a significant role in the killing effect of DPCD. Kamihira and others (1987) compared inactivation of wet (70-90 % water) and dry (2-10% water) cells of Baker’s yeast, E.coli and S.aureus by DPCD treatment at 20 MPa for 2 h and 35oC. Dry cells were inactivated by less than 1 log whereas wet cells were inactivated by 5 to 7 logs. Haas and others (1989) showed that DPCD was more effective as aw of the food increased. Kumagai and others (1997) studied steriliza tion kinetics of S. cerevisiae cells at various water contents and CO2 pressures. The first order sterilization rate constant, k, was almost zero at water contents below 0.2 g/ g dry matter, and increased with increasing water content. This increase was slight at water contents above 1g/g dry matter. Moreover, k increased with increasing CO2 pressure at an identical water content of cells. Similarly, Dillow and others (1999) compared inactivation kinetics of E.coli cultures in the presence and absence of water in the cell culture when treated with DPCD at 34oC and 14 MPa. They observed that small amounts of water greatly enha nced the sterilizing effect of DPCD. It is important to note that water content of treatment medium and therefore the water content of the cells would increase CO2 solubility in the cells, which would explain increased microbial inactivation. The unique role of CO2 in the inactivation of microbial cells has been shown by many researchers and the details of their studi es were listed in the “Mechanisms” section.

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29 Generally, any effect that incr eases the level and rate of CO2 solubility, and therefore, penetration of CO2 into cells in a treatment medium enhances microbial inactivation by DPCD. For instance, CO2 solubility increases with incr easing pressure, other conditions being equal. However, this increase is limited by the saturation solubility of CO2 in the medium. Generally, inactivation efficiency in creases with higher pressure, temperature and residence time. Nakamura and others (1994) demonstrated that the bactericidal effect of CO2 treatment on baker’s yeast dramatically increased by increasing pressure from 2 to 4 MPa, by increasing temperature from 20 to 40oC, and by increasing treatment time from 0.5 h to 3 h. Hong and others (1999) achieved a 5 log reduction for L.plantarum by DPCD at 30oC. It took 50 to 55 min to achieve this reduction at 6.9 MPa while it took only 15-20 min to achieve the same level of reduction at 13.8 MPa. Isenschmid and others (1995) showed that viability of Kluveromyces fragilis, S.cerevisiae, and Candida utilis decreased with increasing CO2 pressure following a typi cal S-shaped curve. Sims and Estigarribia (2002) showed that once the treatment medium is fully saturated with CO2, the killing effect of DPCD did not change significantly with the enhancing effects of pressure or temperature. For example, 7.5 MPa was nearly as effective as 15 MPa, and room temperature was as effective as 31oC in reducing E.coli cells by using a membrane contactor system. This can be e xplained by the rapid increase of CO2 solubility in water with increasing pressure up to 7.5 MPa, but pressure increases above 7.5 MPa result in small increases in solubility (Dodds and others 1956). On the other hand, temperature has a more complex role in increasing microbial inactivation by DPCD. A lthough solubility of CO2 decreases with increasing temperatures, higher temperatures can increase the diffusivity of CO2 and the fluidity of

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30 cell membrane that facilitate penetration of CO2 into the cells. Another important effect of temperature is the phase change of CO2 from sub-critical to supercritical conditions (Tc = 31.1oC). The penetrating power of CO2 is higher under supercritical conditions, and there is a rapid change in solubility and density of CO2 by temperature at the near-critical region. Hong and Pyun (1999) observed that under a constant pressure of 6.8 MPa, microbial inactivation of L. plantarum increased by a log as te mperature decreased from 40oC (7 log reduction) to 30oC (8 log reduction). They explained this less effective inactivation at 40oC by the decrease in solubility of CO2 in this region. Initial pH of treatment medium is an im portant factor affec ting microbial reduction by DPCD. Low pH environment f acilitates penetration of car bonic acid, like many other carboxylic acids (Lindsay, 1976) through the cell membrane, therefore more inactivation is achieved as the medium pH decreas es. For example, Hong and Pyun (1999) demonstrated that under a CO2 pressure of 6.8 MPa at 30oC, treatmens of 25 min in acetate buffer (pH 4.5), 35 min in sterile dist illed water (pH 6.0) and 60 min in phosphate buffer (pH 7.0) were required to achieve 5 log reduction of L.plantarum cells. Cell growth phase or age is another factor affecting inactivati on of microbial cells by DPCD. Young cells are more sensitive th an mature ones. Hong and Pyun (1999) compared inactivation of L.plantarum cells in log phase with those in stationary phase, and found that cells in the late log phase were more sensitiv e to DPCD than those in the stationary phase. They attributed this to th e ability of bacteria entering the stationary phase of growth to synthesize new protei ns that protect cells against adverse environmental conditions (Koltter 1993, Mackey and others 1995).

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31 Different types of bacteria have different susceptibilities to DPCD treatment. It is hard to make comparisons since the treatment systems, solutions or conditions also differ in these studies. Referring to specific studies, it can be concluded that some microorganisms seem more affected by DPCD treatment. For example, Sims and Estigarribia (2002) showed that Lactobacillus plantarum cells were more resistant to DPCD than E.coli S. cerevisiae and Leuconostoc mesenteroides cells. Dillow and others (1999) treated G( +) bacteria ( S. aureus B. cereus, L. innocua ) and G(-) bacteria ( S. salford, P. vulgaris, L. dunnifii, P. aeruginosa and E.coli ) with DPCD. They found that B.cereus cells were more resistant to DPCD while E.coli and P.vulgaris were more sensitive. They suggested that the nature of the cell wall could be an important factor in the difference in sensitivity of these bacteria. Because of their thin cell walls, G(-) bacteria are expected to be more sensitive and their cell wall could be ruptured more easily than that of the G(+) bacteria. More studies need to be conducted in this area to have a clear conclusion. The type of system used for DPCD treatme nt can change the microbial inactivation rate by DPCD. Treatment systems that allow better contact of CO2 with the treatment solution are shown to be more effective in mi crobial reduction because of the more rapid saturation of the solution with CO2. Usually, batch systems require longer treatment times in order to be effective in microbial inactiv ation compared to continuous systems. On the other hand, it is possible to increase the in activation rate of ba tch systems by agitation (Lin and others 1993, Hong and Pyun 2001). Spilimbergo and others (2003) showed that a semi-continuous system is more efficient than a batch system. Treatment of 40 to 60 min was necessary for inactivation of a wide range of bacteria w ith the batch system,

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32 whereas less than 10 min was enough by using a semi-continuous system. Ishikawa and others (1995b) obtained more than 4 orders and 3 orders higher inactivation in L.brevis cells and S.cerevisiae respectively, by using a microbubbling filter in their system. Table 2-6. Summary of the studies on inactivation of vari ous microorganisms Solution Microorganism P (MPa) Time Temp. (oC) System Log redn. Reference aPS S.cerevisiae 20 2 h 35 Batch 7.5 (gC) Kamihira and others 1987 E.coli 20 2 h 35 6.5 (C) S.aureus 20 2 h 35 5 (C) A.niger 20 2 h 35 5 (C) Herbs Total bacteria count 5.52 2 h 45 Batch 5-8 (C) Haas and others 1989 Apple juice Total bacteria count 5.52 30 min 45 >3 (C) Orange juice Total bacteria count 5.52 30 min 55 4 (C) Nutrient broth E.coli 6.21 2 h Room temp. 2 S.aureus 6.21 2 h Room temp. 2 Salmonella seftenberg 6.21 2 h Room temp. 2 Distilled water L.monocytogenes 6.18 2 h 35 Batch 9 (C) Wei and others 1991 Egg yolk S.thyphimurium 13.7 2 h 35 >8 Orange juice Total plate count (TPC) 33 1 h 35 Batch 2 Arreola and others 1991b Growth medium S.cerevisiae 6.9 15min 35 Batch 7 (C) Lin and others 1992 Growth medium L.dextranicum 6.9 21min 15-20 min 35 Batch >8 Lin and others 1993 Sterile water S.cerevisiae 4 >3 h 40 Batch 8 (C) Nakamura and others 1994 PS L.brevis 25 30 min 35 Microbubble 6 (C) Ishikawa and others 1995b S.cerevisiae 25 30 min 35 6 (C) PS E.coli 5 20 min 35 Batch 6 (C) Ballestra and others 1996 Sterile Water S.cerevisiae 15 1 h 40 Batch 8 Kumagai and others1997 bMRS broth Lactic acid bacteria 6.9 200 min 30 Batch 5 Hong and others 1997 cBHIB S.aureus 8 60 min 25 Batch 7 (C) Erkmen 1997 Whole milk Aerobic plate count 14.6 5h 25 Batch >8

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33 Table 2-6 Continued Solution Microorganism P (MPa) Time Temp. (oC) System Log redn. Reference dTSB w/ polymers B.cereus 20.5 4 h 60 Batch 8 (C) Dillow and others 1999 L.innocua 20.5 0.6 h 34 9 (C) S.aureus 20.5 4 h 40 9 (C) S.salford 20.5 4 h 40 9 (C) P.auruginosa 20.5 4 h 40 8 (C) E.coli 20.5 0.5 h 34 8 (C) P.vulgaris 20.5 0.6 h 34 8 (C) L.dunnifi 20.5 1.5 h 40 4 (C) Growth medium L.plantarum 13.8 30 min 30 Batch >6 (C) Hong and others 1999 ePS with broth L.monocytogenes 6 75 min 35 Batch 6.98 (C) Erkmen 2000a PS E.faecalis 6.05 18 min 35 Batch 8 (C ) Erkmen 2000b Fruit juicemilk E.faecalis 6.05 3-6 h 45 5 (C ) PS Brocothirix thermosphacta 6.05 100 min 35 Batch 5.5 (C) Erkmen 2000c Skinned meat Brocothirix thermosphacta 6.05 150 min 35 Batch 5 (C) MRS broth L.plantarum 7 100 min 30 Batch >8 Hong and Pyun 2001 PS S.thyphimurium 6 15 min 35 Batch 7 (C) Erkmen and Karaman 2001 PS w/broth S.thyphimurium 6 140 min 25 Batch 7 (C) Whole milk E.coli 10 6 h 30 Batch 6.42 (C) Erkmen 2001 Skim milk E.coli 10 6 h 30 Batch 7.24 (C) PS B.subtilis 7.4 2.5 min 38 SCh 7 (C) Spilimbergo and others 2002 P.aeruginosa 7.4 2.5 min 38 7 (C) Sterile water E.coli 7.5 5.2 min 24 CMi 8.7 Sims and Estigarribia 2002 Orange Juice E.coli 15 4.9 min 24 >6 Orange juice L.mesenteroids 15 <10 min 25 >6 Orange juice S.cerevisiae 15 <10 min 25 12 Orange juice L.plantarum 7.5 <10 min 35 >8 Orange juice S.thyphimurium 38 10 min 25 CF 6 Kincal and others 2005 Orange juice L.monocytogenes 38 10 min 25 6 Orange juice E.coli O157:H7 107 10 min 25 5 Apple juice E.coli O157:H7 20.6 12 min 25 5.7

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34 Table 2-6 Continued Solution Microorganism P (MPa) Time Temp. (oC) System Log redn. Reference Carrot juice Aerobic plate count 4.9 10 min 5 Batch 4 Park,and others 2002 fWM juice Aerobic plate count 34.4 5 min 40 CF 6.5 Lecky 2005 Mandarin juice Aerobic plate count 41.1 9 min 35 CF 3.47 Lim and others 2006 Coconut water Aerobic plate count 34.5 6 min 25 CF >5 Damar and Balaban 2005 aPS: Physiological Saline, bMRS: De Man Rogosa Sharpe, cBHIB: Brain-Heart Infusion Broth, dTSB: Tryptic Soy Broth eCF: Continuous flow, fWM: Watermelon, gC: Complete inactivation, hSC: Semi-continuous, iCM: Continuous membrane Inactivation of Spores by DPCD Spores are highly resistant forms of bacteria to the physical treatments such as heat, drying, radiation and chemical agents (Watan abe and others 2003a). A limited number of studies in the literature inves tigating inactivation of spores by DPCD show that the extent of inactivation achieved changes with trea tment conditions, treatment systems and the type of organism (Table 2-7). Studies suggested that processing temperatur e had a significant role in inactivation of spores by DPCD. Several re searchers observed that a temp erature threshold should be exceeded in order to achieve a killing effect on bacterial or fungal spores (Enomoto and others 1997, Ballestra and Cuq 1998, Watana be and others 2003b). This threshold temperature can be different for different s pores. Kamihira and ot hers (1987) did not observe any killing effect of DPCD on B. stearothermophilus spores and observed only 53% inactivation of B.subtilis spores by DPCD treatment at a relatively low temperature (35oC). Enomoto and others (1997) showed that there was not a signif icant inactivation of B.megaterium spores at temperatures below 50oC, and survival ratio of spores decreased dramatically by increasing temperature from 50 to 60oC. On the other hand, Ballestra and Cuq (1998) did not observe antimicrobi al activity of DPCD treatment on B.subtilis spores

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35 and Byssochlamys fulva ascospores below 80oC, and on A.niger conidia below 50oC. Similarly, Watanabe and others (2003b) observed that DPCD treatments at temperatures in the range of 35oC to 85oC did not have a killing effect on Geobacillus stearothermophilus spores. However, it may be possible to achieve significant amounts of spore inactivation at relatively low temp eratures by using con tinuous DPCD treatment systems that are shown to be more efficient than batch systems. For instance, Ishikawa and others (1997) achie ved 6 log reduction in B. polymyxa B.cereus and B. subtilis spores at 45oC, 50oC and 55oC, respectively, by using a c ontinuous micro-bubble system. Micro-bubbling by the use of a filter improved th e inactivation of spores by 3 log cycles. There was only 1 log reduction of spores without micro-bubbli ng and 4 log reduction with micro-bubbling under the same treatment conditions. The mechanism of inactivation of spores by DPCD is not known. Watanabe and others (2003a) compared the killing effect of DPCD with heat and high hydrostatic pressure (HHP) treatments. DPCD had more lethality than HHP treatment or heat treatment alone, showing that CO2 had a unique role in inacti vation. They suggested that inactivation mechanisms of bacterial spores by DPCD and heat were different, since inactivation of Bacillus spores by heat treat ment occurred in a single step whereas inactivation by DPCD occurred in two steps. Ballestra and Cuq (1998) also observed two steps in the inactivation of B.subtilis spores at 5 MPa CO2 and 80oC. They suggested that the first step of inactivation c ould represent penetration of CO2 into the cells that is associated with heat activation of the dorma nt spores. Heat activation can make spores more sensitive to the antimicrobial effects of CO2. However, there may be another explanation for spore inactivation by DPCD ba sed on the study of Furukawa and others

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36 (2004). This study believes that DPCD is able to germinate bacterial spores even at relatively low treatment temper atures. Approximately, 40% of B. coagulans and 70% of B. licheniformis spores were germinated by DPCD at 6.5 MPa and 35oC. Therefore, DPCD could be the reason for germination of spores making the resu lting vegetative cells more sensitive to heat inactivation. The study of Watanabe and others (2003a) shows that inactivation of B. coagulans and B. licheniformis spores by heat treat ment only is much lower than inactivation obtained when a co mbination of DPCD and the same heat treatment is applied. In the combined treatment, DPCD is applied first and heat is applied afterwards. Their study suggests that DPCD ma y decrease heat tolerance of bacterial spores. The calculated z values with and w ithout DPCD were the same. However, the D values with DPCD were much smaller, indicat ing an upward shift in the log inactivation vs. time curve with DPCD. The role of DPCD and heat treatments in spore inactivation needs to be investigat ed more explicitly. Table 2-7. Summary of studies on spore inactivation by DPCD Solution Microorganism Pressure (MPa) Time Temp (oC) System Log redn. Reference Sterile water B.subtilis 20 2 h 35 Batch 0.3 Kamihira and others 1987 Growth medium P.roqueforti 5.52 4 h 45 Batch >6 Haas and others 1989 Sterile distilled water B. megaterium 5.8 30 h 60 Batch 7 Enomoto and others 1997 B. polymyxa 30 60min 45 Microbubble 6 Ishikawa and others 1997 B.cereus 30 60min 50 6 aPS B.subtilis 30 60min 55 6 B.subtilis 5 1 h 80 Batch 3.5 Ballestra and Cuq 1998 B. fulva ascospores 5 1 h 80 Batch 0.7 Sterile Ringer solution Sterile water B.stearothermophilus 20 2 h 35 Batch 0

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37 Table 2-7 Continued Solution Microorganism Pressure (MPa) Time Temp (oC) System Log redn. Reference S.cerevisiae ascospores 15 <10min 45 Continuous Membrane filter >6 Sims and Estigarribia 2002 Alicyclobacillus acidoterretis spores 7.5 <10min 45 >6 Orange juice G.stearothermophilus 30 2 h 95 Batch 5 Watanabe and others 2003b aPS: Physiological saline Inactivation of Enzymes by DPCD Inactivation of certain enzymes that aff ect the quality of foods by DPCD has been shown by several researchers (Balaban and others 1991a&b, Chen and others 1992&1993, Tedjo and others 2000, Park and othe rs 2002). A summary of the literature including the enzymes treated with DPCD, th e amount of activity loss achieved in these treatments and DPCD treatment conditions is given in Table 2-8. DPCD can inactivate certain enzymes at temperatures where therma l inactivation is not effective (Balaban and others 1991a). Among these enzymes, pectineste rase (PE) causes cloud loss in some fruit juices; polyphenol oxidase (PPO) causes unde sirable browning in fruits, vegetables, juices and some seafood; lipoxygenase (LOX) causes chlorophyll destruction and offflavor development in frozen vegetables; and peroxidase (POD) has an important role in discoloration of foods and is used as an inde x for efficacy of heat treatment in processing fruits and vegetables. The PE, PPO, POD and LOX from various foods were shown to be effectively inactivated by DPCD. Although the number of studies on enzyme inactivation by DPCD is limited, the studies conducted so far point to the potential of DPCD technology in especially fruit and vegetable juice processing, where these enzymes cause quality deterioration if not inactivated.

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38 Studies suggest that enzyme inactivation by DPCD could be due to several causes such as pH lowering, conformational changes of the enzyme, and i nhibitory effect of molecular CO2 on the enzyme. Balaban and others (1991a) studied the inactivation of PE in orange juice by DPCD. The pH of orange juice must be lo wered to 2.4 for substantial PE inactivation. However, by DPCD treatment pH of orange juice was lowered only to 3.1. Therefore, pH-lowering alone was not sufficient to explain enzyme inactivation by DPCD. The results of Chen and others (1992) support th eir approach. They used a pH control and measured the activity of lobster PPO that wa s kept under pH of 5.3, which is the same as the pH of samples achieved by DPCD tr eatment. Although the pH control sample retained 35% of its original activity at 35oC after 30 min, DPCD treated enzyme lost its activity after 1 min at the same temperature. CO2 was suggested to have a unique role in the inactivation of enzymes. Balaban and others (1993) applied the following trea tments to orange juice and observed the decrease of PE activity. Untreated control ha d a decrease in PE ac tivity of 8% after 20 days storage. Supercritical CO2 treatment (31 MPa, 40oC, 45 min) showed 31% reduction; juice acidified with HCl to pH=3.1 and pressurized with N2 (24 MPa, 40oC, 45 min) had a 36% reduction; juice buffered to pH =3.8 with citrate buffer, then treated with supercritical CO2 (31 MPa, 40oC, 45 min) reduced PE by 23% ; juice pressurized with N2 (20.6 MPa, 55oC, 1 h) showed an increase in PE ac tivity. These results suggest that the buffered juice PE activity decreased only by the molecular effect of CO2, while the unbuffered CO2 combined the effects of pH lowering and CO2 effects. Pressurized N2 did

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39 not lower activity. Similarly, Chen a nd others (1993) have shown that N2 treatment under the same conditions as CO2 treatment did not cause any inactivation of PPO. Table 2-8. Summary of studies on inactivation of enzymes by DPCD Enzyme Source of enzyme Pressure (MPa) Time Temp (oC) System Loss of activity (%) Reference Lipase Commercial (62-68% water) 20 2 h 35 Batch 12-22 1 -amylase Commercial (62-68% water) 20 2 h 35 0 Glucoamylase Commercial (5-7% water) 20 1 h 35 Batch 0 2 Catalase Commercial (5-7% water) 20 1 h 35 10 Lipase Commercial (5-7% water) 20 1 h 35 0 Glucose isomerase Commercial (5-7% water) 20 1 h 35 0 PEa Orange juice 26.9 145 min 56 Batch 100 3 PPOb Spiny lobster 5.8 1 min 43 Batch 98 4 PPO Brown shrimp 5.8 1 min 43 78 PPO Potato 5.8 30 min 43 91 PPO Spiny lobster 0.1 30 min 33 Batch 98.5 5 LOXc Soybean 10.3 15 min 50 Batch 100 6 PODd Horseradish 62.1 15 min 55 100 LOX Soybean 62.1 15 min 35 95 PPO Carrot juice 4.9 10 min 5 Batch 61 7 LOX Carrot juice 2.94 10 min 5 >70 PPO Muscadine grape juice 27.6 6.25 min 30 Continuous flow 75 8 aPE: Pectinesterase, bPPO: Polyphenol oxidase, cLOX: Lipoxygenase, dPOD: Peroxidase 1Kamihira and others 1987, 2 Taniguchi and others 1987,3 Balaban and others 1991b, 4Chen and others 1992, 5 Chen and others 1993, 6 Tedjo and others 2000, 7 Park and others 2002, 8Del Pozo-Insfran and others 2006 DPCD can change isoelectric profiles and protein pattern s of PPO (Chen and others 1992). However, these changes were not caused by CO2 under atmospheric pressure (Chen and others 1993). Chen and others ( 1992) obtained Circular Dichroism spectra of untreated and treated lobster, brown shrimp and potato PPOs. Their results showed that DPCD caused conformational changes in the secondary structures ( -helix, -sheet, turn and random coil) of the enzymes. High pressure is also reported to cause

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40 conformational changes in pr otein and enzyme molecules (Suzuki and Taniguchi 1972). On the other hand, Hendrickx and others ( 1998) reported that pressures around 310 MPa can cause irreversible damage to the secondary structure of proteins, but pressures below it cause no change or changes that are reve rsible upon depressurization. DPCD pressures are very much lower than 310 MPa, therefor e, the conformational changes occurring by DPCD may not be caused by a high pressure effect. This needs to be confirmed by further research. Extent of enzyme inactivation by DPCD is affected by the type and source of the enzyme, DPCD treatment conditions such as pressure, temperature and time, and treatment medium properties. Balaban and others (1991a) observed that higher temperatures and pressures of DPCD treatme nt results in higher %PE inactivation. An enzyme isolated from different sources has diffe rent resistance to DPCD treatment, as is the case with heat inactivation. For example, potato PPO is more resistant to inactivation by DPCD compared to spiny lobster and shrimp PPOs (Chen and others 1992). The presence of other soluble compounds in th e treatment medium may have a protective effect against DPCD treatment. Tedjo and others (2000) showed that %LOX and %POD activity increased by increasing sucrose c oncentration up to 40%. This could be explained by decrease in the solubility of CO2 as sucrose concentration increases. DPCD treatment is reported to be more e ffective than heat treatment in enzyme inactivation and can inactivate enzymes at much lower pressures compared to High Hydrostatic Pressure, an a lternative non-thermal proces sing method. Significant amounts of inactivation of PE, PPO, LOX and POD ar e possible by DPCD at temperatures lower than 55oC. Park and others (2002) achieved si gnificant inactivation of LOX and PPO in

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41 carrot juice at a temperature as low as 5oC. Heating at 55oC for 30 min results in only about 18% and 13% inactivation of LOX and POD, respectively, while DPCD results in total inactivation of these enzymes after 15 min treatment at the same temperature. DPCD Treatment Systems Several batch, semi-continuous and con tinuous treatment systems have been developed since the first DPCD a pplications. In a batch system, CO2 and treatment solution are stationary in a container for a certain period of time during treatment. A semi-continuous system allows a continuous flow of CO2 through the treatment chamber, while a continuous system allows continuous flow of both CO2 and the treatment solution through the system. Most of earlier studies have been perform ed using batch systems. A typical batch system consists of a CO2 gas cylinder, a pressure regulat or, a pressure vessel, a water bath or heater, a CO2 release valve, and a data logge r (Figure 2-6) (Hong and Pyun 1999). At the beginning of the operation, the sample solution is placed into the pressure vessel and temperature is set to the desired value. Next, CO2 is introduced into the vessel until the sample in the vessel is saturated at the desired pressure and temperature. The sample solution is left in the vessel for a certain amount of time and then the CO2 outlet valve is opened to release the gas. Some systems cont ain an agitator that decreases the time to saturate the sample solution with CO2.

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42 Figure 2-6. A typical batch DPCD system In 1995, Ishikawa and others (1995a) de veloped a semi-continuous micro-bubbling system that uses a cylindri cal filter to micro-bubble CO2 entering into the pressure vessel. They showed that the use of the filter significantly increased the efficiency of the system. They could achieve three times more inac tivation of enzymes us ing a micropore filter than without it. They also showed that us ing a filter increased the concentration of dissolved CO2 in the sample from 0.4 to 0.92 mol/L at 25 MPa and 35oC. In 1998, Shimoda and others developed a continuous mi cro-bubble system that was very effective in the inactivation of microorganisms (F igure 2-7). In this system, liquid CO2 and a saline solution were pumped through a CO2 dissolving vessel at cert ain flow rates. Liquid CO2 was changed to gaseous state using an eva porator and then dispersed into the saline solution from a stainless steel mesh filter with 10 m pore size. The micro-bubbles of CO2 moved upwards while dissolving CO2 into the saline solution. Then, the saline solution saturated with CO2 was passed through a heater to reach the desired temperature

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43 and a suspension of microorganisms was pumped into it at this point. Another coil with a heater was used to adjust the residence time (Shimoda and others 2001). Figure 2-7. A continuous micro-bubble DPCD system A continuous membrane contact CO2 system was developed by Sims in 2001 (Figure 2-8) (Sims and Estigarribia 2002). This system consists of four in series hollow polypropylene membrane modules. Each tubular module has 15 parallel fibers of 1.8 mm ID, 39 cm length and 83 cm2 active surface area. A CO2 pump is used to pressurize the system, and the test liquid is pumped conti nuously into the system with a HPLC pump. This setup is very efficient in saturating the liquid with CO2 since it provides a large contact area between CO2 and the test liquid by the use of the membranes. In the membrane contactor, CO2 is not mixed with the test liqui d but instead diffuses into it at saturation levels instantaneously. CO2 is recycled back and re-used.

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44 Figure 2-8. A continuous CO2 membrane contactor system In 1999, Praxair (Chicago, IL) developed a continuous flow DPCD system (Figure 2-9). This system consists of CO2 tanks and a CO2 pump, a product tank and product pump, a high pressure pump, holding coils, decompression valve and a vacuum tank. CO2 and the product are pumped through the syst em and mixed before passing through the high pressure pump. This pump increases the pr essure to the processing levels, and the product temperature is brought to the desired level in hol ding coils. Residence time is adjusted by setting the flow rate of the product passing thr ough holding coils. At the end of the process, an expansion valve is used to release CO2 from the mixture. It is possible to pull out the remaining CO2 in the product by a vacuum tank. This system has been shown to be very effective in killing pathogens and spoilage bacteria for short periods of time (Folkes 2004, Damar and Balaban 2005, Kincal and others 2005, Lecky 2005, Lim and others 2006).

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45 P Main Pump Juice stream Vacuum Heating system Hold tube Treated CO2juiceCO tank2 Expansion valve Pump Pump Chiller 4 1 2 3 5 6 7 8 9 Figure 2-9. A continuous flow DPCD system DPCD Food Applications and Quality Effects DPCD has been applied mostly to liquid f ood products, particularly to fruit juices. To date, there is no commercial food produc t processed by DPCD. There are a limited number of published studies in the literature regarding the e ffect of DPCD on the quality of foods including a few test results p ublished by companies offering commercial systems. Among the first food applicati ons of DPCD is treatment of whole fruits such as strawberry, honeydew melon, and cucumber fo r inhibition of mold growth. Haas and others (1989) demonstrated th at although mold inhibition is possible by DPCD treatment of fruits, DPCD may cause seve re tissue damage in some fr uits even at low pressures. Studies with orange juice shows that DPCD treatment can improve some physical and nutritional quality attributes such as cl oud formation and stability, color and ascorbic acid retention. Arreola and others (1991a) treated fresh or ange juice with DPCD in a batch system from 7 to 34 MPa, 35 to 60oC and for 15 to 180 min time periods. They also

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46 had temperature controls that were kept under the same temperatures for the same amount of time without DPCD treatment. As corbic acid retention of DPCD treated orange juice was between 71 to 98%. Ascorbic acid retention levels of DPCD treated samples were significantly higher than that of temperature controls. Higher ascorbic acid retention by DPCD was explai ned by the exclusion of O2 from the system and lower pH of orange juice by DPCD. Ascorbic acid has higher stability unde r low pH and oxidizes easily when oxygen is present in the environm ent. On the other hand, cloud of orange juice was enhanced by 1.3 to 4.0 times after DPCD treatment compared to original untreated orange juice. Cloud stability of orange juice treated by DPCD at 29 MPa and 50oC for 4 h was retained after 66 days of re frigerated storage. However, temperature controls (50oC for 4 h) and room temperature controls (25oC for 4 h) lost cloud completely during refrigerated storage. In th e same study, instrumentally measured color scores showed that DPCD treated juice wa s brighter than untreated juice. Sensory evaluation of DPCD treated and untreated juic es indicated that th ere was no significant difference in flavor, aroma and overall accep tability of these samples. The color and cloudiness of DPCD treated juice were pr eferred over those of untreated juice. Park and others (2002) treated carrot ju ice with a combined effect of 4.9 MPa DPCD and 600 MPa ultra-high pressure. They observed reduction of pectin methylesterase (PME) activity by 65%, and a cloud loss of 47%. This suggests that the cloud loss in different food systems, even w ith the same enzyme (PME), could follow different mechanisms, and cloud retention in e. g. orange juice does not necessarily imply cloud retention in carrot juice.

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47 Later studies using continuous systems al so show nutritional and sensory quality retention and improvements in th e physical attributes of ora nge juice treated with DPCD. Kincal and others (2006) obtai ned up to 846% cloud increase in orange juice by DPCD treatment (38 MPa, room temperature, 10 min) There were no signifi cant changes in pH and oBrix of treated samples. Small, but statistically insignificant increase in L* and a* values of color occurred by DPCD. Sensory evaluations of DPCD treated and untreated orange juice were not significan tly different. Ho (2003) used the continuous flow system of Praxair (Chicago, IL) and reported that th ere were no significant differences between physical attributes (pH, oBrix and titratable acidity), nut ritional content (vitamin C and folic acid) and aroma profile for untreat ed and DPCD treated orange juice. Folkes (2004) used continuous DPCD tec hnology for pasteurization of beer and compared physical and sensory quality attributes of DPCD treated beer with that of fresh (untreated) and heat pasteurized beer. The ar oma and flavor of DP CD treated beer was not significantly different from fresh beer even afte r 1 month storage at 1.67oC, but heat treated beer was found significantl y different than others in ta ste and aroma at the end of storage ( =0.1). DPCD treated beer had significantly less foam capacity and stability compared to heat pasteurized beer, but not at levels detrimental to the finished product quality. On the other hand, beer haze was significantly reduced by DPCD. Lim and others (2006) treated mandarin juice with DPCD using the continuous flow system by Praxair and measured the pH, oBrix, titratable acidity, cloud and color after DPCD treatment at 13.8-41.4 MPa, 25-45oC and 7-9 min. DPCD treatment enhanced the cloud up to 38.4%, increased lightness and yellowness, and decreased

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48 redness of mandarin juice. DPCD treated sa mples had higher titratable acidity than untreated samples. The pH and oBrix did not change after DPCD treatment ( =0.05). It is important to conduct studies rega rding the consumer likeability of food products that are processed by DPCD si nce the consumer is the target in commercialization of this technology. Objectives of the Study The objectives of this study were: i. To quantify microbial reduc tion in coconut water as a function of treatment conditions such as pressure, temperature, time and CO2 level ii. To evaluate quality of DPCD treated coconut water during storage iii. To compare untreated fresh, DPCDand heat-treated coconut water by sensory evaluation iv. To identify flavor compounds in coconut water and compare the flavor profile of untreated, DPCDand heat -treated coconut water

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49 CHAPTER 3 MATERIALS AND METHODS Preliminary Experiments with Coconuts Juice Extraction and Initial Quality Tests Eight immature green coconuts (Malaysian Dwarf) were obtained from Homestead, FL. A in Makita Drill (Buford, GA) was us ed to drill two holes on opposite sides of coconuts and the water was poured into 1L gl ass bottles. Each bottle was numbered from 1 to 8 and stored in a refrigerator (4oC). Weight of coconuts ranged between 1.85 to 2.40 kg and coconut water extracted from these ranged between 435 to 490 g. oBrix was between 6.3 and 6.6, while pH ranged between 5.35 and 5.50. Total aerobic plate counts (APC) were between zero count and >190 cfu/mL, and there was no yeast and mold (YM) growth initially (Table A-1). APC a nd YM counts were repeated at day 9 for selected bottles and increase in counts we re observed (Table A-2). Presence of PPO and POX enzyme activity in coconut water was confirmed by following the method described by Campos and others (1996). Pinking of Coconut Water Coconut water from the eight coconuts in each bottle changed color during refrigerated storage. The pict ures of the coconut water in each bottle at day 0 and day 9 were given in Figure A-1. Some of the bottles showed brow ning of coconut water at day zero. This could be due to enzymatic br owning that was accelerated by introducing phenolic compounds from the out er surface of the green shel l, as well as heating and metal contact during drilling of the coconuts.

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50 Preliminary tests were conducted to understand the mechanisms causing or accelerating pinking in coconut water. In Test 1, coconut water extracted from one immature green coconut was divided into two. One part was placed into 20 mL gla ss test tubes and divide d into five treatment groups (three tubes/group). Trea tments were control (no treatment)(1), frozen and thawed at 4oC the next day(2), N2 bubbling for 15 min (3), heating at 80oC for 5 min (4) and heating at 80oC for 5 min while exposed to the ai r (5). All tubes were stored under refrigeration (4oC). Tubes were observed for color at days 0, 4, 7, 9 and 12. On day 7, one of the three tubes from control, frozen/th awed and heating (closed caps) groups were removed from that group and bubbled with air for 15 min. Color observation results are given in Table A-3. Two out of three N2 bubbled tubes and all open heated tubes turned pink earlier than others on day 4. On day 12, one tube of N2 bubbled and two unaerated controls, frozen/thawed and heated (closed cap) tubes were still colorless whereas all aerated tubes, open heated tubes and two N2 bubbled tubes were pink. Although it is hard to draw a clear conclusion on the effect of heating or N2 bubbling based on this test, aeration seems to accelerate pinking. In Test 2, the second part of the coco nut water was placed into 50 mL opaque plastic cups and were exposed to different treatments such as ascorbic acid (100 ppm)(1), potassium metabisulfite (40 ppm)(2) or 0.1N HCl addition to lower the pH to 4.0(3) and 3.0(4). Two cups were untreated and used as control (pH=4.8). The color observations were done every day until day 12 and also 3 months later (Table A-4). Control cups turned pink on day 12 whereas others were stil l colorless. At the end of 3 months, all

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51 cups other than ascorbic acid added and potas sium metabisulfite added cups turned pink. Ascorbic acid and potassium metabisulfite seem to stabilize the colo r of coconut water. Test 3 was conducted to observe the effect of aeration and heating on pinking of coconut water. Coconut water extracted from a coconut was divide d into 20 mL glass tubes, three tubes in each treatment. Tr eatments were control (1), heated at 85oC for 5 min (2), boiled for 5 min (3), and air bubbled for 15 min (4). Color observations on day 6 and day 10 showed that all heated and aerat ed tubes eventually turned pink, whereas control tubes were still clear at the end of 10 days refrigerat ed storage (Table A-5). These results suggested that aeration and heating might accelerate pinking. It is unlikely that pinking is due to microorganisms since boiling did not prevent it. Tests with Commercial Coconut Water Drinks In order to understand some properties of commercially available coconut water, six different brands of coc onut water drinks were obtaine d from the market and their sensory evaluation was made by an info rmal tasting. The measured pH and oBrix values and the contents of these products are given in Table A-6. Four of these products were in aluminum cans while two others were in Tetr apak boxes. The pH of these drinks changed between 4.12 and 5.16 while the oBrix was in the range of 5.6 to 10.8. Cooked, metallic, soapy and artificial coconut fl avors were recognized by some of the panelists. Products with lower oBrix were mostly found to have a bl and or no taste whereas the ones with higher oBrix were usually found to be too sweet. Extraction of Coconut Water from Coconuts About 1,140 immature green coconuts ( Cocos nucifera Malaysian Dwarf) were obtained from growers (El Salvador Farm) in Homestead, Florida. Coconuts were left in a commercially available bleach solution (1.0 % (v/v)) and rinsed with water before

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52 cutting. Washed coconuts were passed thr ough a band saw and cut horizontally in one end. The liquid inside was taken to a clear glass bottle by the use of a peristaltic pump, and checked for color, smell and taste. A ny turbid, or abnormally colored liquid was discarded. Clear liquid was placed in 3 gallon plastic pail containe rs that were kept in ice. Once each pail was full, the juice was frozen at –20oC immediately in order to prevent any microbial or enzymatic activity. This pr ocedure was used to mix juices from many coconuts and make the sample homogeneous as much as possible. Although all the coconut water could not be mixed into one batch, the liquid was a representative of a broad number of coconuts. During experiment s whenever needed, the pails were taken randomly into 4oC cold room and thawed. Pictures in Figure A-2 show steps used in extraction of coconut water. Formulation of Coconut Water Beverage Preliminary tests were performed to de termine the necessity for acidification, sweetening and carbonation of coconut wate r. Safety considerations against C. botulinum required acidification. Food grade citric acid (P resque Isle, North East, PA), malic acid (Presque Isle, North East, PA) and pH ase (Jones-Hamilton, Walbridge, OH) were compared by preliminary tastings for their su itability to sweeten coconut water. Malic acid was chosen as the most suitable acid and added to coconut water to lower the pH to 4.30. Malic acid is naturally pres ent in coconut water and was preferred over citric acid and pHase by the panelists. Prel iminary tasting showed that a sweetener was needed to compensate for the sourness caused by aci dification. Splenda (McNeil-PPC, Fort Washington, PA), which is basically a chemica lly modified form of sucrose, was used as the sweetener and the amount was determin ed by informal tastings. Splenda has no caloric value and was preferre d over other artificial sweetne rs because it gives relatively

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53 higher sweetness (600 times that of sucr ose) and lack of strong aftertaste. oBrix of coconut water did not change after Splenda addition. Finally, car bonated coconut water was compared to non-carbonated for likeability by informal tastings. Carbonation was done at 4oC and 1.82 atm CO2 pressure. It was decided to carbonate coconut water after acidification and sweetening because car bonated samples were preferred over noncarbonated by panelists. DPCD Processing Equipment Continuous-flow DPCD System A continuous high pressure CO2 machine of 55.16 MPa pressure and about 0.8 liters/min flow rate capacity (Praxair Co., Chicago, IL) was used for pasteurization of coconut juice. The components of the system and their functions were described in section “DPCD treatment systems” of Chapter 2. The system was run at a juice flow rate of 417 g/min in order to obtain 6 min residence time in the holding tube (79.2 m le ngth and 0.635 cm ID). Sterile water was run through the system until the desired leve ls of pressure, temperature and CO2 level were reached. Coconut water was then poured in th e juice tank and the first 3.5 L of coconut water were discarded. Approximately 1 L of treated coconut water was collected into a sterile 1 L glass bottle at the exit valve. Processed coconut water was cooled down immediately at 4oC until further use. Whenever the tr eatment parameters were changed, sterile water was run through the system un til the desired levels were reached. The equipment was cleaned after each use as described below. Cleaning of the Equipment Oxonia and Principal solutions (Ecolab, St. Paul, MN) were the chemicals used to sanitize the equipment. Concentrations of so lutions were determined as 0.38% Principal

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54 and 0.44% Oxonia solutions (v/v ) with the help of an Ec olab representative. The equipment was first cleaned with 26.5 L of Principal solution and then with 22.7 L of Oxonia solution the day before the experiment. On the day of the experiment, 24 L of sterile distilled water was passed through the eq uipment. At the end of the experimental run, the same sanitization pr ocedure was followed. Previ ous cleanability studies on DPCD equipment shows that a concentra tion of 0.5% Principal solution and 0.28% Oxonia solution were sufficien t to confirm that the equipment was sanitized (Lecky 2005). Heat Pasteurization Equipment Heat pasteurization equipment consisted of a water bath (Precision Scientific Group, Chicago, IL) that was set to the pasteurization temperature (74oC), two 5.4 m stainless steel tubing (0.476 cm ID) and a peristaltic pump (Figure 3.1). Coconut water was pumped by the peristaltic pump at a fl ow rate of 385 mL/min through the first stainless steel tubing (placed in the wate r bath) in order to be heated to 74oC and then passed through a second stai nless steel tubing at 74oC for 15 s. D value of L.monocytogenes at 74oC is 0.72 s and its z value is 5.56oC (Freier 2001). Treatment for 15 s gives 20 log cycles reduction in this microorganism. Coconut water exiting the second tubing was immediately cooled to approximately 10oC by passing through 3.2 m of stainless steel tubing (0.476 cm ID) that was placed in ice slush. Heat treated coconut water was collected in sterile gl ass containers (6 L) and pla ced in the cold room and at 4oC.

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55 juice pump Water bath Ice slush Pasteurized juice 1 2 3 4 Figure 3.1. Schematic drawing of heat pasteurization equipment Carbonation Equipment Untreated, DPCD and heat pasteurized coconut water samples were carbonated by using a Zahm & Nagel Pilot Plant Carbonato r (Zahm & Nagel Co., Buffalo, NY) with a capacity of around 7.5 liters. Carbonator was cl eaned by soap, distilled water and alcohol before each use. Coconut water at 4oC was placed in the carbonator unit that was kept in ice throughout carbonation in or der to keep the temperatur e of the juice at about 4oC. CO2 gas was sent from the gas tank (BOC Group, NJ ) to the carbonator and the air remaining in the carbonator was replaced by CO2 gas. Next, the CO2 pressure was brought to 1.82 atm and CO2 was bubbled through the juice until a ll juice inside the carbonator was collected. Carbonated coconut water was immedi ately filled into glass champagne bottles of 750 mL capacity each and capped with meta l caps. All carbonated water bottles were stored at 4oC.

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56 Optimization of DPCD Treatment Co nditions for Microbial Reduction Aging of Coconut Water Aging of coconut water was necessary to br ing the initial microbial load of coconut water to 107 colony forming units (cfu/mL). Frozen co conut water kept in plastic pails at –20oC, was thawed for 1 week at 4oC and then formulated by the addition of malic acid to lower the pH to 4.3 and 0.7% (w/w) Splenda with a final oBrix of 6.0. Then the coconut water was aged at room temperature (24oC) for about 46 h in order to increase microbial load to >107 cfu/mL. Experimental Design Response surface methodology (RSM) was used for the design and optimization of DPCD treatment conditions for microbial reduction. DPCD process variables were pressure, temperature, CO2 to juice ratio (w/w) and residence time. Experimental conditions were determined by a 3-factor, 3-level Box-Behnken design, which is one of the Response surface designs. Residence time wa s decided to be 6 min, and kept constant throughout the treatments since long times would not be economically feasible. Independent variables were pressure (13.8, 24.1, 34.5 MPa), temperature (20, 30, 40oC) and CO2 to juice ratio (7, 10, 13 g CO2/ 100 g juice). The maximum pressure level was chosen as 34.5 MPa because this pressure can be achieved safely considering the limitations of the system, where 55.16 MPa is the maximum. The minimum pressure level was chosen as 13.8 MPa since below that pressure le vel a significant microbial reduction was not expected based on prev ious studies. Minimum temperature was determined by the limitations of the equipment and had to be chosen as the room temperature at the time of the experi ment. Middle temperature value was 30oC that was a close to the critical temperature for CO2 (31oC). Maximum temperature (40oC) was in

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57 supercritical range, and higher temp eratures than this could affect the quality of the juice. Dependent variable was log reduction in aerob ic microbial load (cfu/mL) of juice after treatment. Microbial log reduction was cal culated for each experimental run as; log[(initial number of cfu /mL)([number of cfu/mL after treatment)]. Fifteen experimental runs were determined by applying Box-Behnken coded design. The codes and conditions for each variable are shown in Table 3-1. The following equations give the relation between the c odes (X1, X2, X3) and the variables (T, P and % CO2 level): X1= 0.10 T(oC) – 3.0 X2=0.097 P(MPa) – 2.33 X3=0.333 % CO2(g CO2/ 100 g juice) – 3.33 Table 3-1. Three factor-3 level Box-Behnke n experimental run coded variables and conditions RUN# Coded T : X1 Coded P : X2 Coded CO2/juice ratio: X3 Temperature (oC) Pressure (MPa) % CO2 Level (w/w) 1 -1 -1 0 20 13.8 10 2 1 -1 0 40 13.8 10 3 -1 1 0 20 34.5 10 4 1 1 0 40 34.5 10 5 -1 0 -1 20 24.1 7 6 1 0 -1 40 24.1 7 7 -1 0 1 20 24.1 13 8 1 0 1 40 24.1 13 9 0 -1 -1 30 13.8 7 10 0 1 -1 30 34.5 7 11 0 -1 1 30 13.8 13 12 0 1 1 30 34.5 13 13 0 0 0 30 24.1 10 14 0 0 0 30 24.1 10 15 0 0 0 30 24.1 10 X1: Code for Temperature, X2: Code for Pressure, X3: Code for % CO2 level

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58 Storage Study A storage study was conducted for 9 weeks and samples were taken at weeks 0, 2, 3, 5 and 9 in order to evaluate microbial physical (pH, color, titratable acidity, oBrix) and sensory attributes of untreated (fresh control), DPCD and heat pasteurized coconut water beverage samples. Flavor profiles of stored samples were also analyzed instrumentally. Storage study was ended at the 9th week since the microbial load for untreated coconut water exceeded 105cfu/mL and the flavor was undesirable. At the beginning of the storage study, frozen coconut water was thawed at 4oC and formulated by malic acid and Splenda additi on. DPCD treated samples were treated at previously determined optimum conditions (25oC, 34.5 MPa, 13% CO2), and heat treated samples were pasteurized at 74oC for 15 s. All samples were then carbonated and capped in 750 mL champagne bottles, and stored at 4oC until further needed. These steps are shown in a schematic drawing (Figure 3-2). Untreated control samples were prepared fresh as described above for each week of sensory panels, whereas the DPCD and heat pasteurized samples were stored samples. DPCD and heat pasteurized samples were analyzed for microbial load prior to the taste pa nels in order to ensure the safety of these samples.

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59 (Sweetening, Acidification) Figure 3-2. Schematic drawing of steps followe d in preparation of storage study samples Microbial Tests Aerobic plate count (APC), and yeast a nd mold count (YM) of untreated, DPCD and heat pasteurized samples were determined by using 3M Petrifilms (3M Microbiology, St.Paul, MN). The pH of coconut water wa s first adjusted to around 7.0 with 1N NaOH and then 10-fold serial dilu tions were prepared by adding 10 mL of coconut water into 90 mL of Butterfield’s phosphate buffer (Hardy Diagnostics, Santa Maria,CA). Two replicates of each dilution were prepared and each was plated on two replicates of 3M Petrifilms. Aerobic plate petrifilms were incubated at 35oC for 48 hr, while yeast and mold petrif ilms were incubated at 25oC for 5 days before counting. The petrifilms with the cf u’s between 20 and 200 were take n into consideration and the average cfu’s corresponding to the dilution was calculated. Coconut wate r Heat treatment 74oC, 15 s DPCD 34.5 MPa, 25oC, 13% CO2, 6 min Untreated (Control) Carbonation Carbonation Carbonation Storage 4oC, 9 weeks

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60 pH Orion (EA 920) pH meter (Boston, MA) wa s used for pH measurements. The pH meter was calibrated using pH 4 and pH 7 st andard solutions (Fis her Scientific, NJ) on each test day. The pH measuremen ts were done in triplicate. Titratable Acidity (%TA) A Brinkmann Instrument (Brinkmann In struments Co., Westbury, NY) consisting of Metrohm 655 Disomat, Metrohm 614 Im pulsomat and Metrohm 632 pH-meter was used for titration of coconut water samples. Samples were placed in a vacuum oven at room temperature (22oC) and 0.75 atm vacuum for 1 hr in order to remove CO2 gas before titrating. 20 mL of coconut water sample was titrated to an end point of pH 8.2 by using standardized 0.1 N NaOH and the amount of NaOH used for titration was recorded. Percent titratable acidity (w/v) was expr essed as % malic acid and calculated by the following equation: %TA= (mL of NaOH used) (Normality of NaOH) (meq of malic acid = 0.067) (100)/ (mL of sample) %TA measurements were done in triplicate for each sample. oBrix A Fisherbrand hand held refractometer with a 0o to 18o Brix scale (Fisher Scientific, Pittsburg, PA) was used for oBrix measurements. 2-3 drops of coconut water were placed onto the prism and the reading was recorded. Measurements were done in duplicate. Color Color of coconut water samples was measured in a CIE L* (Lightness) a* (Redness) b*(Yellowness) color scale by us ing the Colorgard 14 system (BYK-Gardner

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61 Inc., Columbia, MD). Quartz halogen lamp (2845 K) was used as the light source, and allowed to warm up for 10 min prior to meas urements. The system was calibrated using black (Zero reference) and white standa rd (L, a*, b*: 94.31, -0.92, -0.50) tiles. A standard measurement was done by placing a gl ass cup filled with 50 mL of distilled water and the white tile placed on top of the cup in a facedown position. The same procedure was followed with the coconut wa ter samples. The cup was rinsed with distilled water and wiped with Kimwipes be tween samples. Measurements were done in triplicates. Sensory Evaluation Sensory panels were conducted during stor age at weeks 0, 2, 3, 5 and 9 in order to evaluate overall likeability, aroma, taste a nd off flavor of untreated, DPCD and heat pasteurized samples. University of Florida FSHN Dept.’s taste panel facility (University of Florida, Gainesville, FL) consisting of 10 private booths with computers was used to conduct sensory panels. Samples were stored cap ped in champagne bottles in an ice bath before being poured into 60 mL plastic cups, in order to prevent carbonation loss. Each sample was assigned with a randomly selected three-digit code, and placed in cups on a tray in all possible combinati ons of order. Red light was us ed in the panelist booths in order to prevent bias on samples due to pinking of some samples. Panelists were asked to answer some demographic questions at the beginning, and then were offered with an untreated (fresh control) refe rence, and three samples (fresh control, DPCD treated, heat treated). Panelists were asked to rate aroma and taste difference of each sample from the given reference (continuous 15 cm line scale wi th values from 0 to 15) using differencefrom-control test. In addition, overall likeability (9 point he donic scale), off flavor (6 point scale) and their purchase intent for each sample were asked. Panelists took a bit of

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62 cracker and a sip of water to rinse their mouth between the samples. Fifty untrained panelists evaluated the samples at each storage week. Compusense 5 software (Compusense Inc., Ontario, CA) was used to design and conduct the test, and to collect and analyze the data. Sample ba llots that were used in sens ory panels are given in Table E-10. Flavor Analysis Solid phase micro-extraction (SPME) was used to extract aroma compounds from coconut water. The SPME fiber was a 1 cm StableFlex PDMS/CAR/DVB fiber (Supelco, St.Louis, MO) which is a bipolar phase fibe r suitable to extract high and low volatile compounds. 10 mL of coconut water was placed into 40 mL glass vials and brought to 42-45oC in a water bath. SPME fiber was insert ed into the headspace of the vial and extraction was held under c ontinuous stirring at 42-45oC for 45 min using a magnetic stir bar. SPME fiber was inserted into the GC injection port and exposed for 5 min for desorption of aroma compounds. GC/O (H P 5890 Series II) equipment with a FID detector was used to separate and analy ze the aroma compounds. Two different columns were used in GC/O; a non-polar DB-5 column (Zebron, 30 m x 0.32 mm ID x 0.50 m FT) and a polar Carbowax column (Restek, 30 m x 0.32 mm ID x 0.5 m df). Temperature programming conditions for GC/O using each column are given in Table 32. With each column, two persons sniffed twice each SPME extract. Sniffers used a continuous scale slide marked as low, medium and high to rate the intensity of the sniffed compound and also indicated aroma descri ptors of each sniffed compound at the corresponding retention time. The chromatograms for both the FID and sniff port were recorded and saved. C5-C20 alka ne standards were run at ea ch experiment day and their

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63 retention times were recorded. Their litera ture linear retention indices (LRI’s) were plotted against their retention times and the equation relating the LR I’s as a function of retention times was obtained by using the Excel graph options. The same equation was used to calculate LRI’s of the sniffed co mpounds at the corresponding retention times. Examples of LRI calculations of standard al kanes and the formulas relating LRI’s to the retention times are given in Table C-1 a nd Figure C-1, respectively, for the Carbowax column, and in Table C-2 and Figure C-2, respectively, for the DB-5 column. An aromagram was constructed by plotting average sn iff intensity (average of sniff port peak areas) against the calculated LRI’s. A GC/MS (Perkin Elmer; Wellesley, MA ) equipment with quadrupole-ionization detector was used for identification of flavor compounds in coconut water. This equipment used TurboMass 5.01 (Wellesley, MA) software for the integration and analysis, and a NIST (MS Research 2.0) databa se as the library of the compounds for the identification. The SPME extracts were inject ed and exposed through the injection port for 5 min. GC-MS temperature pr ogramming conditions were; 40oC (initial) to 240oC (final) at a 7oC/min ramp rate and with a 9.5 min holding time. Each peak on GC/MS chromatogram was first integrated and then searched through the NIST database for the identification by using the software. The softwa re gave a list of compound names, that matched the peak with the degree of match for each listed compound over 1000. An example of this identification procedure including the chromatogram and the NIST identification sheet is shown in Figure C-3. C5 -C20 alkane standards were used to obtain an equation relating retention time s of compounds to the LRI’s.

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64 Table 3-2. Temperature programming conditions used for GC/O runs with DB-5 and Carbowax columns. Column Type Initial Oven Temp. (oC) Final Oven Temp. (oC) Ramp rate (oC/min) Final holding time (min) Detector A Temp. (oC) Detector B Temp. (oC) Injector Temp. (oC) DB-5 40 265 7 5 270 110 220 Carbowax 40 240 7 5 250 110 220 Data Analysis Response surface regression analysis of Box-Behnken experimental data was performed using SAS 9.1 software program (Cary, NC). A 3-D Response surface plot was obtained using STATISTICA 6.0 (Tulsa, OK). The optimal conditions for pressure, temperature and CO2 level were determined by consider ing the statistical significance (p <0.10) of each variable on microbial reduction. The significance of difference between tr eatment means for the storage study data (pH, %TA, oBrix, color (L*, a*, b*), sensory attri butes) was determined by analysis of variance (ANOVA) using SAS 9.1 software (Cary, NC) at a significance level of =0.05. The means for each treatment were compared using Duncan’s multiple comparison test ( =0.05) to determine statistically different samples. Effects of storage time and interaction effects were also included in the ANOVA analysis.

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65 CHAPTER 4 RESULTS AND DISCUSSION Formulation of Coconut Water Beverage Regulatory and consumer likeab ility aspects were considered in the formulation of the coconut water based beverage. FDA regulati ons regarding low acid foods require that action be taken to inhibit the growth of C.botulinum Coconut water had a pH of around 5.0, and therefore, it must be lowered to belo w 4.6. Informal taste panels were conducted to decide on the suitability of different organic acids and commercially available pH lowering compounds. Malic acid wa s liked the most and was used to lower the pH to 4.3. Splenda (McNeil-PPC, Fort Washington, PA) wa s also added as a sweetener at about 0.7 % (w/w) to compensate for the resulting s ourness. Preliminary tasting studies also showed that carbonated coconut water wa s preferred over non-carbonated. Therefore, coconut water beverage was formulated as a carbonated, acid ified and sweetened beverage with a pH of 4.3 and oBrix of 6.0. Objective 1: Quantification of Microbial Reduction in Coconut Water as a Function of Treatment Conditions To quantify microbial reduction in coconut water as a function of DPCD treatment conditions, response surface methodology (RSM) was used. The number of experimental runs and the treatment conditions at each run were determined by using a 3-factor 3-level Box-Behnken experimental design. This desi gn is one of the response surface designs that allows fitting of a quadratic model and has the advantage of re quiring fewer number of runs compared to other response surface designs when three factors are used. The Box-

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66 Behnken design suggests a sphere in the cubi c process space where the surface of the sphere is tangential to the midpoints of the each edge of the cubic space (Figure 4-1). Center point experiments were replicated three times. Figure 4-1. Geometry of the 3-f actor 3-level Box-Behnken design Three factors of this design that repres ented independent variables in the RSM model were X1:Temperature (coded), X2:Pressure (coded) and X3: CO2 level (coded). The dependent variable was Y: log microbial reduction. Coconut water that was thawed and formulated by acidification and sweeten ing was aged at room temperature (24oC) to reach an initial load of 107cfu/mL. Next, 15 experimental ru ns that were determined by Box-Behnken design were conducted at the three levels of temperature, pressure and CO2 levels (Table 3-1). Table 4.1 shows the e xperimental conditions of each run and the measured log reduction in total numbers of aerobic bacteria. The log reductions were calculated by subtracting final log numbers of bacteria from initial log numbers. Initial and final numbers of bacteria were dete rmined by taking average cfu/mL counts on petrifilms with the cfu’s less than 200 cfu/mL The average initial and final aerobic plate counts (APC) standard deviations at each experimental condition are given in Table B1.

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67 Table 4-1. Log microbial reductions at each experimental point determined by BoxBehnken design RUN# X1 X2 X3 T (oC) P (MPa) CO2 level (g/100g juice) Log microbial reduction experimental (A) Log microbial reduction predicted (B) Residual: (A-B) 1 -1 -1 0 20 13.8 10 4.92 4.90 0.02 2 1 -1 0 40 13.8 10 5.03 5.15 -0.12 3 -1 1 0 20 34.5 10 4.90 4.90 0.00 4 1 1 0 40 34.5 10 5.61 5.15 0.46 5 -1 0 -1 20 24.1 7 4.47 4.25 0.22 6 1 0 -1 40 24.1 7 5.40 5.34 0.06 7 -1 0 1 20 24.1 13 5.42 5.66 -0.24 8 1 0 1 40 24.1 13 4.66 5.06 -0.40 9 0 -1 -1 30 13.8 7 5.30 5.15 0.15 10 0 1 -1 30 34.5 7 4.71 5.15 0.56 11 0 -1 1 30 13.8 13 5.90 5.72 0.18 12 0 1 1 30 34.5 13 6.18 5.72 0.46 13 0 0 0 30 24.1 10 5.58 5.38 0.20 14 0 0 0 30 24.1 10 4.99 5.38 -0.39 15 0 0 0 30 24.1 10 5.22 5.38 -0.16 X1: Coded variable for Temperature (T); X2: Coded variable for Pressure (P); X3: Coded variable for CO2 level The RSM analysis of data was done usi ng SAS 9.1 statistical software program (Cary, NC). First, the following quadratic m odel that included three variables X1, X2 and X3 was used and the RSM regression was conducted on the data: Y= a+ b*X1 + c*X2 + d*X3 + e*X1*X1 + f*X2*X1+ g*X2*X2 + h*X3*X1 + i*X3*X2 + j*X3*X3 where Y: log microbial reduction, X1: Temper ature (coded), X2: Pressure (coded), X3: CO2 level (coded) and the letters from a to j represent corresponding coefficients for each parameter of this model. The SAS code and out put of the analysis are given in Table B-2 and B-3, respectively. The regression coefficient R2 was 0.76 for this model. Significance of each parameter was decided at =0.1 level and the parameters with p value > 0.1 were

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68 excluded from the model. Results showed that only the parameters X3 and X3*X1 were significant, therefore any parameter with X2 (Pressure) variable were excluded from the model. Similarly, Sims and Estigarri bia (2002) reported that increasing CO2 pressure from 7.5 to 15 MPa did not significan tly increase micr obial reduction. Next, another RSM regression analysis was performed by using the modified model that involves only the parame ters with variables X1 and X3: Y= a + b*X1 + c*X3 + d*X1*X3 + e*X1*X1 + f* X3*X3 The SAS output of this anal ysis is given in Table B4. The regression coefficient R2 was 0.63 for the model. The model with the estimated coefficients gives the prediction of log microbial reduction (log red) as a function of temperature (coded) and CO2 level (coded): log reduction = 5.381 + 0.124*Temp + 0.284*CO2 – 0.355*Temp2 0.423*CO2*Temp + 0.05*CO2 2 Coefficients were determined for the coded values of each variable. The log reductions predicted at fifteen experiment al runs using this equation ar e close to the experimental log reductions (Table 4-1). Three-dimensiona l plots of the respons e surface for this equation are given in Figure 4-2. Apparently, there is not an optimum point on the surface plot at which (log reduction) / (Temp)= 0 and (log reduction/ (CO2)=0 gives the highest microbial reduction. The surface plot shows that at lowerand mid-temperatures, microbial reduction increases as CO2 level increases. However, at hi gher-temperatures this behavior changes, and either CO2 level is not effective or cause s a decrease in microbial reduction. The amount of dissolved CO2 has a primary role in microbial reduction. CO2 solubility is affected by temperature change and decreases as temperature increases (Dodds and others

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69 1956). Therefore, increased CO2 does not cause increased micr obial reduction at higher temperatures due to its limited solubility. On the other hand, highest microbial reductions were achieved at temperatures close to middle temperatur e (i.e. temperatures around 2530oC) and highest CO2 level (i.e. CO2 levels around 13%). Therefore, the optimal conditions of DPCD treatment fo r microbial reduction in coc onut water were selected to be 25oC and 13% (g CO2/100 g juice). Predicted log microbial reduction at these conditions is 5.77. Predicted log microbial reduc tions at different levels of temperature and CO2 using the model can be found in CD file: “predicted log reductions.doc”. 4.38 4.52 4.66 4.80 4.94 5.08 5.22 5.36 5.50 5.64 above 4.92 5.03 4.90 5.61 4.47 5.40 5.42 4.66 5.30 4.71 5.90 6.18 5.58 4.99 5.22 T e mp e r a t u r e oC20 30 40% C O213 10 7log reduction6.6 6.2 5.8 5.4 5.0 4.6 4.38 4.52 4.66 4.80 4.94 5.08 5.22 5.36 5.50 5.64 above 4.92 5.03 4.90 5.61 4.467 5.40 5.42 4.66 5.30 4.71 5.90 6.18 5.58 4.99 5.22 % C O27 10 13log reductionT e m p e r a t u r e ,oC40 30 20 6.6 6.2 5.8 5.4 5.0 4.6 Figure 4-2. Plots of the response surface for th e quadratic model with the variables X1: Temperature (coded) and X3: %CO2 level (coded)

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70 Objective 2: Evaluation of Physical, Chemical and Microbial Quality of DPCD Treated Coconut Water Beverage during Storage The storage study was conducted at 4oC for 9 weeks for “untreated”, DPCD treated and heat treated coconut wa ter beverage. Untreated samples were obtained by thawing the fresh frozen coconut water and form ulating it by acidification, sweetening and carbonation. Heattreated samp les were pasteurized at 74oC for 15 s after sweetening and acidification. DPCD treated coconut water wa s processed at the previously determined optimum conditions (Temp=25oC, CO2 level=13%) for microbial reduction after sweetening and acidification. The pressure was 34.5 MPa and treatment time was 6 min. Heat and DPCD treated samples were carbonate d after treatments. Samples were tested for microbial growth, pH, titratable acidity, oBrix and color throughout storage. Microbial quality of coconut water beve rages was evaluated by measuring total aerobic bacteria (APC) and yeast and mold (Y M) counts. The plot of APC results for each treatment during storage time are shown in Figure 4-3 and the data (cfu/mL) is given in Table D-1. One tail t-tests ( =0.05) were used to determine whether there was significant difference in APC and YM count s of each treatment between week 0 and week 9 (Table D-2). Data showed that numbe r of aerobic bacteria in untreated coconut water stayed almost unchanged during the firs t 6 weeks but showed significant increase after week 6 and reached > 105 cfu/mL at the end of 9 weeks. There is only one data point after week 6 to show that increase, theref ore further study would be useful to understand the extent of this increase between week s 6 and 9. In addition, the comparison of carbonated coconut water with non-carbonated coconut water for microbial counts would help to understand if carbonation was the reason for no microbial increase during the first 6 weeks.

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71 0.1 1 10 100 1000 10000 100000 1000000 0246810 Storage time (Weeks)cfu/mL Control DPCD Heat Figure 4-3. Total aerobic plate counts (APC) of untreated cont rol, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa,13% CO2 for 6 min; Heat treatment at 74oC for 15 s) -1.00 4.00 9.00 14.00 19.00 24.00 0246810 Storage time (Weeks)cfu/m L Control DPCD Heat Figure 4-4. Yeast counts of untreated contro l, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s) Numbers of aerobic bacteria decreased si gnificantly in DPCD and heat treated samples. The lack of oxygen in the bottles caused by carbonation might have caused the

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72 decrease in the microbial grow th. It is important to note th at untreated coconut water had initial microbial loads of around 3 logs, but DPCD or heat treatments unexpectedly did not cause total inactivation that must be achieved by pasteurization. In order to understand the real cause for the presence of microorganisms after treatments, every step of the process was reevaluated for the po ssibility of contamin ation. The carbonation process was a possible cause since this step is conducted in nonaseptic conditions. The carbonation process was repeated by using st erile water under similar conditions to coconut water, and the initial and final microbi al counts of sterile di stilled water showed that carbonation might cause contamination by up to 3 logs. The APC counts for distilled water before and after carbonation are given in Table D-3. Heat treated samples were apparently less contaminated than DPCD treated samples. The decrease in the aerobic bacteria growth from week 0 to 9 was 1 l og in DPCD treated samples and approximately 2 logs in heat treated samples. Yeast counts of all treatments were low throughout storage. There was no detectable mold growth while yeast counts were only around 1 log in itially and decreased to no growth by the end of storage (Figure 44). Yeast counts for each treatment are given in Table D-4. Measured pH values of untreated, DPCD tr eated and heat treated coconut water are given in Table D-5 and the plot of pH duri ng storage is shown in Figure 4-5. Statistical analysis of pH data by analysis of varian ce (ANOVA) suggests a si gnificant storage time and treatment interaction (Table D-6). DPCD treated samples had significantly lower pH than other treatments ( =0.05). However, the pH means of treatments are 4.199, 4.197 and 4.190 for heat treated, control and DPCD treated samples, respectively. Although

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73 these values are statistically significantly di fferent, they are exactly the same values for two significant figures, i.e. 4.20. It is suggested that the high accuracy of the pH meter in the triplicate measurements lowers the sum of squares for errors and causes this result. The pH of the samples did not change much during storage and was fluctuating around 4.20. Theoretically, a pH change was not expe cted during storage except for microbial problems. However, microbial data do not support such a decrease. The slight fluctuations in pH for the samples could be explained by sample-to-sample differences. 4.1 4.12 4.14 4.16 4.18 4.2 4.22 4.24 4.26 02359 Storage time (Weeks)pH Control DPCD Heat Figure 4-5. The pH of untreated, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s) oBrix values for treatments during storage were close, and the maximum change in oBrix was 0.15 units. This could possibly be due to sample-to-sample variation. Mean oBrix values of treatments were 6.04, 6.0 and 6.0 for control, DPCD and heat treated coconut water, respectively. Theoretically, a change in oBrix of samples was not expected during storage unless there is evaporation or fermentation of the samples. Samples were tightly capped in glass champagne bottles and microbial data or pH data do not support such changes. Standard errors for oBrix measurements are zero, which indicates the high

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74 repeatability of the measurements. Statistical analysis of data show ed significant weekly changes and treatment differences since sum of squares of the error term is too low as a result of high repeatability in the measurem ents. The data is given in Table D-7 and the plot of the data during storage is give n in Figure 4-6. The SAS output of ANOVA for oBrix data is shown in Table D-8. 5.75 5.8 5.85 5.9 5.95 6 6.05 6.1 6.15 6.2 6.25 012359Storage time (Weeks)Brix Control DPCD Heat Figure 4-6. The oBrix of untreated, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s) Titratable acidity for untreated, DPCD and heat treated coconut water were measured during storage and expressed as % malic acid (w/v) equivalents (Table D-9). Statistical analysis of data by ANOVA s howed that DPCD treated samples had significantly higher titratable acidity (mean = 0.282 g malic acid / 100 mL coconut water) whereas untreated and heat treated sample s had mean values of 0.259 and 0.266 g malic acid / 100 mL coconut water, respectively (T able D-10). The weekly mean % titratable acidity values for each treatment are given in Figure 4-7. The reason for higher overall titratable acidity of DPCD treated samples may be insufficient removal of CO2 during vacuum treatment. DPCD treated samples we re expected to have higher amounst of CO2

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75 due to the residual dissolved CO2 after DPCD applica tion. Increase in titratable acidity of juices by DPCD was observed also by studies of Kincal D. (2000) for orange juice and Lim and others (2006) for tangerine juice. During storage, titratable acidity may ch ange due to acid generation by microbial growth. However, there was no increasing tre nd in % TA for the samples during storage. The only treatment that shows a significant increase in microbial growth was the untreated control sample, but it did not show an increase in titratable acidity during the last week of storage. Although heat treated samples did not show a microbial increase, titratable acidity showed some fluctuations during storage. These could be due to the bottle-to-bottle variations during carbonation. Color of coconut water samples was measur ed in CIE color scale as L*, a* and b* values and the data is shown in Table D-11. Data from week 0 were omitted because of measurement errors. The plots of L*, a* and b* values against storage weeks are presented in Figure 4-8, 4-9 a nd 4-10, respectively. The data shows slight changes in L*,a*,b* values for treatments during storage. L* values of the samples, representing lightness, decreased from week 0 to 5 and then increased slightly at week 9. The a* value, which represents redness on the positiv e scale and greenness on the negative scale, increased from week 2 to 5, then decreased fo r heat treated sample, and increased up to week 9 for the untreated control sample; wher eas it increased from w eek 2 to 3 and then decreased for the DPCD treated sample. These re sults need to be considered with caution because some of the samples started pinki ng from the first day of storage. Color measurements were done on randomly selected bottles at each storag e week. Therefore, there was large variation in redness for even the same treatment sample from one bottle

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76 to another, depending on the initiation of pinking. From the preliminary experiments, it was known that once the coconut water in a bott le starts pinking, th e intensity of pinking increased during storage. Normally, one would expect an increase in a* value for all treatments because independent of the treatm ent, all bottles eventually showed pinking during storage. The changes in L*, a* and b* values of the treatments could be due to bottle to bottle variations and it is not possi ble to make a clear c onclusion based on this data. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 02359Storage time (Weeks)% TA (%malic acid (w/v)) Control DPCD Heat Figure 4-7. Titratable acidity (as % malic acid (w/v)) of untreated, DP CD treated and heat pasteurized samples during st orage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s)

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77 0 8 16 24 32 40 48 56 64 72 80 2359 Storage time (Weeks)L* value Control DPCD Heat Figure 4-8. Mean L* values of untreated cont rol, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s) -3 -2 -1 0 1 2 3 4 5 6 7 2359 Storage time (Weeks)a* value Control DPCD Heat Figure 4-9. Mean a* values of untreated cont rol, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s)

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78 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 2359 Storage time (Weeks)b* value Control DPCD Heat Figure 4-10. Mean b* values of untreated cont rol, DPCD and heat treated coconut water during storage (DPCD treatment at 25oC, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at 74oC for 15 s) Objective 3: Comparison of Untreated Control, DPCD and Heat Treated Coconut Water by Sensory Evaluation Consumer panels of 50 untrained panelists were used to evalua te overall likeability, aroma, taste and off flavor of untreated (c ontrol), DPCD and heat treated coconut water beverage during storage at 4oC in glass champagne bottles. Panels were conducted at weeks 0, 2, 3, 5 and 9. The taste panel data during storage is pres ented in Table E-1. Overall likeability of samples was rated on a 9 point scale where the score 1= dislike extremely, and 9= like extremely. AN OVA was conducted to see if there were significant differences in overall likeability of samples due to trea tment or storage time effects. The SAS output of ANOVA is s hown in Table E-2. The means of overall likeability scores for each treatment for the overall storage time shows that untreated control (mean=5.03a) and DPCD treated sample (mean=4.95a) were liked the most and heat treated sample (4.58b) was liked significantly less than the other samples. Results showed that there was significant stor age time-treatment interaction at =0.05, therefore,

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79 overall likeability of treatments was changing at different rates duri ng storage time. For this reason, overall likeability of different treatments was compared separately at each storage time by ANOVA. The mean overall like ability scores and standard errors are given for treatments at each storage w eek in Table E-3. Figure 4-11 shows the comparison of each treatment for overall likeab ility scores at different storage weeks. Initially, DPCD treated and untreated samples were liked significantly more than heat treated sample. However, starting from the 2nd week, overall likeability of samples moved close to each other and this difference became insignificant. It is hard to explain the reason for this change. There could be so me flavor and aroma change in the samples to cause a change in overall likeability sc ores. Since the samples were carbonated and stored in glass bottles, flavor change due to oxidization is not expected. From the previous studies, a change in the overall lik eability due to storage time could be possible since microbial growth during st orage could affect flavor an d aroma. Kincal and others (2005) reported that microbial load increased in DPCD treat ed samples during storage. However, the results of microbial tests showed that there was no increase in the microbial counts of DPCD or heat treated samples. Th erefore, the change in overall likeability should not be due to microbial changes.

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80 1= dislike extremely 9= like extremelya a a a a a a a a a a a a a b0 1 2 3 4 5 6 7 8 9 02359Storage time (Weeks)Overall likeability Control DPCD Heat Figure 4-11. Comparison of overall likeabil ity of each treatment during storage A difference from control test was conducte d to evaluate the taste and aroma of heat and DPCD treated samples. Fresh untreat ed coconut water was gi ven to the panelists as a reference control every w eek, and panelists were asked to rate the difference in taste and aroma of three samples from this referen ce control. One of the samples was the same as the reference control. Ideally, the differe nce from reference control for the control sample should be rated as zero by the panelist s since they are the same. Although most of the panelists rated this as close to zero, some panelists rated this difference as high as 10. The taste and aroma difference data for control samples were sorted from lowest to highest scores at each week and the freque ncy of each score was plotted as histograms. Figure 4-12 and Figure 4-13 show the hist ograms of aroma difference and taste difference scores, respectively, for the control samples. Scores grea ter than 6 for taste difference and greater than 5 for aroma difference from control were excluded from the

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81 data, and the ANOVA was conducted on this ne w ‘corrected’ data. This correction was needed to exclude the effect of outlying panelists from the st atistical analysis results. Statistical analysis of corrected ar oma difference from control data by ANOVA showed that DPCD and heat treated coconut water treatments were not significantly different in the overall mean aroma scores (Table E-4). Storage was not significantly affecting the aroma scores of the samples ( =0.05). The overall mean scores for aroma difference were 2.12a, 1.92a and 1.15b for DPCD-treated, heat -treated and untreated control samples. The weekly comparison of treatments for the aroma scores shows that DPCD and heat treated samples were not ra ted significantly different for aroma (Figure 4-14). The mean aroma difference from control scores of the panelists at each week are given in Table E-5. The SAS output of the ANOVA of the taste difference-from contro l data is in Table E-6. The overall mean values for taste difference scores were 2.08a, 3.67b and 4.17c for the control, DPCD and heat treated coconut water, respectively. Treatments were significantly different for the taste scor es. On the other hand, weekly ANOVA results showed that DPCD and heat tr eated samples were rated signi ficantly different at week 0 only, and this difference was insignificant st arting at week 2 until the end of storage (Figure 4-15). The mean taste difference-from control scores for panelists at each week are given in Table E-7. These results confir m the overall likeability of the samples throughout storage. Heat treated samples were rated significantly higher for the taste difference from control at week 0 and liked the least. The low intensity levels of flavor and aroma in coconut water may cause larger relative errors where comparing differently

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82 treated samples, and mask the protective effects of DPCD compared to thermal treatments. Figure 4-12.The frequency histograms of st orage study aroma difference from control scores of untreated (control) samples Week 0 aroma difference (control) 0 10 20 30 012345678910 ScoreFrequency Week 2 aroma difference (control) 0 5 10 15 20 25 30 012345678910 ScoreFrequency Week 3 aroma difference (control) 0 10 20 30 012345678910 ScoreFrequency Week 5 aroma difference (control) 0 5 10 15 20 25 30 012345678910 ScoreFrequency Week 9 aroma difference (control) 0 10 20 30 012345678910 ScoreFrequency

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83 Figure 4-13.The frequency histograms of st orage study taste difference from control scores of untreated (control) samples Week 0 taste difference (control) 0 5 10 15 012345678910 ScoreFrequency Week 2 taste difference (control) 0 5 10 15 012345678910 ScoreFrequency Week 3 taste difference (control) 0 5 10 15 012345678910 ScoreFrequency Week 5 taste difference (control) 0 2 4 6 8 10 12 012345678910 ScoreFrequency Week 9 taste difference (control) 0 5 10 15 012345678910 ScoreFrequency

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84 0= not different 15= very differentb a b b b a a a ba ba ba a a a a0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1502359 Storage time (Weeks)Aroma difference score Control DPCD Heat Figure 4-14. Comparison of treatments for arom a difference from control scores during storage 0= not different 15= very differentc b bb b b a a a a a a a a a0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1502359 Storage time (Weeks)Taste difference scor e Control DPCD Heat Figure 4-15. Comparison of treatments for ta ste difference from control scores during storage Panelists were also asked to rate off fla vor in the samples on a 6-point scale. The ANOVA of the data suggested that heat tr eated samples had significantly higher overall mean off-flavor scores (mean=2.99b) than untreated (mean=2.68a) and DPCD treated

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85 (mean=2.66a) coconut water (Table E-8). Weekly comparison of the treatments for off flavor formation showed th at significant difference between treatments was only occurring at weeks 0 and 2, and became insi gnificant starting from week 3 (Figure 4-16). The weekly mean off-flavor scores are in Table E-9. These results also confirm the overall likeability and taste scores for treatm ents. These results suggest that heat treated samples had some off flavor at the beginning of storage which caused a significantly higher rating for taste difference from untreat ed control and lowest rating for likeability of heated samples initially. However, in late r weeks either this off flavor was masked by other flavors, or DPCD treated samples also developed off flavors and overall likeability or taste difference scores for treatments became closer. 1= none 6= extremely intense b ab a a a b b aa a a a a a a1 2 3 4 5 6 02359 Storage time (Weeks)Off flavor score Control DPCD Heat Figure 4-16. Comparison of treatments fo r off flavor scor es during storage Overall mean values and comparison of means for overall likeability, taste and aroma differences from contro l and off flavor scores for each treatment are summarized in Table 4-2.

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86 Table 4-2. Comparison of overall mean values for sensory attribut es from different treatments ( =0.05). Overall Likeability* Aroma difference from control* Taste difference from control* Off flavor* Untreated 5.03a 1.15a 2.08a 2.68a DPCD 4.95a 1.92b 3.67b 2.66a Heat 4.58b 2.12b 4.17c 2.99b *Different letters in a colu mn mean no significant differe nce between means at =0.05. (Mean values are averages of all weeks) To evaluate purchasing poten tial of the DPCD treated coconut water beverage, panelists were asked if they would buy th e product. The percentages of panelists answering “yes” to that question are given in Table 4-3 for each treatment at each storage week. The overall percentages of panelists who would purchase the products were 32.8% for untreated control, 34.8% for DPCD treat ed and 28% for heat pasteurized samples. Panelists who answered “no” were asked if they would buy that product if they knew about its rehydrating properties. Table 4-4 gives the percentages of the panelists who were still saying “no” to pur chasing the products. Panelists who answered “no” to the first question and still answer ing “no” to the second questio n were 72.4% for control, 72.0% for DPCD and 76.4% for heat pasteurized coconut water. It seems that informing the panelists about the health benefits of coconut water coul d only slightly change their purchase intent. Table 4-3. The percentages of panelists an swering “yes” to the question: Would you buy that product? Week Control (% of panelists)DPCD (% of panelists)Heat (% of panelists) 0 34 38 22 2 32 30 16 3 38 36 32 5 26 40 34 9 34 30 36

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87 Table 4-4. The percentages of panelists answer ing “no” the first purchase intent question and answering still “no” the second pur chase intent question: Would you buy this product if you knew coconut water had rehydrating properties? Week Control (% of panelists) DPCD (% of panelists)Heat (% of panelists) 0 76 68 85 2 91 69 76 3 71 72 76 5 57 80 70 9 67 71 75 Objective 4: To Identify Flavor Compounds in Coconut Water and Compare Flavor Profile of DPCD and Heat Treated Coconut Water Literature studies on coconut flavors are limited to fresh coconut meat, milk, or roasted and grated coconut meat (Lin a nd Wilkens 1970, Jayalekshmy and others 1991, Jirovetz and others 2003). Since there was no information on flavor compounds of young green coconut water in the literature, it was necessary to identify flavor compounds in untreated coconut water before comparison of flavor profiles in DP CD and heat treated samples. Flavor compounds were identifie d in untreated coconut water using GC/MS with the National Institute of Standards and Technology (NIST) library database and also some compounds were tentatively identifie d by matching LRI’s obtained from GC/O Carbowax and DB-5 columns with those obtai ned from the literature databases. The flavor compounds that were identified in coconut water by GC/MS match and also by tentative match using GC/O are listed in Table 4-5. Studies with coconut show that lactones give the characteristic coconut aroma, and also some esters, aldehydes and alcohols are among the flavor compounds in coconut meat or milk (Lin and Wilkens 1970, Jirovetz and others 2003). Although none of the -lactones were identified in this study from coconut water, some esters and al dehydes were identified. In order to have a better confirmation, standard chemicals of some of these suspected compounds were obtained and run in GC/MS. Corresponding GC /MS chromatograms are given in Figure

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88 C-4. Each of the corresponding peaks were in tegrated and identifie d by the software, and their retention times, GC/MS degr ee of match values and calc ulated LRI’s were recorded, and are given in Table C-3. GC/MS identific ation outputs that were obtained by NIST library match of each peak can be found in: CD file “feb2nd GCMS Standards 4 groups.xls”. Coconut water samples were r un by GC/MS at the same conditions as standards, and some flavor compounds were positively confirmed using a similar peak integration and identification procedure. Th e compounds that were positively confirmed in coconut water are shown in red color in Table 4-5 with the calcu lated LRI’s for those observed in fresh coconut water (LRI Wa x observed) and calculated LRI’s of the standard chemicals (LRI Wax standard). Some of GC/MS chromatograms that were obtained by running fresh coconut water sample s are given in Figure C-5, and the peak identification outputs obtained using NIST library matches can be found in: CD files “March 9th GCMS CW identification of peaks.xls” and “Feb1st GCMS CW identification of peaks.xls”. Some of the flavor compounds were tentatively determined in fresh coconut water (Table 4-5). Two sni ffers recorded the retention times and gave the aroma descriptors for sniffed compounds using the olfactory port of GC/O. This procedure was repeated twice in GC/O with Carbowax and DB-5 columns. LRI’s of the sniffed compounds were calculated for each colu mn. Literature flavor databases (Acree and Arn 2005, CREC 2005) provide LRI’s of th e chemical compounds in various GC/O columns with aroma descriptors. 1-Octene-3 -one and 2,6-nonadienal had LRI’s close to literature values in both columns and expected aroma descriptors by the sniffers (Table 45). Retention times, calculated LRI’s and arom a descriptors given by sniffers in GC/O runs are given in Table C-4 for each column used. The raw data of FID and olfactory port

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89 responses can be found in CD folders: “feb7th DB5 CW” and “feb 8th CW wax”. Methional is tentatively identified in co conut water by matching its observed LRI in Carbowax column with the li terature LRI (Table 4-5), a nd because its boiled/cooked potato aroma described by sniffers (Table 4-6) is typical of that compound. Table 4-5. The list of flavor compounds that were identified in untreated fresh coconut water Compound LRI Wax observed GC/MS / GC/O LRI Wax standard / literature LRI DB-5 observed LRI DB-5 literature GC/MS degree of match (over 1000) Tentative match Ethyl butanoate 1049 /10551048 797 800 826 1-Butanol 1163 1154 /1145----675 893 Octanal 1308 /1311 1313 /1302----1002 864 Octene-3-one,1 1316 1315 980 980 ---------Tentative 6-methyl,5heptene-2-one 1355 1362 ----------atomic spectrum matched Nonanal 1412 1419 /1409-----1107 873 Ethyl octanoate 1450 /1456 1451 /14441196 1195 917 Methional 1476 1478 -----913 -----Tentative 2,6-nonadienal 1575 1611 1152 1155 ------Tentative Undecanal 1630 /1631 1630 -----1306 881 Methyl dodecanoate 1819 1820 -----1509 873 Octanoic acid 2077 2077 /2047-----1279 849 Standard chemicals of GC/MS identified co mpounds were run in GC/O and sniffed by the sniffers in order to understand whethe r these flavor compounds were aroma active at certain concentrations, and also to be familiar with the possible aroma compounds in coconut water. Tables 4-6 and 4-7 give a list of the standard chemi cals that were sniffed through GC/O with DB-5 and Carbowax column s, with the observed LRI’s, retention times and the aroma descriptors given by the sniffers. Some of these compounds were not aroma active at the given concentrations, but sniffers detected most of the standard

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90 chemicals at 10 ppm and 100 ppm concentratio ns through DB-5 or Carbowax columns. Table 4-7 shows the experiment with standard chemicals that were run through Carbowax column. Coconut water was also run at the sa me experimental conditions and LRI’s were calculated and aroma descriptors were recorded in order to see if any of the standards could be detected. Among these compounds, 6methyl-5-heptene-2-one and nonanal were aroma active at 100 ppm and 10 ppm concentra tions, respectively; however, they were not detected by sniffers. Sin ce these two compounds were de tected in coconut water by GC/MS identification, these re sults suggest that the con centrations of 6-methyl-5heptene-2-one and nonanal were lower than 100 ppm and 10 ppm, respectively. On the other hand, methyl dodecanoate and octanoic acid were not aroma active at 100 ppm and 10 ppm concentrations, respectively, and were not detected by sniffers. Therefore, although some flavor compounds were detected in coconut water, concentrations were not high enough to be detected by sniffers. Ta ble 4-8 summarizes the list of the detected flavor compounds in coconut water and the aroma descriptors give n to them. The raw data with FID and olfactory responses corre sponding to the Tables 4-6 and 4-7 can be found in CD folders: “Table 4-6 raw data Jan 25th”, “Table 4-7 raw data March 18th”. Aroma profiles of DPCD and heat trea ted carbonated coconu t water beverages were developed by sniffing each sample twice by two sniffers in GC/O olfactory port using a polar Carbowax column. C5-C20 alkane standards were used to calculate LRI values of the sniffed compounds. Aromagrams of the samples were constructed by taking average peak areas of the sniffed compounds in the olfactory port and the corresponding aroma descriptors given by sniffers. Only the compounds that were sn iffed at least twice during four sniffs were reported in the ar omagrams. The retention times, calculated

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91 LRI’s, peak areas of each olfactory response and average peak areas are given with the corresponding aroma descriptors in Table C-5 and C-6 for DPCD (25oC ,34.5 MPa, 13% CO2, 6 min) and heat (74oC, 15 s) treated samples, respec tively. The raw data from GC-O runs can be found in CD file: “March22nd GCO carbonated CW DPCD and heat.xls”. Figure 4-17 gives the comparison of the aromagrams for DPCD (25oC, 34.5 MPa, 13%CO2, 6 min) and heat treated (74oC, 15 s) carbonated coconut water beverages stored at 4oC for 2 weeks. Results showed that most of the aroma compounds were common in DPCD and heat treated coconut water beverages. However, a few more compounds were sniffed in heat treated sa mples. GC/MS chromatograms of coconut water samples also show more peaks detected in heat treated samples compared to DPCD treated coconut water (Figure C-6). Additional aroma compounds in heat treated samples were described as fruity, gr een, nutty, rancid, unpleasant, fatty and burnt aromas. These aromas were probably developed by decom position of compounds due to heating. Table 4-6. Standard chemicals (10 ppm of each in a mixture) that were run in GC/O with DB-5 column Compound name DB-5 Literature LRI LRI observed (DB-5) Rt (min) (DB-5) Aroma descriptor by sniffer Aroma descriptor from literature Propanol 536 ----------Not sniffed Alcohol, pungent Ethanol 668 ----------Not sniffed Sweet Butanol 675 ----------Not sniffed Medicine, fruit Octanal 1006 1004 11.30 Soapy, fruity Fat, soap, lemon, green Nonanal 1107 1106 13.53 Butter, chemical,soap Piney, floral, citrusy Nonanol 1154 ----------Not sniffed Fat, green Ethyl octanoate 1195 1196 15.44 Sweet, rose Fruity, fat, floral Nonanoic acid 1275 1271 16.97 Liquid soap Green, fat Octanoic acid 1279 ----------Not sniffed Sweat, cheese Undecanal 1291 1295 17.44 Old leather Oil, pungent, sweet

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92 Table 4-6. Continued Compound name DB-5 Literature LRI LRI observed (DB-5) Rt (min) (DB-5) Aroma descriptor by sniffer Aroma descriptor from literature Gammanonalactone 1366 1358 18.66 Sweet, candy Coconut, peach Deltadecalactone 1469 1463 20.62 Fruity, bubblegum Peach Methyl dodecanoate 1509 ----------Not sniffed Fat, coconut Carvacrol ---------------Not sniffed -----2-ethyl-1-hexanol ---------------Not sniffed -----Table 4-7. Standard chemicals (100 ppm each in a mixture) that were run in GC/O with Carbowax column Chemical Name LRI observed (Carbowax) LRI literature (Carbowax) Rt (min) Aroma descriptor by sniffer Methyl dodecanoate 1813 1795 20.98 No odor 6-methyl-5-heptene-2one 1362 ----13.08 Green, chemical Octanal 1311 1302 12.12 Fatty, green, rancid Ethyl butanoate 1058 1048 7.29 Fruity, sweet, bubblegum Ethyl octanoate 1449 1444 14.73 Sweet Undecanal 1624 17.88 Green Table 4-8. The descriptors given by sniffers for the flavor compounds identified in coconut water Compound Descriptors from sniffers Ethyl butanoate Sweet, apple, candy, fruity Octanal Green, fatty, rancid Octene-3-one,1 Mushroom, dirt 6-methyl, 5-heptene-2-one Aroma active at 100 ppm concentration, but not sniffed in coconut water Nonanal Aroma active at 10 ppm concentrat ion, but not sniffed in coconut water Ethyl octanoate Sweet, cotton-candy Methional Boiled/ cooked potato 2,6-nonadienal Green, almond, woody Undecanal Woody, rancid, soapy, nutty Methyl dodecanoate Not aroma active at 100 ppm concentr ation, and not sniffed in coconut water Octanoic acid Not aroma active at 10 ppm c oncentration, and not sniffed in coconut water

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93 butterscotch,spicy spicy,rose chemical,spicy burnt unpleasant,rancid, nutty rancid,dirty rubber, smoke wood,green green,floral medicinal,earth alcohol,sweet sweet,fruity mushroom,dirt sweet,fruity boiled potato nutty,rancid sweet, green,fruity charcoal,burnt,sweet soapy,fatty fruity,green unpleasant, rancid oil90011001300150017001900210023002500 Linear Retention Index (Wax)OLFACTORY RESPONSE DPCD HEAT Figure 4-17. Comparison of aromagrams of DPCD (25oC, 34.5 MPa, 13% CO2, 6 min) and heat (74oC, 15 s) treated carbonated co conut water beverages obtained from olfactory port responses (2 weeks storage at 4oC).

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94 CHAPTER 5 CONCLUSIONS This study involved the formulation of a coconut water beverage, cold pasteurization of this beve rage with dense phase CO2 (DPCD) technol ogy, evaluation of physical, chemical, microbial and sensory qua lity of DPCD pasteu rized coconut water compared to fresh and heat pasteurized sa mples, and optimization of DPCD treatment conditions for microbial reduction in coconut water. By considering regulatory and sensory as pects, coconut water needed to be acidified, sweetened and carbona ted. It was acidified with malic acid to a pH around 4.30, sweetened by Splenda (McNeil-PPC, Fort Washington, PA) at a level of 0.7% (w/w), having a oBrix of 6.0, and carbonated at 4oC and 184 KPa pressure. The first objective was to quantify microb ial reduction in coconut water as a function of treatment conditions. The res ponse surface methodology (RSM) analysis of microbial reduction data showed that pressu re did not have a significant effect in microbial reduction and the microbial re duction was predicted as a function of temperature and CO2 level by the quadratic equation: log microbial reduction = 5.381 + 0.124*Temp + 0.284*CO2 – 0.355*Temp2 0.423*CO2*Temp + 0.05*CO2 2 (coefficients were calculate d for coded values of CO2 level (CO2) and temperature (Temp)). The response surface did not give an optimum point where the (log reduction)/ (Temp)= 0 and (log reduction/ (CO2)=0 gives the highest microbial reduction. The response surface plot suggest ed higher microbial reductions at mid-

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95 temperatures and higher CO2 levels. Therefore, the optimum conditions of DPCD treatment for microbial reduction were determined as 25oC, 34.5 MPa and 13% CO2 (g CO2/100 g juice) with a 6 min treatment time which causes 5.77 log reduction in total aerobic bacterial count. The second objective was to evaluate physic al, chemical and microbial quality of DPCD treated coconut water during storage. The quality attri butes such as pH, oBrix, titratable acidity, color, aerobi c bacteria and yeast counts fo r DPCD treated coconut water were measured during 9 weeks of refrigerated storage and compared to those of untreated control and heat pasteurized samples. Aer obic bacteria and yeast counts for untreated coconut water increased significantly at the end of 9 weeks, and the aerobic bacteria count reached above 105cfu/mL, became cloudy, and developed off odors indicating end of shelf-life. On the other ha nd, the aerobic bacteria counts and yeast counts for DPCD and heat treated coconut water decreased significantly at the end of 9 weeks. Carbonation process was shown to be a possible caus e for contamination in DPCD and heat pasteurized samples. The pH and oBrix of all samples stayed around 4.20 and 6.0, respectively, throughout storage. Titratable acidity of DPCD treated samples was significantly higher than fresh and heat pasteurized samples, possibly because of the dissolved CO2 remaining in coconut water from DP CD treatment. All samples eventually turned pink during refrigerate d storage, independent of the type of treatment. The preliminary studies on pinking suggested th at heating and aeration might accelerate pinking. Further studies are needed to elaborate the cause of pinking. The third objective was to compare untre ated control, DPCD and heat treated coconut water by sensory evaluation. Untr ained panelists evaluated coconut water

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96 samples at weeks 0, 2, 3, 5 and 9 for overall lik eability, taste, arom a, off flavor and purchase intent. DPCD treated and fresh co conut water samples were liked similarly whereas heat pasteurized coconut water was significantly less like d at the beginning of storage. DPCD and heat pasteurized sample s were not significantly different for aroma difference from control scores. On the other ha nd, taste difference from control scores for DPCD and heat pasteurized samples were significantly different initially and became similar beginning from 2nd week. Heat pasteurized sample s had significantly higher off flavor scores than DPCD treated sa mples during the first two weeks. The fourth objective was to identify fl avor compounds in coconut water and compare flavor profiles of DP CD and heat treated coconut water. Flavor compounds such as esters (ethyl butanoate, ethyl oc tanoate), aldehydes (o ctanal, undecanal, 2,6nonadienal) and others were identified in young green coconut water. The aroma profiles of DPCD and heat treated coconut water be verages showed that heat treated coconut water had more aroma active compounds than the DPCD treated coconut water. These were probably created by thermal decom position during heat treatment and were described as unpleasant, fatty, green and burnt aroma by sniffers. This study showed that DPCD treatment extended shelf-life of coconut water beverage that was acidified, sweetened and carbonated, and th e sensory quality of DPCD treated coconut water was better than heat pasteurized coconut wate r during the first two weeks. As a recommendation for future studies, it would be useful to investigate the mechanisms and causes of pinking in coconut water so that the means of prevention could be elaborated. Further studies on sensory evaluation with trained panelists are

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97 recommended for better description of th e aroma differences between different treatments. Further studies on instrumental an alysis of flavors are also recommended for more detailed identification of the aroma active compounds.

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98 APPENDIX A RESULTS OF PRELIMINARY TESTS WITH COCONUT WATER Table A-1. Initial aerobic plat e count (APC) and yeast and mo ld (YM) counts for coconut water from eight immature green coconuts Coconut # 1 2 3 4 5 6 7 8 APC 0-4 51-63 TNTCaTNTC TNTC TNTC 170184 182179 139137 181197 YMc NGb NG NG NG NG NG NG NG a too numerous to count ; b no growth; c numbers in red color indicate mold growth (if there is any) Table A-2. Day 9 aerobic plate count (APC) a nd yeast and mold (YM) counts for coconut water from selected coconuts of eight immature green coconuts a numbers in red color indicate mold growth (if there is any); btoo numerous to count Dilution # 10-1 10-2 10-3 10-4 10-5 10-6 APC 74-60 69-65 7 0-2 1-1 0-0 0-0 0-0 0-1 0-0 0-1 Coconut # 1 YMa 9-8 2-10 1-0 3-3 0-0 0-0 0-0 0-0 0-0 0-0 APC TNTCb TNTC TNTC TNTC 135TNTC 46-56 58-59 1-3 5-0 Coconut # 3 YM 146-164 172-173 39-38 28-21 2-1 1-2 0-0 0-0 0-0 0-0 APC TNTC TNTC 186-175 245-175 20-27 27-47 4-5 2-1 0-0 0-0 0-0 0-0 Coconut # 5 YM 69-91 4-0 12-0 3-2 0-0 0-0 0-0 0-0 0-0

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99 (Day 0) (Day 9) Figure A-1. Pictures of coconut water from ei ght immature green coconuts at day 0 (left) and day 9 (right) Table A-3. Preliminary pinking te st 1: Visual observation of the color of coconut water after different treatments during storage at 4oC in glass tubes Observation Time: Control (1)** Frozen/ Thawed (2)** Heated (open air) (85oC; 5 min) (3)** Heated (closed) (85oC; 5 min) (4)** N2 bubbled for 15 min (5)** T1 T2 T3 T1 T2T3T1 T2 T3T1T2 T3 T1T2T3 Day 0 C C C C C C C C C C C C C C C Day 4 C C C C C C P P P C C C P P C Day 7 * Day 9 P C C P C C P P P P C C P P C Day 12 P C C P C C P P P P C C P P C *On day 7, marked tubes were aerated for 15 min; **numbers in parentheses imply treatment numbers referring to the text; T1,T2,T3 indicates three tube replicates; C: Clear color; P: Pink color. Table A-4. Preliminary pinking te st 2: Visual observation of the color of coconut water after different treatments during storage at 4oC in opaque plastic cups Observation Time: Control (1)* Ascorbic acid added (100ppm) (2)* Potassium metabisulfite added(40ppm) (3)* pH=4.0 (by 0.1N HCl) (4)* pH=3.0 (by 0.1N HCl) (5)* C1 C2 C1 C2 C1 C2 C1 C2 C1 C2 Day 0 to 11 C C C C C C C C C C Day 12 P P C C C C C C C C 3 months P P C C C C P P P P C1: Cup 1; C2: Cup 2; C: Cl ear color; P: Pink color; *numbers in parentheses imply treatment numbers referring to the text

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100 Table A-5. Preliminary pinking te st 3: Visual color observati on of untreated, heat treated or aerated coconut water during storage in glass tubes at 4oC. Observation Time: Control (1)* Heated at 85oC for 5 min (2)* Boiled for 5 min (3)* Aerated for 15 min (4)* T1 T2 T3 T1 T2T3 T1 T2 T3 T1 T2 T3 Day 0 C C C C C C C C C C C C Day 6 C C C P C C P P P P P C Day 10 C C C P P P P P P P P P *Numbers in parentheses imply treatment nu mbers referring to the text; T1, T2, T3 indicates three tube replicates; C: Clear color; P: Pink color Table A-6. The pH, oBrix and ingredients of commerc ially available coconut water beverages Brand Name pH oBrixIngredients (from Label) Conchita 5.15 9.5 Immature coconut juice, sugar, coconut meat, potassium metabisulfite La Fe 5.16 8.9 80% immature coconu t juice, water, coconut pulp, sugar, citric acid, sodium metabisulfite Coco Rico 4.12 10.8 Carbonated water, HFCS, coconut extract, sodium benzoate Goya 5.07 8.9 Immature coconut juice, sugar, coconut pulp, water,citric acid, potassium metabisulfite KeroCoco 4.89 5.6 Natural coconut wate r, preservative INS 223 (Sodium metabisulfite) Grace 4.85 5.9 Coconut water

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101 Figure A-2. Pictures showing the steps of extraction of coconut water from coconuts

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102 APPENDIX B BOX-BEHNKEN EXPERIMENTAL DESIGN, DATA AND ANALYSIS Table B-1. The average initial and final aerobi c plate counts (APC) standard deviations at 15 experimental runs from 3-fact or, 3-level Box-Behnken experimental design Temperature (oC) Pressure (MPa) CO2 level (g CO2 / 100 g juice) Initial APC (cfu/mL)* Final APC (cfu/mL)* 20 13.8 10 (1.26E+07) (5.06E+05) 15117 40 13.8 10 (1.83E+07) (9.95E+05) 17010 20 34.5 10 (1.26E+07) (5.06E+05) 16324 40 34.5 10 (1.83E+07) (9.95E+05) 4510 20 24.1 7 (1.26E+07) (5.06E+05) 43016 40 24.1 7 (1.83E+07) (9.95E+05) 737 20 24.1 13 (1.26E+07) (5.06E+05) 4812 40 24.1 13 (1.83E+07) (9.95E+05) 40393 30 13.8 7 (4.80E+07) (8.16E+06) 23151 30 34.5 7 (4.80E+07) (8.16E+06) 933115 30 13.8 13 (4.80E+07) (8.16E+06) 6219 30 34.5 13 (4.80E+07) (8.16E+06) 324 30 24.1 10 (4.80E+07) (8.16E+06) 1274 30 24.1 10 (1.26E+07) (5.06E+05) 13023 30 24.1 10 (1.83E+07) (9.95E+05) 11113 *Averages of the plates with APC counts lower than 200 colony forming units (cfu’s)

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103 Table B-2. SAS software code used for the response surface methodology (RSM) analysis of 15 experimental runs de termined by Box-Behnken experimental design data coconut; input RUN X1 X2 X3 TEMP PRESSURE PCTCO2 LOGRED; datalines ; 1 -1 -1 0 20 2000 10 4.924279286 2 1 -1 0 40 2000 10 5.032002168 3 -1 1 0 20 5000 10 4.896250562 4 1 1 0 40 5000 10 5.609238576 5 -1 0 -1 20 3500 7 4.46690209 6 1 0 -1 40 3500 7 5.39912823 7 -1 0 1 20 3500 13 5.419129308 8 1 0 1 40 3500 13 4.660391098 9 0 -1 -1 30 2000 7 5.297425871 10 0 1 -1 30 5000 7 4.712758289 11 0 -1 1 30 2000 13 5.895911402 12 0 1 1 30 5000 13 6.176091259 13 0 0 0 30 3500 10 5.577437516 14 0 0 0 30 3500 10 4.986427193 15 0 0 0 30 3500 10 5.221058405 ; proc print data =coconut; run ; proc rsreg data =coconut; model logred = x1 x2 x3; run ; Table B-3. SAS software output of the re sponse surface methodology (RSM) regression analysis of 15 experimental-run data determined by Box-Behnken experimental design including variables X1 (coded variable for Temperature), X2 (coded variable for Pressure) and X3 (coded variable for %CO2 level) The RSREG Procedure Coding Coefficients for the Independent Variables Factor Subtracted off Divided by X1 0 1.000000 X2 0 1.000000 X3 0 1.000000 Response Surface for Variable LOGRED Response Mean 5.218295 Root MSE 0.386683 R-Square 0.7625 Coefficient of Variation 7.4101 Type I Sum Regression DF of Squares R-Square F Value Pr > F Linear 3 0.778169 0.2472 1.73 0.2752 Quadratic 3 0.628499 0.1997 1.40 0.3452 Crossproduct 3 0.993417 0.3156 2.21 0.2045 Total Model 9 2.400085 0.7625 1.78 0.2716 Sum of Residual DF Squares Mean Square Total Error 5 0.747618 0.149524

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104 Table B-3 Continued Parameter Parameter DF Estimate Standard Error t Value Pr > |t| estimate Intercept 1 5.261641 0.223251 23.57 <.0001 5.261641 X1 1 0.124275 0.136713 0.91 0.4050 0.124275 X2 1 0.030590 0.136713 0.22 0.8318 0.030590 X3 1 0.284414 0.136713 2.08 0.0920 0.284414 X1*X1 1 -0.340179 0.201236 -1.69 0.1517 -0.340179 X2*X1 1 0.151316 0.193341 0.78 0.4693 0.151316 X2*X2 1 0.193980 0.201236 0.96 0.3793 0.193980 X3*X1 1 -0.422741 0.193341 -2.19 0.0805 -0.422741 X3*X2 1 0.216212 0.193341 1.12 0.3143 0.216212 X3*X3 1 0.064925 0.201236 0.32 0.7600 0.064925 Canonical Analysis of Response Surface Based on Coded Data Critical Value Factor Coded Uncoded X1 0.517443 0.517443 X2 0.016346 0.016346 X3 -0.532945 -0.532945 Predicted value at stationary point: 5.218255 Eigenvectors Eigenvalues X1 X2 X3 0.258999 -0.093844 0.822796 0.560535 0.109837 0.430314 0.541235 -0.722423 -0.450109 0.897788 -0.173411 0.404852 Stationary point is a saddle point. Table B-4. SAS software output of the re sponse surface methodology (RSM) regression analysis of 15 experimental-run data determined by Box-Behnken experimental design including variables X1 (coded variable for Temperature) and X3 (coded variable for %CO2 level) The RSREG Procedure Coding Coefficients for the Independent Variables Factor Subtracted off Divided by X1 0 1.000000 X3 0 1.000000 Response Surface for Variable LOGRED Response Mean 5.218295 Root MSE 0.360958 R-Square 0.6275 Coefficient of Variation 6.9172 Type I Sum Regression DF of Squares R-Square F Value Pr > F Linear 2 0.770683 0.2448 2.96 0.1030 Quadratic 2 0.489564 0.1555 1.88 0.2080 Crossproduct 1 0.714840 0.2271 5.49 0.0439 Total Model 5 1.975087 0.6275 3.03 0.0707 Sum of Residual DF Squares Mean Square Total Error 9 1.172617 0.130291

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105 Table B-4 Continued Parameter DF Estimate Standard Error t Value Pr > |t| parameter Intercept 1 5.381014 0.173399 31.03 <.0001 5.381014 X1 1 0.124275 0.127618 0.97 0.3556 0.124275 X3 1 0.284414 0.127618 2.23 0.0528 0.284414 X1*X1 1 -0.355100 0.187292 -1.90 0.0905 -0.355100 X3*X1 1 -0.422741 0.180479 -2.34 0.0439 -0.422741 X3*X3 1 0.050004 0.187292 0.27 0.7955 0.050004 Sum of Factor DF Squares Mean Square F Value Pr > F X1 3 1.306751 0.435584 3.34 0.0696 X3 3 1.371256 0.457085 3.51 0.0625 The RSREG Procedure Canonical Analysis of Response Surface Based on Coded Data Critical Value Factor Coded Uncoded X1 0.531209 0.531209 X3 -0.598452 -0.598452 Predicted value at stationary point: 5.328918 Eigenvectors Eigenvalues X1 X3 0.140206 -0.392502 0.919751 -0.445302 0.919751 0.392502 Stationary point is a saddle point.

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106 APPENDIX C GC/O AND GC/MS FLAVOR ANA LYSIS DATA AND RESULTS Table C-1. Excel output of alka ne standards’ linear retentio n index (LRI) calculations in GC/O with a Carbowax column Standard alkane Retention time (min) LRI literature LRI calculated by formula C10 8.57 1000 1008 C11 10.05 1100 1089 C12 12.14 1200 1198 C13 14.17 1300 1302 C14 16.19 1400 1405 C15 18.03 1500 1502 C16 19.8 1600 1599 C17 21.49 1700 1697 C18 23.09 1800 1797 C19 24.61 1900 1899 C20 26.04 2000 2002 The formula relating alkane standards' LRI's to retention times in Carbowax column y = 0.0563x3 2.3784x2 + 84.352x + 424.06 R2 = 0.9998 0 500 1000 1500 2000 2500 051015202530 Retention time (min)LRI Figure C-1.Plot of the formula relating th e LRI’s to the retention times for aroma compounds in GC/O with a Carbowax column

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107 Table C-2. Excel output of alka ne standards’ linear retentio n index (LRI) calculations in GC/O with a DB-5 column Standard alkane Retention time(min)LRI literature LRI's calculated by the formula C7 4.78 700 706 C8 6.68 800 794 C9 8.9 900 895 C10 11.27 1000 1002 C11 13.44 1100 1102 C12 15.57 1200 1203 C13 17.57 1300 1302 C14 19.47 1400 1400 C15 21.26 1500 1499 C16 22.96 1600 1598 C17 24.57 1700 1698 C18 26.09 1800 1798 C19 27.54 1900 1900 C20 28.91 2000 2002 The formula relating alkane standards' LRI's to retention times in DB-5 column y = 0.0275x3 0.782x2 + 52.633x + 469 R2 = 0.9999 0 500 1000 1500 2000 2500 05101520253035 Retention time (min)LRI Figure C-2. Plot of the formula relating th e LRI’s to the retention times for aroma compounds in GC/O with a DB-5 column

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108 Figure C-3. An example of GC/MS peak iden tification using National Institute of Scie nce and Technology (NIST) library database Step 1. Integration of peaks on GC/MS chromatogram (Numbers above each peak represents retenti on time in minutes, peak height and peak area from top to bottom, respectively) Coconut water FRESH2 SPME 09-Mar-2006 16:13:57, Coconut water + 2.264.266.268.2610.2612.2614.2616.2618.2620.2622.2624.2626.2628.2630.2632.2634.2636.2638.26 Time 0 100 % coconutl009 Scan EI+ TIC 4.11e8 Area, Height 7.02;16671743.0;377321792 3.31 189010224.0 163870080 17.86 13324507.0 228001104 7.25 3838040.0 16505004 15.04 3802617.8 41520276 11.59 469640.1 10770774 9.28 383863.2 5514265 13.71 663806.3 8763753 16.09 878296.7 7956184 24.38 4471119.5 113618136 19.25 6006361.0 78180440 21.18 4326834.5 69036632 21.89 592941.9 14641322 25.37 2674308.0 66172440 39.27 837940.1 7072264 35.64 1542995.3 12132749 31.59 906248.4 15858109 30.38 391579.2 6689500 26.83 788452.1 15243993 34.45 554234.7 6726085 Fresh untreated coconut water (45 min of extraction with Solid Ph ase Microextraction (SPME) at 40-45oC)

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109(Figure C-3 Continued) Step 2. Identification of a selected peak (the peak with Rt= 17.86 min) using Nati onal Institute of Science and Technology (NIS T) library database

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110 Figure C-4. GC/MS chromatograms of the four mixed groups of standard chemicals that were run in GC/MS for a possible confirmation (The list of standards in each mixed group are given in C-5). Mixed stardard group 1 02-Feb-2006 09:03:40, Mixed stardar + 10.0012.0014.0016.0018.0020.0022.0024.0026.0028.0030.0032.0034.0036.0038.00 Time 0 100 % 0 100 % 0 100 % 0 100 % cw01 Scan EI+ TIC 1.96e8 30.81 21.18 17.87 8.69 30.73 29.79 27.17 26.60 31.57 35.30 34.45 32.38 32.88 36.72 37.50 cw02 Scan EI+ TIC 1.21e8 29.80 11.62 8.76 9.50 28.44 17.23 26.39 20.64 21.11 24.83 29.02 33.58 31.88 30.84 38.05 34.50 35.01 cw03 Scan EI+ TIC 1.25e8 24.41 21.90 15.09 8.58 9.54 28.33 26.86 35.67 34.49 32.99 31.74 30.76 29.99 cw04 Scan EI+ TIC 1.57e8 30.57 18.80 8.66 29.98 28.48 26.85 31.24 35.59 34.47 32.68 Grou p 1 Standards ( Standard-CW-1 ) Grou p 2 Standards ( Standard-CW-2 ) Grou p 3 Standards ( Standard-CW-3 ) Grou p 4 S t andards ( Standard-CW-4 )

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111 Table C-3. Retention times (RT), linear rete ntion indices (Wax LR I) and GC/MS degree of match values of four mixed group of st andard chemicals that were run in GC/MS for possible confirmation Standard CW-1 (Group 1 standards) RT (min) Wax LRI GC/MS identified/degree of match Ethyl caprylate (ethyl octanoate) 17.87 1451 890 Undecanal 21.18 1630 947 -Decalactone 30.81 2248 900 Standard CW-2 (Group 2 standards) 1-Butanol 11.62 1154 906 Nonanal 17.23 1419 890 -Nonalactone 28.44 2084 883 Nonanoic acid 29.80 2178 934 Standard CW-3 (Group 3 standards) Octanal 15.09 1313 883 Nonanol 21.90 1671 904 Methyl laurate (Methyl dodecanoate) 24.41 1820 905 Octanoic Acid 28.33 2077 912 Standard CW-4 (Group 4 standards) 2-Ethyl-1-hexanol 18.80 1500 929 Carvacrol 30.57 2232 905 Decanoic acid 31.24 2278 889 GC/MS running conditions: Mass Spec Method Wax column: 40min run (solvent delay of 8 min) GC Method Wax column:40min run (40oC-240oC at ramp rate of 7oC9.5min hold) Injector temp 240C Injection volume 0.5ul

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112 Figure C-5. Sample GC/MS chromatograms obtained by running fresh coconut water samples. *SPME: Solid phase microextraction Coconut water FRESH2 SPME 09-Mar-2006 16:13:57, Coconut water + 2.264.266.268.2610.2612.2614.2616.2618.2620.2622.2624.2626.2628.2630.2632.2634.2636.2638.26 Time 0 100 % coconutl009 Scan EI+ TIC 4.11e8 Area, Height 7.02;16671743.0;377321792 3.31 189010224.0 163870080 17.86 13324507.0 228001104 7.25 3838040.0 16505004 15.04 3802617.8 41520276 11.59 469640.1 10770774 9.28 383863.2 5514265 13.71 663806.3 8763753 16.09 878296.7 7956184 24.38 4471119.5 113618136 19.25 6006361.0 78180440 21.18 4326834.5 69036632 21.89 592941.9 14641322 25.37 2674308.0 66172440 39.27 837940.1 7072264 35.64 1542995.3 12132749 31.59 906248.4 15858109 30.38 391579.2 6689500 26.83 788452.1 15243993 34.45 554234.7 6726085 Coconut water fresh SPME 09-Mar-2006 13:13:45, Coconut water + 3.045.047.049.0411.0413.0415.0417.0419.0421.0423.0425.0427.0429.0431.0433.0435.0437.0439.04 Time 0 100 % coconutl007 Scan EI+ TIC 5.11e8 Area, Height 7.02;20764292.0;473780576 3.14 15403422.0 313220224 3.42 219865360.0 148331040 17.87 12485538.0 155771744 7.26 4580465.0 19347430 17.22 2307376.0 20214800 11.61 571356.5 16191101 13.68 675825.4 12907235 24.39 5286358.0 137471264 19.27 3607212.5 39481708 21.20 993128.6 16562228 25.37 2645771.0 62618372 37.82 1997267.9 7577922 35.65 627627.2 8408889 31.59 641612.4 12703050 29.58 1287689.1 23587152 26.84 433681.3 7352777 34.45 505240.8 5594766 38.88 995069.4 5974896 Fresh untreated coconut water sample 1 (45 min extraction with SPME* at 40-45oC) Fresh untreated coconut water sample 2 (45 min extraction with SPME* at 40-45oC)

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113 Table C-4. Flavor compounds identified in co conut water through GC /O runs: Retention times, calculated Linear Retention Indices (LRI’s) and aroma descriptors given by sniffers in GC/O runs with DB-5 and Carbowax columns DB-5 Column Carbowax Column Retention time (min) Linear Retention Index(LRI) Aroma descriptors by sniffers Retention time (min) Linear Retention Index(LRI) Aroma descriptor by sniffers 6.77 797 Fruity,apple,sweet 9.43 1055 Sweet,fruity, candy,flower 7.82 845 Sweet,fruity,candy 9.74 1072 Sweet, candy 10.81 980 Mushroom,medicinal, earth 13.34 1260 Green, fruity 11.23 999 Fruity, sweet 14.83 1336 Mushroom, dirt 13.8 1117 Medicinal, chemical,pencil 17.17 1456 Sweet, coconut,candy 14.57 1152 Rancid, green, dirt 19.37 1575 Almond,green, rancid,wood, pencil 15.46 1196 Sweet,candy, rose,flower 24.25 1875 Old leather 15.87 1216 Green, fruity 16.95 1269 Pencil, wood 18.48 1347 Old leather (The same colored letters co rresponds to the literature ma tched compounds based on the LRI’s at DB-5 and Carbowax columns : Ethyl butanoate 1-octene-3-one ethyl octanoate 2,6-nonadienal ).

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114 Table C-5. Peak areas of the sniffed compounds (olfactory port responses) and the aroma descriptors given by sniffe rs for DPCD treated (25oC, 34.5 MPa, 13% CO2, 6 min) and carbonated coconut water samples in GC/O with Carbowax column DPCD treated and Carbonated Coconut Water RTa (min) LRIb AROMA DESCRIPTOR DPCD1c DPCD2DPCD3 DPCD4 DPCD AVEd 2.05 951 ALCOHOL,SWEET, FRUTY 0 339374 624020 0 240849 3.05 1047 SWEET,FRUITY, ROSE 792069 206632 0 985164 495966 6.2 1250 0 0 0 0 0 7.45 1315 MUSHROOM,NUTTY, DIRT,RANCID 672647 413901 670617 694377 612886 8.87 1387 ALCOHOL,MEDICINAL, EARTH 708085 234793 0 0 235720 9.96 1443 SWEET,FRUITY, COTTONCANDY 971195 0 711411 0 420652 10.57 1476 BOILED POTATO 750623 0 735610 539867 506525 11.32 1516 GREEN,CHEMICAL, MEDICINAL,FLORAL 929356 0 803886 723693 614234 11.43 1522 BURNT, DIRTY, RANCID OIL,WOOD 882670 391418 671714 469414 603804 11.99 1553 PENCIL,WOOD, ALMOND,GREEN,NUTTY 792753 409736 774259 793723 692618 12.23 1567 0 0 0 0 0 12.9 1605 RUBBER,SMOKEY, BURNT,ROSE 339245 0 351643 513251 301035 13.36 1631 0 0 0 0 0 14.94 1724 DIRT,NUTTY,RANCID, BOILED NUT,EARTH 956838 0 437109 422308 454064 16.76 1833 0 0 0 0 0 17.4 1873 0 0 0 0 19.78 2025 ALCOHOL, BURNT 0 538833 0 421482 240079 21.54 2146 CHEMICAL,GLUE, MEDICINAL,SPICY 869299 0 834402 0 425925 22.3 2200 ROSE,OLD SPICE 294379 0 265553 0 139983 22.52 2217 BUTTERSCOTCH,SPICY, BURNT CARAMEL 1046072 575606 983842 698169 825922 a Retention time; bLinear retention index; c Peak areas of each of f our replicates of DPCD treated samples; d Average peak areas of four replicates

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115 Table C-6. Peak areas of the sniffed compounds (olfactory port responses) and the aroma descriptors given by sniffe rs for heat treated (74oC, 15 s) and carbonated coconut water in GC/O with Carbowax column Heat treated and Carbonated Coconut Water RTa LRIb AROMA DESCRIPTOR HEAT1c HEAT2 HEAT3 HEAT4 HEAT AVEd 2.05 951 ALCOHOL,SWEET, FRUTY 585587 467181 799533 0 463075 3.05 1047 SWEET,FRUITY,ROSE 658491 338353 720327 0 429293 6.2 1250 FRUITY,GREEN 797552 0 718493 0 379011 7.45 1315 MUSHROOM,NUTTY, DIRT, RANCID 863051 441346 686111 782807 693329 8.87 1387 ALCOHOL, MEDICINAL, EARTH 835683 0 535560 640610 502963 9.96 1443 SWEET, FRUITY, COTTONCANDY 705454 0 840408 327752 468404 10.57 1476 BOILED POTATO 731560 449456 1E+06 414806 686963 11.32 1516 GREEN,CHEMICAL, MEDICINAL, FLORAL 959563 0 624267 613652 549371 11.43 1522 WOODY, PENCIL 446908 731984 747628 0 481630 11.99 1553 PAPER, WOOD, ALMOND,GREEN, FLORAL 538844 979147 1E+06 697627 835177 12.23 1567 SWEET,FLORAL, FRUITY,CANDY, GREEN,BANANA 553982 587634 545965 517102 551171 12.9 1605 0 0 0 0 0 13.36 1631 WOODY,DIRT, RANCID,SOAPY, ALMOND, NUTTY 871909 566836 725192 610807 693686 14.94 1724 DIRT,NUTTY,RANCID, BOILED NUT,EARTH 666693 836192 778033 693411 743582 16.76 1833 NUTTY,RANCID,BAD, RANCID OIL 639297 739951 694090 0 518335 17.4 1873 SOAPY,FAT,ROSY, FLORAL 934488 0 698580 503850 534230 19.78 2025 METAL,COOKED, RANCID, BURNT 429039 0 485639 721339 409004 21.54 2146 0 0 0 0 0 22.3 2200 SPICY,FLORAL, SOAP 628453 0 792350 0 355201 22.52 2217 ROSE,OLD SPICE 688938 0 563572 0 313128 27.00 2557 BUTTERSCOTCH, SPICY, BURNT 970501 478469 1E+06 0 663632 a Retention time; bLinear retention index; c Peak areas of each of f our replicates of heat treated samples; d Average peak areas of four replicates

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116 GC/MSCHROMATOGRAMS Carbonated CW Heat 2 22-Mar-2006 18:14:06, Carbonated CW + 2.264.266.268.2610.2612.2614.2616.2618.2620.2622.2624.2626.2628.2630.2632.2634.2636.2638.26 Time 0 100 % 0 100 % 0 100 % 0 100 % coconutl0014 Scan EI+ TIC 3.51e8 7.01 2.62 3.14 3.45 5.16 26.23 19.19 17.15 16.53 13.47 25.65 23.38 21.89 31.58 coconutl0013 Scan EI+ TIC 4.30e8 7.01 2.62 3.14 3.37 25.38 16.01 12.92 19.19 17.14 24.98 26.24 31.60 coconutl0012 Scan EI+ TIC 5.26e8 7.02 3.14 2.63 3.67 25.37 17.87 13.07 19.27 coconutl0011 Scan EI+ TIC 5.90e8 7.00 3.14 2.62 3.32 17.86 17.21 25.65 19.26 29.59 Figure C-6. GC/MS chromatogr ams of DPCD treated (c oconut0011 and coconut0013) and heat treated (coconut0013 and co conut0014) coconut water beverages (carbonated) (Samples were extracted fo r 45 min by solid phase microe xtraction (SPME) at 40-45oC). Heattreatedreplicate1 Heattreated replicate2 DPCDtreatedreplicate1 DPCDtreatedreplicate2

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117 APPENDIX D STORAGE STUDY: MICROBIAL, CHEMIC AL AND PHYSICAL QUALITY DATA Table D-1. Total aerobic plat e counts (APC) of untreate d, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC) Storage time (Week) Control (Untreated)* DPCD treated* Heat pasteurized* 0 1410 46 1130 43 76.33 2 1 485 28 260 27 7.25 2 2 278 19 272 23 3.50 1 3 303 28 1060 25 3.75 0 4 175 18 103 6 4.50 0 5 242 10 540 28 3.0 1 6 298 22 98 18 2.0 1 9 107000 7150 130 16 1.0 1 *Mean of number of colony forming units (c fu’s) on petrifilms with counts less than 200 Std Error (number of replicate pe trifilms at each dilution is four) Table D-2. Excel outputs of one -tail t tests conducted for comparison of mean aerobic plate counts (APC) and yeast and mold (YM) counts for week 0 and week 9 samples. Control (untreated) coconut water samp les’ APC: week0 vs week9 comparison t-Test: Two-Sample Assuming Unequal Variances Variable 1a Variable 2b Mean 1407.5 106500 Variance 8425 2.04E+08 Observations 4 4 Hypothesized Mean Difference 0 df 3 t Stat -14.70 P(T<=t) one-tail 0.0003 t Critical one-tail 2.35 P(T<=t) two-tail 0.0007 t Critical two-tail 3.18 t-Test: Two-Sample Assuming Unequal Variances

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118 Table D-2 Continued DPCD treated coconut water samples’ APC: week0 vs week9 comparison Variable 1a Variable 2b Mean 1125 129.625 Variance 7500 1991.411 Observations 4 8 Hypothesized Mean Difference 0 df 4 t Stat 21.60 P(T<=t) one-tail 1.36E-05 t Critical one-tail 2.13 P(T<=t) two-tail 2.72E-05 t Critical two-tail 2.78 Heat treated coconut water samples’ APC: week0 vs week9 comparison t-Test: Two-Sample Assuming Unequal Variances Variable 1a Variable 2b Mean 76.33 1 Variance 16.33 1.33 Observations 3 4 Hypothesized Mean Difference 0 df 2 t Stat 31.34 P(T<=t) one-tail 0.0005 t Critical one-tail 2.92 P(T<=t) two-tail 0.001 t Critical two-tail 4.30 Control (untreated) coconut water samp les’ YM: week0 vs week9 comparison t-Test: Two-Sample Assuming Unequal Variances Variable 1a Variable 2b Mean 14.67 1.25 Variance 6.33 0.25 Observations 3 4 Hypothesized Mean Difference 0 df 2 t Stat 9.10 P(T<=t) one-tail 0.006 t Critical one-tail 2.92 P(T<=t) two-tail 0.012 t Critical two-tail 4.30

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119 Table D-2 Continued DPCD treated coconut water samples’ YM: week0 vs week9 comparison t-Test: Two-Sample Assuming Unequal Variances Variable 1a Variable 2b Mean 4.666667 0 Variance 4.333333 0 Observations 3 4 Hypothesized Mean Difference 0 df 2 t Stat 3.882901 P(T<=t) one-tail 0.030191 t Critical one-tail 2.919987 P(T<=t) two-tail 0.060382 t Critical two-tail 4.302656 Heat treated coconut water sample s’ YM: week0 vs week9 comparison t-Test: Two-Sample Assuming Unequal Variances Variable 1a Variable 2b Mean 1.333333 0 Variance 0.333333 0 Observations 3 4 Hypothesized Mean Difference 0 df 2 t Stat 4 P(T<=t) one-tail 0.028595 t Critical one-tail 2.919987 P(T<=t) two-tail 0.057191 t Critical two-tail 4.302656 a(Variable 1) corresponds to Week 0; b(Variable 2) corresponds to Week 9

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120 Table D-3. Aerobic plate counts (APC) and yeast and mold (YM) counts of sterile distilled water before and after carbonation with the Zalhm carbonator APC counts bYM counts* Dilution: 0 10-1 10-2 0 10-1 Initial counts: 0-0 0-0 0-0 0-0 0-0 0-0 0-0 0 0-0 0-0 Final counts: aTNTCTNTC 114-124 122-141 -----231-230 186-179 26-20 17-24 a too numerous to count ; b numbers in red color indicate mo ld growth (if there is any) Numbers in red color indicate mo ld growth (if there is any) Table D-4. Yeast and mold (YM) count s of untreated, heat treated (74oC, 15 s) and DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) coconut water beverages during storage Storage time (Week) Control (Untreated)** DPCD treated** Heat pasteurized** 0 14.67 1 4.67 1 1.33 0 1 6.00 2 0.25 0 0 0 2 8.00 1 0.25 0 0 0 3 18.75 1 2.75 0 0.25 0 4 8.25 0 2.75 1 0.50 0 5 10.00 2 1.25 1 0 0 6 2.50 0 0 0 0 0 9 1.25 0.25 0 0 0 0 (Numbers in red color indicat e mold growth (if there is any)) ; **Mean of number of colony forming units (cfu’s) on petr ifilms with counts less than 200 Std Error (number of replicate petrifilms at each dilution is four) Table D-5. The pH of untreated, DPCD treat ed and heat pasteurized samples during storage Storage time (Week) Control (Untreated)* aDPCD treated* bHeat pasteurized* 0 4.18 3.44E-08 4.18 0 4.18 0 2 4.17 0 4.16 0 4.16 0 3 4.19 0 4.22 0 4.24 0 5 4.20 0 4.19 0 4.19 0 9 4.24 0 4.20 0 4.22 0 *Mean of three replicate measurements Std Error; Treatment conditions: a 34.5 MPa, 25oC, 13% CO2 ; b74oC, 15 s

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121 Table D-6. SAS software output of analysis of variance (ANOVA) for the pH data of different treatments from the storage study The ANOVA Procedure Dependent Variable: pH Sum of Source DF Squares Mean Square F Value Pr > F Model 14 0.03118667 0.00222762 38.55 <.0001 Error 30 0.00173333 0.00005778 Corrected Total 44 0.03292000 R-Square Coeff Var Root MSE pH Mean 0.947347 0.181182 0.007601 4.195333 Source DF Anova SS Mean Square F Value Pr > F treat 2 0.00069333 0.00034667 6.00 0.0064 week 4 0.02412000 0.00603000 104.37 <.0001 week*treat 8 0.00637333 0.00079667 13.79 <.0001 Duncan's Multiple Range Test for pH NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 30 Error Mean Square 0.000058 Number of Means 2 3 Critical Range .005668 .005957 Means with the same letter are not significantly different Duncan Grouping Mean N treat A 4.199333 15 heat A 4.196667 15 control B 4.190000 15 CO2 Duncan Grouping Mean N week A 4.223333 9 9 A 4.218889 9 3 B 4.192222 9 5 C 4.180000 9 0 D 4.162222 9 2 Table D-7. The oBrix of untreated, DPCD treated a nd heat pasteurized samples during storage Storage time (Week) Control (Untreated)* aDPCD treated* bHeat pasteurized* 0 5.9 0 5.9 0 5.9 0 1 6.0 0 6.0 0 6.0 0 2 6.1 0 6.0 0 6.0 0 3 6.0 0 6.1 0 6.0 0 5 6.15 0.05 6.0 0 6.1 0 9 6.1 0 6.0 0 6.0 0 *Mean of two replicate measurements Std Error; Treatment conditions: a 34.5 MPa, 25oC, 13% CO2, 6 min; b74oC, 15 s

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122 Table D-8. SAS software output of analysis of variance (ANOVA)for oBrix data of different treatments from the storage study The ANOVA Procedure Dependent Variable: Brix Sum of Source DF Squares Mean Square F Value P r > F Model 17 0.17805556 0.01047386 37.71 <.0001 Error 18 0.00500000 0.00027778 Corrected Total 35 0.18305556 R-Square Coeff Var Root MSE Brix Mean 0.972686 0.277136 0.016667 6.013889 Source DF Anova SS Mean Square F Value Pr > F treat 2 0.01388889 0.00694444 25.00 <.0001 week 5 0.11472222 0.02294444 82.60 <.0001 week*treat 10 0.04944444 0.00494444 17.80 <.0001 Duncan's Multiple Range Test for Brix NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 18 Error Mean Square 0.000278 Number of Means 2 3 Critical Range .01429 .01500 Means with the same letter are not significantly different. Duncan Grouping Mean N treat A 6.041667 12 control B 6.000000 12 CO2 B 6.000000 12 heat The ANOVA Procedure Duncan's Multiple Range Test for Brix NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 18 Error Mean Square 0.000278 Number of Means 2 3 4 5 6 Critical Range .02022 .02121 .02184 .02227 .022 59 Means with the same letter are not significantly different. Duncan Grouping Mean N week A 6.083333 6 5 B 6.033333 6 9 B 6.033333 6 2 B 6.033333 6 3 C 6.000000 6 1 D 5.900000 6 0

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123 Table D-9. Titratable acidity (as % malic acid (w/v)) of untreated, DPCD treated and heat pasteurized coconut water beverages during storage Storage time (Week) Control (Untreated)* aDPCD treated* bHeat pasteurized* 0 0.2156 0.0192 0.2738 0.0058 0.2291 0.0057 2 0.2604 0.0024 0.2674 0.0065 0.2664 0.0034 3 0.302 0.0062 0.3002 0.0016 0.2738 0.0059 5 0.2512 0.0029 0.2654 0.0001 0.258 0.0039 9 0.2656 0.0176 0.3052 0.0039 0.3033 0.0055 *Mean of three replicate measurements Std Error; Treatment conditions: a 34.5 MPa, 25oC, 13% CO2, 6 min ; b74oC, 15 s Table D-10. SAS software output of analys is of variance (ANOVA) for % titratable acidity data of different treatments from storage study The ANOVA Procedure Dependent Variable: %TA Sum of Source DF Squares Mean Square F Value Pr > F Model 14 0.02876390 0.00205456 10.81 <.0001 Error 30 0.00570096 0.00019003 Corrected Total 44 0.03446486 R-Square Coeff Var Root MSE %TA Mean 0.834586 5.121316 0.013785 0.269173 Source DF Anova SS Mean Square F Value Pr > F treat 2 0.00433421 0.00216710 11.40 0.0002 week 4 0.01831396 0.00457849 24.09 <.0001 week*treat 8 0.00611573 0.00076447 4.02 0.0024 Duncan Grouping Mean N treat A 0.282426 15 CO2 B 0.266119 15 heat B 0.258976 15 control Duncan Grouping Mean N week A 0.292022 9 3 A 0.291379 9 9 B 0.264763 9 2 B 0.258188 9 5 C 0.239515 9 0

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124 Table D-11. The mean L*, a*, b* values of untreated, DPCD (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water beverages during storage L* values Week: *Control *DPCD treated *Heat treated 2 59.74 0.17 60.21 0.31 58.97 0.23 3 58.38 0.44 54.75 0.02 58.44 0.75 5 54.15 0.78 52.50 0.28 53.67 0.28 9 58.21 0.35 57.48 0.34 58.21 0.60 a* values Week: *Control *DPCD treated *Heat treated 2 -1.71 0.02 -1.50 0.01 -0.92 0.02 3 -0.60 0.04 4.90 0.10 1.72 0.12 5 3.55 0.17 4.73 0.19 3.27 0.29 9 6.08 0.30 2.37 0.08 1.71 0.01 b* values Week: *Control *DPCD treated *Heat treated 2 1.84 0.02 2.00 0.07 2.17 0.05 3 2.71 0.09 2.99 0.03 3.50 0.07 5 3.85 0.02 2.98 0.12 3.27 0.29 9 2.37 0.16 2.79 0.03 2.42 0.01 *Mean of three replicate measurements Std Error;

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125 APPENDIX E STORAGE STUDY TASTE PANELS: DATA AND ANALYSIS Table E-1. Taste panel data output obtained by Compusense software: Sensory evaluation scores of treatments during the storage study (Evaluation score scales : Overall likeability: 9 point scale; Aroma differen ce and taste difference from control: 15 cm line scale; Off flavor: 6 point scale; Purchase inte nt and ask again: 1=Yes and 2=No) Storage time (Weeks) Judges*Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchasea Intent Ask b again 0 1 control 6 1 5 2 1 0 1 DPCD 8 0.1 5.4 1 1 0 1 heat 4 2.8 7.1 2 2 2 0 2 control 4 1 2 3 2 2 0 2 heat 1 2.7 5 5 2 2 0 2 DPCD 6 1.1 0.8 2 2 1 0 3 DPCD 6 0.6 3 3 2 2 0 3 control 7 5 1.5 3 2 2 0 3 heat 7 2 4 3 2 2 0 4 DPCD 2 6.6 2.5 5 2 2 0 4 heat 3 0.2 0.5 4 2 2 0 4 control 4 0.1 2.8 3 2 2 0 5 heat 3 0 1.6 3 2 2 0 5 control 7 0 0 1 1 0 5 DPCD 7 0 0 1 1 0 6 heat 9 0.2 0.1 3 1 0 6 DPCD 8 1.3 1.5 2 1 0 6 control 9 0.1 0.1 3 1 0 7 control 3 1.8 1.1 4 2 1 0 7 DPCD 6 0 0 4 1 0 7 heat 5 0 6.1 4 2 1 0 8 control 5 0.6 2.2 2 2 2 0 8 heat 6 0.1 0.8 1 2 2 0 8 DPCD 5 1.5 0.9 2 2 2 0 9 DPCD 3 1.5 2.7 2 2 2 0 9 control 3 1.2 0.2 2 2 2 0 9 heat 3 0.2 2.6 3 2 2 0 10 DPCD 2 0.1 0.5 3 2 2 0 10 heat 2 0 3.3 2 2 2 0 10 control 2 0.2 6.2 4 2 2

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126 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 0 11 heat 5 3 2 3 2 2 0 11 control 5 0.5 4 3 2 2 0 11 DPCD 5 0.5 2 2 2 2 0 12 heat 8 8 2.6 1 1 0 12 DPCD 8 7.1 5.2 1 1 0 12 control 8 2.5 1.8 1 1 0 13 control 9 8.4 6.2 4 1 0 13 DPCD 8 0 8 4 1 0 13 heat 7 0 8 4 1 0 14 control 7 1.2 0.5 2 1 0 14 heat 4 3.3 6.6 1 2 2 0 14 DPCD 7 1.8 2.5 1 1 0 15 DPCD 7 9 5.5 1 1 0 15 control 9 0 0 3 1 0 15 heat 6 0 2 2 1 0 16 DPCD 6 3 4.7 5 1 0 16 heat 7 8.3 4.4 4 1 0 16 control 4 9.3 1 3 2 1 0 17 heat 5 0.1 0.3 3 2 2 0 17 control 5 0.2 0.3 3 2 2 0 17 DPCD 5 0.5 0.6 3 2 2 0 18 heat 7 0.2 0.2 1 1 0 18 DPCD 7 0.2 0.3 1 1 0 18 control 7 0.3 0.3 1 1 0 19 control 3 0.1 6.3 4 2 2 0 19 DPCD 3 0.1 5.6 4 2 2 0 19 heat 2 0.3 7.7 5 2 2 0 20 control 5 1.4 1.8 1 2 1 0 20 heat 6 3.3 4.3 2 2 1 0 20 DPCD 6 1.1 1.9 2 2 1 0 21 DPCD 2 5.4 4.8 5 2 2 0 21 control 2 6.1 5.6 5 2 2 0 21 heat 1 5.2 5.9 4 2 2 0 22 DPCD 6 8.5 1.5 1 2 2 0 22 heat 6 2.5 2 1 2 2 0 22 control 6 1 1.1 1 2 2 0 23 heat 2 1 4 2 2 2 0 23 control 3 0.1 0.2 1 2 2 0 23 DPCD 2 0.1 0.2 1 2 2 0 24 heat 4 0 2.8 2 2 1

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127 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 0 24 DPCD 8 0.3 0.1 1 1 0 24 control 7 0.6 1 1 1 0 25 control 6 6.6 0.9 3 2 2 0 25 DPCD 5 9.4 1.9 3 1 0 25 heat 1 1.5 9.9 6 2 2 0 26 control 3 7.1 2.2 2 2 2 0 26 heat 1 1.9 10 5 2 2 0 26 DPCD 3 1 1 1 2 2 0 27 DPCD 7 1.4 1.5 1 1 0 27 control 5 1.6 1.6 1 2 2 0 27 heat 5 3 2.2 1 2 2 0 28 DPCD 7 0.6 4.3 2 1 0 28 heat 8 0.6 6.2 4 1 0 28 control 6 0.6 4.1 3 1 0 29 heat 2 2.2 8.7 6 2 2 0 29 control 3 1.6 6.7 5 2 2 0 29 DPCD 3 1.5 6.2 5 2 1 0 30 heat 1 0 8 4 2 2 0 30 DPCD 4 0.2 3.7 3 2 2 0 30 control 7 0.1 1.4 2 1 0 31 control 2 4.2 7.9 2 2 1 0 31 DPCD 5 2.8 3.5 1 2 1 0 31 heat 3 2.2 8.1 3 2 1 0 32 control 5 0.1 0.1 3 2 1 0 32 heat 3 0.4 4 3 2 2 0 32 DPCD 5 0.7 1.8 2 2 2 0 33 DPCD 3 0.3 3.8 4 2 2 0 33 control 4 3.4 1.4 3 2 2 0 33 heat 5 0.5 1.2 3 2 2 0 34 DPCD 4 4.7 4.8 3 2 2 0 34 heat 5 2.5 4.4 4 2 2 0 34 control 6 1.5 0.2 1 2 2 0 35 heat 3 5.6 8.9 4 2 2 0 35 control 4 6.7 1.5 1 2 2 0 35 DPCD 4 0.9 3.5 1 2 2 0 36 heat 6 0.7 4.8 4 1 0 36 DPCD 6 0.4 6.9 1 1 0 36 control 8 0.6 5.2 1 1 0 37 control 4 1 7.6 2 2 2 0 37 DPCD 6 2.8 8.4 2 2 1 0 37 heat 7 2.5 9.1 1 1

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128 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 0 38 control 4 0.2 7.4 4 2 2 0 38 heat 6 0.2 1.4 2 2 1 0 38 DPCD 6 0.2 3.1 2 2 1 0 39 DPCD 5 0.2 6.8 4 1 0 39 control 5 0.3 6.1 4 2 1 0 39 heat 6 0.1 1.6 3 1 0 40 DPCD 7 6.2 2.2 2 2 2 0 40 heat 7 3.4 4 1 1 0 40 control 8 2.8 4.3 1 1 0 41 heat 2 2.2 7 5 2 2 0 41 control 3 0.8 5.2 4 2 1 0 41 DPCD 3 0.9 4.8 4 2 1 0 42 heat 4 0.2 6.6 3 2 2 0 42 DPCD 6 0.2 5.4 1 2 1 0 42 control 7 0 0 1 2 1 0 43 control 3 0.9 0.1 3 2 2 0 43 DPCD 5 6.6 4.8 2 2 2 0 43 heat 5 5.4 5.7 2 2 2 0 44 control 5 0 0 1 2 2 0 44 heat 5 0.2 0.3 1 2 2 0 44 DPCD 5 0.1 0.4 1 2 1 0 45 DPCD 3 1 7.8 6 2 2 0 45 control 6 0.1 3.6 1 1 0 45 heat 3 7 9.9 5 2 2 0 46 DPCD 6 7.1 1.6 3 2 1 0 46 heat 1 1.9 4.8 6 2 2 0 46 control 4 0 1 4 2 2 0 47 heat 5 0.8 3.3 2 2 1 0 47 control 7 1.5 2.7 1 1 0 47 DPCD 6 3.5 2.6 1 1 0 48 heat 6 7.6 3.8 5 2 2 0 48 DPCD 6 0.4 0.6 5 2 2 0 48 control 7 2.3 6.9 4 2 2 0 49 control 7 1 0 1 1 0 49 DPCD 7 1 0 1 1 0 49 heat 5 1 3 3 2 2 0 50 control 9 4 0 2 1 0 50 heat 1 5.6 9.9 6 2 2 0 50 DPCD 7 5.8 5.6 2 1 2 1 control 2 1 2 2 2 2 2 1 DPCD 6 0.5 1.5 1 1

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129 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 2 1 heat 4 0.5 3.5 2 2 2 2 2 control 1 2 4.4 4 2 2 2 2 heat 2 0.6 6.6 4 2 2 2 2 DPCD 1 7.9 8.4 3 2 2 2 3 DPCD 7 4.8 2.5 2 2 1 2 3 control 4 4 4.1 2 2 2 2 3 heat 3 3.3 1 2 2 2 2 4 DPCD 6 2.5 4.7 3 1 2 4 heat 5 0.8 8.1 5 2 1 2 4 control 8 1.1 2 3 1 2 5 heat 6 0.6 6.9 3 1 2 5 control 7 0.6 0.7 3 1 2 5 DPCD 5 0.6 4.9 4 2 1 2 6 heat 4 1.5 5.2 3 2 1 2 6 DPCD 3 2.5 6.4 3 2 2 2 6 control 2 1.1 6.6 5 2 2 2 7 control 5 0.2 1.5 2 1 2 7 DPCD 4 0.1 3.8 3 2 1 2 7 heat 3 2.8 5.5 4 2 1 2 8 control 5 1.9 0.9 4 2 2 2 8 heat 7 0.8 4.3 2 1 2 8 DPCD 5 0.5 0.9 3 2 2 2 9 DPCD 5 2.5 0.3 1 1 2 9 control 5 2.2 4 2 1 2 9 heat 2 4.4 7.8 4 2 1 2 10 DPCD 3 4.7 6.8 4 2 2 2 10 heat 4 0.1 8.3 4 2 2 2 10 control 3 0.1 2.8 3 2 2 2 11 heat 6 0.5 1 1 1 2 11 control 7 8.4 0.2 1 1 2 11 DPCD 8 0.4 0.7 1 1 2 12 heat 6 0 2.3 3 2 2 2 12 DPCD 8 0.1 0.1 1 1 2 12 control 7 0.1 2.6 3 1 2 13 control 2 0.9 6.2 4 2 2 2 13 DPCD 5 0.8 6.3 3 2 1 2 13 heat 4 1.3 2 3 2 2 2 14 control 5 0.2 1.8 2 2 2 2 14 heat 5 0.2 0.1 1 2 2 2 14 DPCD 5 0.1 3 2 2 2 2 15 DPCD 5 0.2 6.1 4 1

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130 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 2 15 control 7 1.1 7.7 4 1 2 15 heat 2 0.3 6.4 5 2 2 2 16 DPCD 5 1.8 0.9 3 1 2 16 heat 4 0.9 5.8 3 2 1 2 16 control 6 1 1.5 2 1 2 17 heat 3 1 4.3 5 2 2 2 17 control 2 0.5 0.2 5 2 2 2 17 DPCD 3 1.7 5.2 4 2 2 2 18 heat 3 6.6 1 4 2 2 2 18 DPCD 2 4.8 5.2 5 2 2 2 18 control 4 4.4 0.1 6 2 2 2 19 control 3 0.8 0.5 2 2 2 2 19 DPCD 3 0.8 3.2 3 2 2 2 19 heat 3 0.3 5.2 4 2 2 2 20 control 2 1.5 0.3 2 2 2 2 20 heat 9 6.1 9.4 5 1 2 20 DPCD 3 4.7 1.5 3 2 2 2 21 DPCD 3 0.9 2.2 3 2 2 2 21 control 2 0.7 4.2 4 2 2 2 21 heat 6 1 5.6 4 1 2 22 DPCD 7 0.1 3.6 2 1 2 22 heat 5 1.5 1.1 3 2 2 2 22 control 6 0 0.1 4 1 2 23 heat 3 5.7 7.4 5 2 2 2 23 control 5 0.4 4.1 4 2 2 2 23 DPCD 7 0.2 7.7 3 1 2 24 heat 7 4.1 5.8 3 1 2 24 DPCD 6 1.5 6 1 1 2 24 control 7 5.6 4.9 2 1 2 25 control 1 0.8 4.8 4 2 2 2 25 DPCD 1 0.4 9.1 5 2 2 2 25 heat 1 2.3 9.2 5 2 2 2 26 control 4 1.5 1.1 2 2 2 2 26 heat 5 0.1 1.9 3 2 2 2 26 DPCD 6 3.5 4.5 3 2 1 2 27 DPCD 6 0.6 8.8 4 2 2 2 27 control 3 6.8 1.9 5 2 2 2 27 heat 4 5.7 9.2 3 2 2 2 28 DPCD 5 0.7 0.9 1 2 2 2 28 heat 5 2.9 1.9 1 2 2 2 28 control 5 0.2 2 1 2 2

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131 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 2 29 heat 4 0.6 2 3 2 2 2 29 control 4 0.5 1 2 2 2 2 29 DPCD 4 2.6 0.2 1 2 2 2 30 heat 4 1.5 6.7 3 2 2 2 30 DPCD 5 6.5 4.9 1 2 2 2 30 control 5 0.1 7.8 2 2 2 2 31 control 1 7.2 8.6 5 2 2 2 31 DPCD 3 0.2 8.1 4 2 2 2 31 heat 2 0.5 8.3 4 2 2 2 32 control 3 2 5.1 3 2 2 2 32 heat 5 0.2 4.2 3 2 2 2 32 DPCD 5 0.6 4.8 3 2 1 2 33 DPCD 6 6.1 5.9 3 1 2 33 control 6 0.9 5.4 2 1 2 33 heat 7 1.6 2.8 1 1 2 34 DPCD 5 0.6 1.8 1 2 1 2 34 heat 3 1.5 9.2 4 2 1 2 34 control 1 1.1 8.3 6 2 1 2 35 heat 4 3.1 6.6 3 2 2 2 35 control 4 4 6.3 3 2 2 2 35 DPCD 4 0.2 0.1 2 2 2 2 36 heat 5 0.4 3.7 2 2 2 2 36 DPCD 5 2.2 4.8 1 2 2 2 36 control 5 4.1 3.8 1 2 2 2 37 control 6 4 1.9 1 1 2 37 DPCD 5 3.5 4 2 1 2 37 heat 6 0 0.9 1 1 2 38 control 4 10 10 5 2 2 2 38 heat 4 10 10 4 2 2 2 38 DPCD 6 0.1 9.9 3 2 1 2 39 DPCD 5 1.2 1.7 2 2 2 2 39 control 6 0.6 3.4 1 2 2 2 39 heat 4 3.8 6.6 3 2 2 2 40 DPCD 6 6.5 6.2 2 2 1 2 40 heat 3 1.2 3.8 4 2 2 2 40 control 6 2.8 3.8 2 1 2 41 heat 1 3.9 6.7 4 2 2 2 41 control 6 0.2 1.1 1 2 2 2 41 DPCD 6 2.7 2.7 1 2 2 2 42 heat 3 5.3 7.6 4 2 1 2 42 DPCD 3 0.1 8.2 4 2 1

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132 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 2 42 control 3 2.2 1 3 2 1 2 43 control 5 0.1 0.2 3 2 2 2 43 DPCD 4 2.5 1.2 3 2 2 2 43 heat 2 0.3 7 4 2 2 2 44 control 4 3.6 4 4 2 2 2 44 heat 4 3.5 5.4 5 2 2 2 44 DPCD 4 3 5.3 5 2 2 2 45 DPCD 6 0.9 3.5 1 1 2 45 control 7 0.3 0.9 1 1 2 45 heat 5 0.8 5.4 4 2 1 2 46 DPCD 6 6.9 6.2 4 1 2 46 heat 5 0.9 4.6 1 2 1 2 46 control 7 8.8 3.5 3 1 2 47 heat 4 0.7 2.8 3 2 2 2 47 control 4 0 3.2 4 2 2 2 47 DPCD 6 0 6.1 1 2 2 2 48 heat 3 1.7 6.5 4 2 2 2 48 DPCD 6 6.6 4.1 4 1 2 48 control 8 4 1.8 4 1 2 49 control 1 8.1 9.9 6 2 2 2 49 DPCD 1 3.8 7.1 6 2 2 2 49 heat 1 0.1 0.2 2 2 2 2 50 control 3 0.1 2.2 4 2 1 2 50 heat 4 0.4 2 4 2 1 2 50 DPCD 4 0.5 2.1 4 2 1 3 1 control 6 5 8.7 4 1 3 1 DPCD 3 0.1 1.6 3 2 2 3 1 heat 5 0.2 0.3 3 2 2 3 2 control 6 0.3 4.9 3 2 2 3 2 heat 7 0.1 8.9 3 2 2 3 2 DPCD 5 2.6 0 1 2 2 3 3 DPCD 5 1.5 1.4 4 2 2 3 3 control 6 4.1 2 4 2 2 3 3 heat 4 2 6.8 5 2 2 3 4 DPCD 7 10 0.1 2 1 3 4 heat 5 9.9 9.8 4 2 1 3 4 control 4 10 10 5 2 2 3 5 heat 1 0.1 0.4 2 2 2 3 5 control 1 0.1 0.9 2 2 2 3 5 DPCD 1 0.1 0.3 2 2 2 3 6 heat 1 5.2 1.6 4 2 2

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133 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 3 6 DPCD 5 1 5.5 4 2 2 3 6 control 3 1.9 4 2 2 2 3 7 control 2 6.1 2.6 2 2 2 3 7 DPCD 3 0.1 6.2 1 2 2 3 7 heat 3 0.9 3.7 1 2 2 3 8 control 1 0.3 9.6 6 2 2 3 8 heat 4 4.6 1.1 4 2 2 3 8 DPCD 6 1.2 7.3 3 1 3 9 DPCD 1 5.3 0.6 1 2 2 3 9 control 6 0.3 8.4 1 2 2 3 9 heat 4 5.3 4.3 1 2 2 3 10 DPCD 7 3.5 4.3 3 1 3 10 heat 3 0.1 5.9 4 2 2 3 10 control 6 0.1 6.3 3 1 3 11 heat 4 3.8 1.7 4 2 2 3 11 control 6 0.8 0.2 1 2 1 3 11 DPCD 3 0.8 6.7 5 2 2 3 12 heat 2 0.7 0.9 4 2 2 3 12 DPCD 3 4.2 5.1 5 2 2 3 12 control 3 3.5 7.8 4 2 2 3 13 control 2 4 3 3 2 2 3 13 DPCD 5 0.4 5.5 4 1 3 13 heat 5 2.3 4.9 4 1 3 14 control 3 0.1 0.1 3 2 2 3 14 heat 3 0.6 2 4 2 2 3 14 DPCD 3 0.1 0.6 4 2 2 3 15 DPCD 5 5.3 4.1 4 2 2 3 15 control 5 4.2 7.3 3 2 2 3 15 heat 5 2.2 6.9 3 2 2 3 16 DPCD 2 7.6 7.2 4 2 1 3 16 heat 5 2.2 1.6 1 1 3 16 control 2 2.5 1.7 4 2 1 3 17 heat 5 0.6 1.9 2 2 2 3 17 control 7 0 0 1 1 3 17 DPCD 6 0.3 1 2 2 1 3 18 heat 4 1.1 1 1 2 1 3 18 DPCD 5 1 2 1 2 1 3 18 control 3 0.3 0.6 1 2 1 3 19 control 5 0.1 1 2 2 1 3 19 DPCD 4 0.2 2.7 3 2 2 3 19 heat 3 0.1 4.2 3 2 2

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134 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 3 20 control 3 0.3 1.9 4 2 1 3 20 heat 5 1.7 4.5 4 2 1 3 20 DPCD 6 3.4 4.8 4 2 1 3 21 DPCD 6 1 3 3 1 3 21 control 6 1 4 4 1 3 21 heat 7 0 5 4 1 3 22 DPCD 5 0.1 5.9 5 2 1 3 22 heat 3 1 6.6 4 2 2 3 22 control 8 0.5 0.2 2 1 3 23 heat 5 2.6 0 4 2 1 3 23 control 5 1.7 0.9 4 2 1 3 23 DPCD 6 1.8 1 3 1 3 24 heat 6 2.5 4.5 3 1 3 24 DPCD 4 1.5 3.2 2 2 1 3 24 control 7 0.5 6 4 1 3 25 control 4 0.4 0.2 3 2 2 3 25 DPCD 3 4.8 6.3 4 2 2 3 25 heat 2 5.2 7.8 5 2 2 3 26 control 6 0.3 0.3 1 2 2 3 26 heat 3 5.2 6.2 4 2 2 3 26 DPCD 4 3.4 5.8 2 2 2 3 27 DPCD 5 1.2 5.3 4 2 2 3 27 control 7 0.8 0.1 2 1 3 27 heat 6 0.8 3 2 1 3 28 DPCD 6 3 6.7 2 1 3 28 heat 7 0.3 1.8 1 1 3 28 control 6 0.8 3 2 1 3 29 heat 7 0.5 5.5 2 1 3 29 control 6 0.1 4.3 3 2 2 3 29 DPCD 8 0.2 0 1 1 3 30 heat 2 9.9 8.3 3 2 1 3 30 DPCD 5 8.9 0.8 4 1 3 30 control 6 8.6 2.2 3 1 3 31 control 9 0 0 1 1 3 31 DPCD 7 0 0.4 2 2 1 3 31 heat 7 0 2.2 3 2 1 3 32 control 1 0.3 5.6 2 2 2 3 32 heat 1 6.4 1.2 2 2 2 3 32 DPCD 1 7.2 8.1 2 2 2 3 33 DPCD 6 1.2 2 1 2 1 3 33 control 6 0.6 2.2 1 2 1

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135 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 3 33 heat 6 0.6 3.2 1 2 1 3 34 DPCD 6 4.7 2.2 2 1 3 34 heat 6 0.1 1.1 1 1 3 34 control 7 0.1 1.9 1 1 3 35 heat 6 0.3 4.7 3 1 3 35 control 5 0.1 5.4 2 1 3 35 DPCD 4 0.3 4.8 2 1 3 36 heat 6 7.2 2.5 1 2 1 3 36 DPCD 5 1.4 4.1 1 1 3 36 control 4 0.3 4.8 1 2 1 3 37 control 4 0 3.2 4 2 2 3 37 DPCD 6 4.9 1.9 1 2 2 3 37 heat 5 1.5 2.3 1 2 2 3 38 control 5 0.8 1.9 4 2 1 3 38 heat 4 4.9 6.9 3 2 2 3 38 DPCD 6 2.5 6.5 2 1 3 39 DPCD 7 6.4 6.7 2 1 3 39 control 8 1.1 4.9 1 1 3 39 heat 6 0.4 3 2 1 3 40 DPCD 5 0.8 4 2 2 2 3 40 heat 5 0 4.6 3 2 2 3 40 control 6 0 1.5 1 1 3 41 heat 7 4.7 0.1 2 1 3 41 control 6 0.1 1 2 1 3 41 DPCD 7 0.3 3.2 3 1 3 42 heat 7 0.8 5.2 2 1 3 42 DPCD 7 1.9 6.2 3 1 3 42 control 6 2.8 2.5 2 1 3 43 control 6 0.9 0.3 3 1 3 43 DPCD 6 0 0.2 4 1 3 43 heat 6 0.1 0.2 4 1 3 44 control 4 0 3.7 3 2 2 3 44 heat 3 0 6 4 2 2 3 44 DPCD 6 0 2 1 2 2 3 45 DPCD 5 0.2 4.7 2 2 2 3 45 control 6 0.2 8.1 3 2 2 3 45 heat 4 0.2 8.4 2 2 2 3 46 DPCD 7 0.6 5.9 1 1 3 46 heat 7 0.8 3.5 1 1 3 46 control 8 0.7 0.8 1 1 3 47 heat 7 10 6.1 3 1

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136 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 3 47 control 7 10 5 3 1 3 47 DPCD 6 10 9.2 4 2 1 3 48 heat 5 2 6.1 3 1 3 48 DPCD 4 2 0.5 3 2 2 3 48 control 5 2 3.5 3 2 2 3 49 control 5 8.1 8.6 4 2 2 3 49 DPCD 2 2 9.9 2 2 2 3 49 heat 5 9.6 7.9 3 2 2 3 50 control 4 7 2 2 2 2 3 50 heat 5 4.5 2.5 2 2 2 3 50 DPCD 4 5.1 2 2 2 2 5 1 control 2 7.1 7.7 2 2 2 5 1 DPCD 1 7.7 7.3 2 2 2 5 1 heat 1 7.4 0.2 1 2 2 5 2 control 6 0.5 3 4 1 5 2 heat 8 4.2 3.8 3 1 5 2 DPCD 7 6 6.5 3 1 5 3 DPCD 4 2.5 7.9 5 2 1 5 3 control 5 0.8 4 4 2 1 5 3 heat 4 3 3.7 4 2 1 5 4 DPCD 6 0.1 0.3 1 1 5 4 heat 6 0.1 0.6 2 1 5 4 control 6 0.1 0.1 1 1 5 5 heat 4 0.2 7.2 4 2 2 5 5 control 2 0.3 7 4 2 2 5 5 DPCD 4 3 6.3 3 2 2 5 6 heat 4 1 1 3 2 2 5 6 DPCD 3 2 2 3 2 2 5 6 control 4 1.6 5.8 4 2 2 5 7 control 3 5.2 4.8 4 2 2 5 7 DPCD 5 0.2 5.8 4 2 2 5 7 heat 4 0 3.2 4 2 2 5 8 control 4 1.1 1.2 1 2 1 5 8 heat 3 1.1 3.5 1 2 1 5 8 DPCD 3 0.8 9.1 1 2 1 5 9 DPCD 4 1 3 2 2 2 5 9 control 6 0 0 1 2 1 5 9 heat 7 0 0 1 1 5 10 DPCD 1 0 3 5 2 2 5 10 heat 1 3.7 3.3 6 2 2 5 10 control 1 1.1 1.4 4 2 2

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137 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 5 11 heat 5 2.2 3.2 2 2 2 5 11 control 5 3 1.5 1 2 2 5 11 DPCD 5 2.2 2.8 1 2 2 5 12 heat 7 0.9 1.5 2 1 5 12 DPCD 5 0.2 4 1 2 1 5 12 control 5 0.9 1.9 2 2 1 5 13 control 7 1 0.2 2 1 5 13 DPCD 6 0.6 3 2 1 5 13 heat 7 1.6 2.7 2 1 5 14 control 6 0.6 1 1 1 5 14 heat 7 1 1 1 1 5 14 DPCD 7 0.6 1.5 1 1 5 15 DPCD 4 1 7 5 2 2 5 15 control 5 0.6 0.5 3 2 1 5 15 heat 4 0.1 4 4 2 2 5 16 DPCD 8 9.9 1.1 1 1 5 16 heat 8 0.6 5.2 1 1 5 16 control 8 1.4 1.5 1 1 5 17 heat 6 0 0.9 2 2 1 5 17 control 7 0 0 1 2 1 5 17 DPCD 8 0 2.2 1 1 5 18 heat 5 3.7 4.6 5 2 2 5 18 DPCD 5 4.3 4.3 5 2 2 5 18 control 6 1.5 6.1 4 2 2 5 19 control 4 6.1 3.5 3 2 1 5 19 DPCD 5 1 3.5 3 2 1 5 19 heat 5 1.5 3 2 2 1 5 20 control 2 2.3 0.1 1 2 1 5 20 heat 1 0.2 0.1 1 2 1 5 20 DPCD 2 0.2 2.4 2 2 2 5 21 DPCD 5 7.8 6.9 3 2 2 5 21 control 5 4.4 4.7 3 2 2 5 21 heat 5 4.5 5.4 3 2 2 5 22 DPCD 6 9.7 5.8 2 1 5 22 heat 6 2.2 0.5 2 1 5 22 control 6 4.2 8 4 2 1 5 23 heat 8 9.9 3.5 1 1 5 23 control 8 0.5 0.8 1 1 5 23 DPCD 6 0.8 5.9 2 2 2 5 24 heat 4 0.3 8.9 2 2 1 5 24 DPCD 3 0.7 9.9 2 2 2

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138 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 5 24 control 6 0.9 0.7 1 2 1 5 25 control 5 6.6 6.9 3 1 5 25 DPCD 7 0 3.5 3 1 5 25 heat 7 3.1 3.5 1 1 5 26 control 4 5 7.5 4 2 2 5 26 heat 6 5 6 4 2 2 5 26 DPCD 7 5 7 4 2 2 5 27 DPCD 2 0.1 5 6 2 2 5 27 control 1 0.2 5.4 6 2 2 5 27 heat 1 0.3 0.2 6 2 2 5 28 DPCD 1 0.6 0.9 1 2 2 5 28 heat 1 5.6 2.1 2 2 2 5 28 control 2 1.4 0.8 2 2 2 5 29 heat 3 2.9 6.6 3 2 2 5 29 control 6 4.6 1 1 2 2 5 29 DPCD 5 1.5 3.1 2 2 2 5 30 heat 1 0.9 5.8 2 2 2 5 30 DPCD 1 0.1 3.5 3 2 2 5 30 control 2 0.2 4.4 1 2 2 5 31 control 4 4.8 5.2 3 2 2 5 31 DPCD 5 1.5 3 2 2 2 5 31 heat 5 0.1 3.7 2 2 2 5 32 control 3 0.9 1.4 2 2 2 5 32 heat 3 1.5 4.6 3 2 2 5 32 DPCD 3 4.2 1.2 2 2 2 5 33 DPCD 6 1.5 1.2 1 1 5 33 control 5 0.5 3.6 3 2 1 5 33 heat 5 1.1 1.9 2 2 2 5 34 DPCD 4 0 4.2 4 2 2 5 34 heat 6 3.7 5.8 3 1 5 34 control 7 0 8.4 3 2 2 5 35 heat 9 1 6.5 1 1 5 35 control 6 0.7 1.6 3 1 5 35 DPCD 6 1.7 7.7 2 1 5 36 heat 3 0.4 1.4 4 2 2 5 36 DPCD 2 0.8 0.8 4 2 2 5 36 control 4 1 3.5 3 2 2 5 37 control 6 2.8 1.3 1 2 1 5 37 DPCD 2 1.1 6.9 3 1 5 37 heat 4 0.9 1.2 1 1 5 38 control 3 2.5 7.8 4 2 1

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139 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 5 38 heat 1 0.9 9.1 5 2 1 5 38 DPCD 7 0.2 10 4 1 5 39 DPCD 6 0.1 5.8 2 1 5 39 control 4 0.1 5.7 3 1 5 39 heat 6 5.7 8.1 3 1 5 40 DPCD 7 3.5 0.9 2 1 5 40 heat 4 0.4 5.8 4 2 2 5 40 control 6 0.1 0.1 1 2 2 5 41 heat 7 0.6 0.2 1 2 1 5 41 control 7 0.3 2.5 2 2 1 5 41 DPCD 6 1 4 3 2 1 5 42 heat 4 1 4.6 4 2 1 5 42 DPCD 5 2.5 2.6 4 2 1 5 42 control 5 1 2.3 3 2 1 5 43 control 6 2.2 0.7 4 1 5 43 DPCD 9 0.1 0.8 2 1 5 43 heat 9 1.9 8.8 1 1 5 44 control 7 0.9 1.1 2 1 5 44 heat 6 2.5 4 3 2 1 5 44 DPCD 7 2.7 3.2 1 1 5 45 DPCD 7 1.4 7.4 3 1 5 45 control 2 6.1 2 4 2 2 5 45 heat 1 1.5 0.1 3 2 2 5 46 DPCD 3 8.4 8.6 4 2 2 5 46 heat 4 1.4 2.7 3 2 2 5 46 control 5 2.1 1.2 2 2 2 5 47 heat 5 6.1 7.6 2 1 5 47 control 7 5.7 5.3 1 1 5 47 DPCD 6 2.9 1.4 2 1 5 48 heat 6 1.1 1.7 4 1 5 48 DPCD 6 4.9 0.8 3 1 5 48 control 6 3.5 6.6 5 1 5 49 control 3 1 9 5 2 2 5 49 DPCD 8 9 2 1 1 5 49 heat 6 2 2 2 2 2 5 50 control 5 0.3 6.5 4 2 1 5 50 heat 5 0.4 5.7 4 2 2 5 50 DPCD 4 0.5 6.1 4 2 2 9 1 control 6 0.9 2.8 4 2 2 9 1 DPCD 4 0.2 5 3 2 2 9 1 heat 6 0.8 0.8 3 2 2

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140 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 9 2 control 4 8 7.6 3 2 2 9 2 heat 5 5.7 8.1 3 2 2 9 2 DPCD 5 8.9 3.5 3 2 2 9 3 DPCD 3 0.6 6.6 5 2 2 9 3 control 5 0.4 1.9 3 1 9 3 heat 3 0.3 7.2 4 2 2 9 4 DPCD 2 1.1 1.9 4 2 2 9 4 heat 6 0.1 6.3 3 2 2 9 4 control 6 0.5 7.1 3 2 2 9 5 heat 5 0.5 4.9 3 2 2 9 5 control 4 0.2 0.6 2 2 2 9 5 DPCD 4 0.4 5 2 2 2 9 6 heat 3 0.4 7.5 4 2 2 9 6 DPCD 3 0.6 6.9 4 2 2 9 6 control 5 5.8 5.6 3 2 2 9 7 control 9 1 8 4 1 9 7 DPCD 5 0.1 0.2 3 2 2 9 7 heat 7 0 8 5 1 9 8 control 7 0.3 0.9 1 1 9 8 heat 7 0.3 1.4 3 1 9 8 DPCD 8 3.3 0.9 1 1 9 9 DPCD 6 2.2 3.8 1 1 9 9 control 7 1.8 0.3 1 1 9 9 heat 6 1 4.8 1 1 9 10 DPCD 7 0.4 2.2 1 2 2 9 10 heat 8 0.8 2.2 1 2 1 9 10 control 8 0 0 1 1 9 11 heat 7 3.2 2.3 1 1 9 11 control 7 0.1 0.1 1 1 9 11 DPCD 5 0.1 8.4 2 2 1 9 12 heat 7 5.7 4.4 3 1 9 12 DPCD 6 2.2 5.9 2 2 1 9 12 control 5 1 3.5 4 2 2 9 13 control 5 0.3 0.5 2 2 2 9 13 DPCD 5 0.2 0.5 1 2 2 9 13 heat 5 0.3 4.7 1 2 2 9 14 control 3 1.5 0 4 2 2 9 14 heat 2 2.5 2.5 5 2 2 9 14 DPCD 3 0.1 0.5 5 2 2 9 15 DPCD 5 0.1 0.8 2 2 1 9 15 control 5 0.1 0.1 2 2 1

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141 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 9 15 heat 5 0.3 0.6 2 2 1 9 16 DPCD 7 4.5 1.9 3 1 9 16 heat 6 1.3 5.3 2 1 9 16 control 7 2.3 3.8 2 1 9 17 heat 8 0 0 2 1 9 17 control 8 0 0 2 1 9 17 DPCD 6 1.4 6.7 2 1 9 18 heat 7 0.6 0.3 1 1 9 18 DPCD 8 0.5 0.3 1 1 9 18 control 7 0.4 0.8 1 1 9 19 control 7 0.1 0 2 2 2 9 19 DPCD 7 2.5 2.8 1 1 9 19 heat 8 1.2 3 4 1 9 20 control 6 4.1 4.8 4 2 1 9 20 heat 5 5.8 3.8 3 2 2 9 20 DPCD 6 4.3 1.1 1 1 9 21 DPCD 3 7.6 0.6 5 2 2 9 21 control 7 9.6 8.8 3 1 9 21 heat 1 4.1 0.1 6 2 2 9 22 DPCD 2 6.4 7.1 5 2 2 9 22 heat 4 1.1 3.7 3 2 2 9 22 control 5 0.6 7 5 2 2 9 23 heat 3 1.3 4 4 2 2 9 23 control 5 0.3 3.5 2 2 2 9 23 DPCD 5 1.1 2.5 2 2 2 9 24 heat 1 9 10 6 2 2 9 24 DPCD 5 8 2 2 2 2 9 24 control 6 2 3.5 2 2 1 9 25 control 6 2.8 3.5 3 1 9 25 DPCD 2 0.1 9.2 5 2 2 9 25 heat 7 6.2 2.2 1 1 9 26 control 5 0.9 2.2 2 2 2 9 26 heat 4 3.8 3.2 3 2 2 9 26 DPCD 3 2 4.3 3 2 2 9 27 DPCD 3 3.5 2.7 5 2 1 9 27 control 4 3.9 6.1 4 2 1 9 27 heat 6 6.3 8.1 3 2 1 9 28 DPCD 6 9.1 4.9 2 2 1 9 28 heat 6 2 7.6 2 2 1 9 28 control 6 2 3 2 2 1 9 29 heat 4 4.8 2.2 4 2 1

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142 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 9 29 control 2 0 8.7 5 2 2 9 29 DPCD 3 1.4 6.7 4 2 2 9 30 heat 8 0.2 1.4 1 1 9 30 DPCD 5 8.7 6.4 3 2 1 9 30 control 6 4 0.9 2 1 9 31 control 2 6.2 5.8 4 2 1 9 31 DPCD 6 5 7.8 3 1 9 31 heat 6 0 7.9 3 2 2 9 32 control 6 7.9 2.2 3 2 1 9 32 heat 6 0.6 7.6 3 2 1 9 32 DPCD 7 0.6 3.5 4 1 9 33 DPCD 6 1 3 3 1 9 33 control 4 0.5 6 4 2 2 9 33 heat 6 0.2 5 3 1 9 34 DPCD 5 0 0.3 1 2 2 9 34 heat 5 0.2 0.6 1 2 2 9 34 control 5 0 0.2 1 2 2 9 35 heat 8 8.4 5.3 4 1 9 35 control 6 4.9 6.3 6 2 1 9 35 DPCD 7 5 4.8 3 1 9 36 heat 3 4.8 5.9 4 2 2 9 36 DPCD 4 4.2 4.4 3 2 2 9 36 control 4 1.5 2.5 3 2 2 9 37 control 3 0.2 0.6 4 2 2 9 37 DPCD 2 2.3 3.8 5 2 2 9 37 heat 2 0.7 4.7 5 2 2 9 38 control 7 0.4 0.6 1 1 9 38 heat 4 0 5.3 4 1 9 38 DPCD 2 0 10 5 2 2 9 39 DPCD 7 3 2.8 2 2 1 9 39 control 6 7.9 7 4 2 1 9 39 heat 3 2 8 5 2 1 9 40 DPCD 4 0.3 3.3 3 2 1 9 40 heat 6 1.5 3.3 2 1 9 40 control 7 0.1 0.4 1 1 9 41 heat 8 1.5 4.5 1 1 9 41 control 8 2.4 4.2 1 1 9 41 DPCD 6 0.2 1.9 1 1 9 42 heat 3 0.2 9.6 5 2 2 9 42 DPCD 5 5.7 0.3 1 2 2 9 42 control 5 6.3 0.3 1 2 2

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143 Table E-1 Continued Storage time (Weeks) Judges Treatment Overall likeability Aroma difference from control Taste Difference from control Off-flavor Purchase Intent Ask again 9 43 control 4 0.1 1.7 5 2 2 9 43 DPCD 3 2.2 9.2 6 2 2 9 43 heat 2 0.1 9.9 6 2 2 9 44 control 4 0.6 2 2 2 2 9 44 heat 5 0.6 2.8 1 2 2 9 44 DPCD 4 0.6 1 2 2 2 9 45 DPCD 5 0 2.6 4 2 2 9 45 control 7 0 7.8 4 2 2 9 45 heat 4 0 0 2 2 2 9 46 DPCD 7 1.1 0.7 4 1 9 46 heat 2 8.2 8.6 5 2 2 9 46 control 6 0.9 2.3 2 2 1 9 47 heat 5 6.5 4.2 3 2 1 9 47 control 6 2.8 1.1 3 1 9 47 DPCD 5 1.2 1.4 3 2 1 9 48 heat 6 8.2 3.8 2 1 9 48 DPCD 7 0.8 5 2 1 9 48 control 4 3.2 7.5 3 2 1 9 49 control 8 0.1 5 3 1 9 49 DPCD 3 0.1 9 4 2 1 9 49 heat 5 0.1 4.5 4 1 9 50 control 3 0.2 5.2 2 2 2 9 50 heat 1 0.2 9.8 5 2 2 9 50 DPCD 7 0.2 3.7 4 1 a Purchase intent: Panelists answering “Yes” to the “Would you buy this product?” question chose score “1” and those answering “N o” to the same question chose score “2”. b Ask Again: Panelists choosing score “2” were asked a second question; ”Would you buy this product if you knew its rehydrating prop erties”. If their answer was “Yes”, they chose score “1” and if “No”, they chose the score “2”. This column is empty if the panelist was not asked the second question. *Control (untreated); H eat (heat treated at 74oC, 15 s); DPCD (DPCD treated at 34.5 MPa, 25oC, 13%CO2, 6 min); Storage at 4oC

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144 Table E-2. SAS software output of analys is of variance (ANOVA) for “overall likeability” data for untreate d, DPCD and heat treated coconut water by panelists The ANOVA Procedure Dependent Variable: likeability Sum of Source DF Squares Mean Square F Value Pr > F Model 259 1906.118667 7.359532 4.35 <.0001 Error 490 829.040000 1.691918 Corrected Total 749 2735.158667 R-Square Coeff Var Root MSE likeability Mean 0.696895 26.79355 1.300738 4.854667 Source DF Anova SS Mean Square F Value Pr > F week 4 47.565333 11.891333 7.03 <.0001 panelist(week) 245 1798.260000 7.339837 4.34 <.0001 treat 2 29.090667 14.545333 8.60 0.0002 week*treat 8 31.202667 3.900333 2.31 0.0197 The ANOVA Procedure Duncan's Multiple Range Test for likeability NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 490 Error Mean Square 1.691918 Number of Means 2 3 Critical Range .2286 .2407 Means with the same letter are not significantly different. Duncan Grouping Mean N treat A 5.0320 250 control A 4.9520 250 DPCD B 4.5800 250 heat Table E-3. The weekly mean “overall likeabil ity” scores for untreated, DPCD and heat pasteurized samples during storage Storage time (Week) Control (Untreated)* DPCD* Heat* 0 5.36 0.29 5.34 0.25 4.38 0.31 2 4.38 0.29 4.76 0.24 4.08 0.24 3 5.06 0.27 4.88 0.25 4.68 0.25 5 4.80 0.26 4.90 0.30 4.76 0.32 9 5.56 0.23 4.88 0.25 5.00 0.29 *Mean weekly score Std error

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145 Table E-4. SAS software output of analysis of variance (ANOVA) for “aroma difference from control scores” (corrected data) of different treatments during storage study The ANOVA Procedure Dependent Variable: aroma Sum of Source DF Squares Mean Square F Value Pr > F Model 224 1449.077612 6.469096 2.08 <.0001 Error 420 1303.573488 3.103746 Corrected Total 644 2752.651101 R-Square Coeff Var Root MSE aroma Mean 0.526430 101.7484 1.761745 1.731473 Source DF Anova SS Mean Square F Value Pr > F week 4 4.652651 1.163163 0.37 0.8267 panelist(week) 210 1315.925116 6.266310 2.02 <.0001 treat 2 112.821054 56.410527 18.17 <.0001 week*treat 8 15.678791 1.959849 0.63 0.7514 Duncan's Multiple Range Test for aroma NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 420 Error Mean Square 3.10374 Number of Means 2 3 Critical Range .3340 .3516 Means with the same letter are not significantly different. Duncan Grouping Mean N treat A 2.1242 215 DPCD A 1.9181 215 heat B 1.1521 215 control Table E-5. The weekly mean “aroma difference from control” scores for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC) Week Control* DPCD* Heat* 0 0.99 0.16 2.09 0.40 1.82 0.33 2 1.37 0.21 2.18 0.34 1.77 0.27 3 0.91 0.18 2.09 0.32 1.88 0.32 5 1.35 0.20 2.31 0.43 1.79 0.30 9 1.15 0.20 1.95 0.37 2.34 0.43 *Mean weekly score Std error

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146 Table E-6. SAS software output for analysis of variance (ANOVA) for “taste difference from control scores” (corrected data) of different treatments during the storage study The ANOVA Procedure Class Level Information Class Levels Values week 5 0 2 3 5 9 panelist 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 treat 3 DPCD control heat Number of Observations Read 597 Number of Observations Used 597 The ANOVA Procedure Dependent Variable: tastediff Sum of Source DF Squares Mean Square F Value Pr > F Model 208 2171.954631 10.442090 2.39 <.0001 Error 388 1698.488317 4.377547 Corrected Total 596 3870.442948 R-Square Coeff Var Root MSE tastediff Mean 0.561164 63.23168 2.092259 3.308878 Source DF Anova SS Mean Square F Value Pr > F week 4 40.635925 10.158981 2.32 0.0564 panelist(week) 194 1591.380356 8.202992 1.87 <.0001 treat 2 477.201642 238.600821 54.51 <.0001 week*treat 8 62.736708 7.842089 1.79 0.0772 Duncan's Multiple Range Test for tastediff NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error Alpha 0.05 Error Degrees of Freedom 388 Error Mean Square 4.377547 Number of Means 2 3 Critical Range .4124 .4341 Means with the same letter are not significantly different. Duncan Grouping Mean N treat A 4.1744 199 heat B 3.6744 199 DPCD C 2.0779 199 control Table E-7. The weekly mean “taste difference from control” scores for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC) Week Control* DPCD* Heat* 0 1.66 0.26 2.79 0.33 4.37 0.46 2 2.32 0.25 3.99 0.40 4.76 0.41 3 2.21 0.27 3.72 0.42 3.72 0.38 5 2.20 0.29 3.99 0.43 3.45 0.40 9 2.01 0.29 3.92 0.48 4.56 0.47 *Mean weekly score Std error

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147 Table E-8. SAS software output for analysis of variance (ANOVA) of “off flavor” scores of different treatments during storage study The ANOVA Procedure Dependent Variable: offflavor Sum of Source DF Squares Mean Square F Value Pr > F Model 259 953.312000 3.680741 4.23 <.0001 Error 490 425.946667 0.869279 Corrected Total 749 1379.258667 R-Square Coeff Var Root MSE offflavor Mean 0.691177 33.55391 0.932351 2.778667 Source DF Anova SS Mean Square F Value Pr > F week 4 18.6186667 4.6546667 5.35 0.0003 panelist(week) 245 903.9733333 3.6896871 4.24 <.0001 treat 2 16.4826667 8.2413333 9.48 <.0001 week*treat 8 14.2373333 1.7796667 2.05 0.0395 Duncan's Multiple Range Test for offflavor NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error Alpha 0.05 Error Degrees of Freedom 490 Error Mean Square 0.869279 Number of Means 2 3 Critical Range .1639 .1725 Means with the same letter are not significantly different. Duncan Grouping Mean N treat A 2.98800 250 heat B 2.68400 250 control B 2.66400 250 DPCD Table E-9. The weekly mean “off flavor” scor es for untreated, DPCD treated (34.5 MPa, 25oC, 13% CO2, 6 min) and heat treated (74oC, 15 s) coconut water during storage (4oC) Storage time (Week) Control (Untreated)* DPCD* Heat* 0 2.40 0.18 2.40 0.20 3.14 0.22 2 3.06 0.20 2.72 0.19 3.28 0.17 3 2.60 0.18 2.64 0.17 2.78 0.17 5 2.64 0.19 2.64 0.19 2.64 0.19 9 2.72 0.18 2.92 0.20 3.10 0.21 *Mean weekly score Std error

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148 Table E-10. Sample ballots that were used in sensory panels throughout the storage study (Output obtained by Compusense software). Carbonated Coconut Water *NOTE : Today's samples contain an arti ficial sweetener (Sucralose, Brand Name: Splenda). If you are, or susp ect you are, allergic, sensitive, or otherwise not able to consume artificial sweet eners, please DO NOT taste today. Thank you. Panelist Code: ________________________ Panelist Name: ________________________________________________ Question # 1. Please indicate your gender. Male Female Question # 2. Male: Please indicate your age range. Under 18 18-29 30-44 45-65 Over 65 Question # 3. Female: Please indicate your age range. Under 18 18-29 30-44 45-65 Over 65

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149 Table E-10 Continued Question # 4 Sample ______ Review Instructions Do not taste any of the samples at this time. The first test will be smelling the samples. Please read the di rections on the next screen. You are being presented with a reference sample marked 000. Please SMELL the reference sample. Then SMELL sample %01 and compare it to the reference sample. Please mark how different the sample SMELLS from the reference sample on the line scale. Sample Aroma Not Different Very At All Different Question # 5 Sample ______ Review Instructions Take a bite of cracker and a sip of water to rinse your mouth. The next 4 questions are related to your tasting experince. Please make the sample last, we have limited source. Click on the 'Continue' button below. You are being presented with a reference sample marked 000. Please TASTE this sample. Then TASTE sample %01 and compare it to the refer ence sample. Then mark how different the sample TASTES from the reference sample on the line scale. Taste Difference Not Different Very At All Different Question # 6 Sample ______ How much do you like the sample %01 OVERALL ? Sample %01 dislike neither like extremely like nor extremely dislike 1 2 3 4 5 6 7 8 9 Question # 7 Sample ______

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150 Table E-10 Continued Please rate the in tensity of the OFF FLAVOR, if any. Off-flavor None Just Slightly Moderately Very Extremely Detectable Detectable Intense Intense Intense 1 2 3 4 5 6 Question # 8 Sample ______ Would you buy this product? Yes No Question # 9 Sample ______ Would you buy this product if you knew coconut water had rehydrating properties? Yes No

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160 BIOGRAPHICAL SKETCH Sibel Damar was born in Ankara, Turkey. Sh e scored in the hi ghest 98% percentile in the nationwide college entrance examinati on and entered M.E. Technical University, one of the best universities in Turkey. Af ter earning her B.S. and M.Sc. degrees from Food Engineering Department, she was awar ded the prestigious Graduate Alumni Fellowship to begin her Ph.D. work in the food science program at the University of Florida. Under Dr. Murat O. Balaban’s supe rvision, she is going to receive her Ph.D. degree in 2006 and gradua te with a GPA of 4.0.


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Permanent Link: http://ufdc.ufl.edu/UFE0015541/00001

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Title: Processing of Coconut Water with High-Pressure Carbon Dioxide Technology
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Copyright Date: 2008

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Holding Location: University of Florida
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Material Information

Title: Processing of Coconut Water with High-Pressure Carbon Dioxide Technology
Physical Description: Mixed Material
Copyright Date: 2008

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Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
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PROCESSING OF COCONUT WATER WITH HIGH PRESSURE CARBON
DIOXIDE TECHNOLOGY















By

SIBEL DAMAR


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006


































To my Mom and Dad















ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Murat O. Balaban for his guidance and

invaluable support in all stages of my research. He taught me how to do research, how to

work in a team, how to be productive and also prepared me for a professional

environment. I would also like to thank Dr. Marty R. Marshall, Dr. Russell L. Rouseff

and Dr. Bruce A. Welt for their guidance in instrumental analysis. My special thanks go

to Dr. Charles A. Sims, Dr. Robert P. Bates and Dr. Ramon C. Littell for sharing their

expertise and contributing to my dissertation.

I also would like to thank my dear friends Gogce and Stefan for their help

throughout my research. My special thanks go to my dad and mom for their invaluable

support and making life easier for me, through all stages of my doctoral work.

El Salvador Farms (Homestead, FL) provided the coconuts, their contribution is

appreciated.
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ......... .................................................................................... iii

LIST O F TA BLE S ......... ................... ... ............ .............. .. vii

LIST OF FIGURES ......... ........................................... ............ xi

ABSTRACT .............. .......................................... xiv

CHAPTER

1 IN TR OD U CTION .............. .......................... ............. ..................... ....

2 LITER A TU RE REV IEW ............................................................... ........................ 4

Coconut W ater: Composition and Characteristics......................................................4
F lavor A naly sis ............................................... 8
Introduction ................................................................. 8
Instrum mental A analysis .................... ............ .................... 10
Gas chromatography/olfactometry (GC/O)................................................10
Solid phase m icroextraction (SPM E) .............. .. ................. .....................11
S en so ry A n aly sis ......................................................... ............ .................... 12
Coconut Flavors ... ..................................... .... ..............................13
Therm al Processing M ethods .................................. .....................................15
Pasteurization .................................... .. .. ........ .. ............15
U ltrapasteurization ............................... .................. ................ ............. 15
U ltra H igh Tem perature (UH T)...................................... ......................... 15
Heat Pasteurization of Juices................. ...................................16
N on-therm al Processing M ethods......................................... .......................... 17
Dense Phase Carbon Dioxide Technology .....................................................19
Mechanisms of Microbial Inactivation by DPCD......................... ............19
pH low ering effect..................... .... ............................................. ......2 0
Inhibitory effect of molecular CO2 and bicarbonate ion............. ..............22
Physical disruption of cells ....................... .... ............... ............... .... 23
Modification of cell membrane and extraction of cellular components.......25
Inactivation of Vegetative Cells by DPCD .................................... ..................27
Inactivation of Spores by D PCD .............................................. ............... 34
Inactivation of Enzymes by DPCD ....................... .....................................37
D PCD Treatm ent System s........................................................ ............... 41









DPCD Food Applications and Quality Effects..............................................45
Objectives of the Study ........ ..................................... .......... ................. 48

3 M ATERIALS AND M ETHOD S........................................................ ............... 49

Preliminary Experiments with Coconuts...................................... ..............49
Juice Extraction and Initial Quality Tests ................................. ............... 49
Pinking of Coconut W ater ............... ..... ... .... ..... .. .................49
Tests with Commercial Coconut Water Drinks................................................51
Extraction of Coconut Water from Coconuts..... ............................51
Formulation of Coconut Water Beverage..... ................... ...............52
D PCD Processing Equipm ent.......................................................... ............... 53
Continuous-flow DPCD System..... ..................... ...............53
Cleaning of the Equipment ........... ..... ......... ................... 53
Heat Pasteurization Equipment............................................................ ..................... 54
Carbonation Equipm ent ................................................................. ....................... 55
Optimization of DPCD Treatment Conditions for Microbial Reduction ..............56
Aging of Coconut W ater .................................. .....................................56
E x p erim ental D esign ........................................ ............................................56
Storage Study ........... ............................ 58
Microbial Tests .................. ........ ..................59
p H ..........................................................................6 0
Titratable A cidity (% TA ) ............................................................................. 60
B r ix ............................................................................................................... 6 0
C o lo r ....................................................................................................6 0
Sensory Evaluation .............................................. .... .... .. ........ .... 61
F lav or A n aly sis ............................................... ................ 62
D ata A n aly sis............................. ...................................................... ............... 64

4 RE SU LTS AN D D ISCU SSION ........................................................................ ...... 65

Formulation of Coconut W ater Beverage............................................................. 65
Objective 1: Quantification of Microbial Reduction in Coconut Water as a
Function of Treatm ent Conditions............................. ....................................... .65
Objective 2: Evaluation of Physical, Chemical and Microbial Quality of DPCD
Treated Coconut Water Beverage during Storage ...................................70
Objective 3: Comparison of Untreated Control, DPCD and Heat Treated Coconut
Water by Sensory Evaluation............................................78
Objective 4: To Identify Flavor Compounds in Coconut Water and Compare
Flavor Profile of DPCD and Heat Treated Coconut Water ...............................87

5 C O N C L U SIO N S ....... .......................................................................... ....... ...... .. 94

APPENDIX

A RESULTS OF PRELIMINARY TESTS WITH COCONUT WATER.....................98

B BOX-BEHNKEN EXPERIMENTAL DESIGN, DATA and ANALYSIS................ 102









C GC/O AND GC/MS FLAVOR ANALYSIS DATA AND RESULTS ....................106

D STORAGE STUDY: MICROBIAL, CHEMICAL AND PHYSICAL QUALITY
D A T A .............................................................................................1 17

E STORAGE STUDY TASTE PANELS: DATA AND ANALYSIS...........................125

L IST O F R E FE R E N C E S ......... .. ............. .............................................................. 151

B IO G R A PH ICA L SK ETCH ............ .................................................... .....................160
















LIST OF TABLES


Table page

2-1. A summary of contents for coconut water and human blood plasma .....................7

2-2. Chemical and physicochemical composition of green coconuts.............................7

2-3. Mineral composition of tender coconut water .....................................................8

2-4. Volatile compound classes and their sensory characteristics ....................................9

2-5. Non-volatile compound classes and their sensory characteristics.............................10

2-6. Summary of the studies on inactivation of various microorganisms.........................32

2-7. Summary of studies on spore inactivation by DPCD............................ ............36

2-8. Summary of studies on inactivation of enzymes by DPCD ....................................39

3-1. Three factor-3 level Box-Behnken experimental run coded variables and
c o n d itio n s ......................................................................... 5 7

3-2. Temperature programming conditions used for GC/O runs with DB-5 and
Carbowax columns. ........................................ ... .. ........ .... ............64

4-1. Log microbial reductions at each experimental point determined by Box-
B ehnken design ........................................................................67

4-2. Comparison of overall mean values for sensory attributes from different
treatm ents (a=0.05). ..................... ...... ............ ................. .... ....... 86

4-3. The percentages of panelists answering "yes" to the question: Would you buy
th at p ro d u ct? ....................................................... ................ 8 6

4-4. The percentages of panelists answering "no" the first purchase intent question
and answering still "no" the second purchase intent question: Would you buy
this product if you knew coconut water had rehydrating properties? ...................87

4-5. The list of flavor compounds that were identified in untreated fresh coconut
w ate r ...................................... .................................................... 8 9









4-6. Standard chemicals (10 ppm of each in a mixture) that were run in GC/O with
D B -5 colu m n ...................................... .............................. ................. 9 1

4-7. Standard chemicals (100 ppm each in a mixture) that were run in GC/O with
C arbow ax column n ...................... .................. ................... .... ....... 92

4-8. The descriptors given by sniffers for the flavor compounds identified in coconut
w ate r ...................................... .................................................... 9 2

A-1. Initial aerobic plate count (APC) and yeast and mold (YM) counts for coconut
water from eight immature green coconuts .............................. ..... ............98

A-2. Day 9 aerobic plate count (APC) and yeast and mold (YM) counts for coconut
water from selected coconuts of eight immature green coconuts ..........................98

A-3. Preliminary pinking test 1: Visual observation of the color of coconut water after
different treatments during storage at 40C in glass tubes.............. ...................99

A-4. Preliminary pinking test 2: Visual observation of the color of coconut water after
different treatments during storage at 40C in opaque plastic cups .........................99

A-5. Preliminary pinking test 3: Visual color observation of untreated, heat treated or
aerated coconut water during storage in glass tubes at 4C. .................................100

A-6. The pH, Brix and ingredients of commercially available coconut water
beverages ..................................... ................................. ......... 100

B-1. The average initial and final aerobic plate counts (APC) + standard deviations at
15 experimental runs from 3-factor, 3-level Box-Behnken experimental design..102

B-2. SAS software code used for the response surface methodology (RSM) analysis
of 15 experimental runs determined by Box-Behnken experimental design .........103

B-3. SAS software output of the response surface methodology (RSM) regression
analysis of 15 experimental-run data determined by Box-Behnken experimental
design including variables Xl (coded variable for Temperature), X2 (coded
variable for Pressure) and X3 (coded variable for %CO2 level)............................103

B-4. SAS software output of the response surface methodology (RSM) regression
analysis of 15 experimental-run data determined by Box-Behnken experimental
design including variables XI (coded variable for Temperature) and X3 (coded
variable for % C O 2 level) .................................................................................. 104

C-1. Excel output of alkane standards' linear retention index (LRI) calculations in
GC/O with a Carbowax column................................ ........................ ......... 106

C-2. Excel output of alkane standards' linear retention index (LRI) calculations in
G C /O w ith a D B -5 colum n............................................................................. 107









C-3. Retention times (RT), linear retention indices (Wax LRI) and GC/MS degree of
match values of four mixed group of standard chemicals that were run in
GC/M S for possible confirm ation ....................................................... .............. 111

C-4. Flavor compounds identified in coconut water through GC/O runs: Retention
times, calculated Linear Retention Indices (LRI's) and aroma descriptors given
by sniffers in GC/O runs with DB-5 and Carbowax columns.............................113

C-5. Peak areas of the sniffed compounds (olfactory port responses) and the aroma
descriptors given by sniffers for DPCD treated (25C, 34.5 MPa, 13% CO2, 6
min) and carbonated coconut water samples in GC/O with Carbowax column ....114

C-6. Peak areas of the sniffed compounds (olfactory port responses) and the aroma
descriptors given by sniffers for heat treated (74C, 15 s) and carbonated
coconut water in GC/O with Carbowax column ....................................................115

D-1. Total aerobic plate counts (APC) of untreated, DPCD treated (34.5 MPa, 25C,
13% CO2, 6 min) and heat treated (74C, 15 s) coconut water during storage
(4 C ) .............................................................................................1 17

D-2. Excel outputs of one-tail t tests conducted for comparison of mean aerobic plate
counts (APC) and yeast and mold (YM) counts for week 0 and week 9 samples. 117

D-3. Aerobic plate counts (APC) and yeast and mold (YM) counts of sterile distilled
water before and after carbonation with the Zalhm carbonator ..........................120

D-4. Yeast and mold (YM) counts of untreated, heat treated (74C, 15 s) and DPCD
treated (34.5 MPa, 25C, 13% C02, 6 min) coconut water beverages during
sto ra g e .......................................................................... 12 0

D-5. The pH of untreated, DPCD treated and heat pasteurized samples during storage 120

D-6. SAS software output of analysis of variance (ANOVA) for the pH data of
different treatm ents from the storage study............................................................121

D-7. The Brix of untreated, DPCD treated and heat pasteurized samples during
sto ra g e .......................................................................... 1 2 1

D-8. SAS software output of analysis of variance (ANOVA)for Brix data of different
treatm ents from the storage study ............................................... ............... 122

D-9. Titratable acidity (as % malic acid (w/v)) of untreated, DPCD treated and heat
pasteurized coconut water beverages during storage ...........................123

D-10. SAS software output of analysis of variance (ANOVA) for % titratable acidity
data of different treatments from storage study ............................................... 123









D-11. The mean L*, a*, b* values of untreated, DPCD (34.5 MPa, 25C, 13% CO2,6
min) and heat treated (74C, 15 s) coconut water beverages during storage .........124

E-1. Taste panel data output obtained by Compusense software: Sensory evaluation
scores of treatments during the storage study (Evaluation score scales: Overall
likeability: 9 point scale; Aroma difference and taste difference from control: 15
cm line scale; Off flavor: 6 point scale; Purchase intent and ask again: 1=Yes
and 2=N o) ...................................... ............................... ........ ...... 12 5

E-2. SAS software output of analysis of variance (ANOVA) for "overall likeability"
data for untreated, DPCD and heat treated coconut water by panelists ...............44

E-3. The weekly mean "overall likeability" scores for untreated, DPCD and heat
pasteurized sam ples during storage ...................................... ......... ............... 144

E-4. SAS software output of analysis of variance (ANOVA) for "aroma difference
from control scores" (corrected data) of different treatments during storage study 145

E-5. The weekly mean "aroma difference from control" scores for untreated, DPCD
treated (34.5 MPa, 25C, 13% C02, 6 min) and heat treated (74C, 15 s) coconut
w ater during storage (4 C ) ............................................. ............................ 145

E-6. SAS software output for analysis of variance (ANOVA) for "taste difference
from control scores" (corrected data) of different treatments during the storage
stu d y ..............................................................................14 6

E-7. The weekly mean "taste difference from control" scores for untreated, DPCD
treated (34.5 MPa, 25C, 13% C02, 6 min) and heat treated (74C, 15 s) coconut
w ater during storage (4 C ) ............................................. ............................ 146

E-8. SAS software output for analysis of variance (ANOVA) of "off flavor" scores of
different treatm ents during storage study...............................................................147

E-9. The weekly mean "off flavor" scores for untreated, DPCD treated (34.5 MPa,
25C, 13% C02, 6 min) and heat treated (74C, 15 s) coconut water during
sto rag e (4 C ) ...................................... ............................. ................ 14 7

E-10. Sample ballots that were used in sensory panels throughout the storage study
(Output obtained by Compusense software). ................................. ............... 148















LIST OF FIGURES
Figure page



2-1. Cross section of coconut (Cocos nucifera) fruit.............. ........... ............... 4

2-2. Coconut producing areas of the w orld ......... .......................................................... 5

2-3. Measured and calculated pH of pure water pressurized with CO2 up to 34.5 MPa ..20

2-4. Scanning electron micrographs (SEM) of untreated (a) and DPCD treated (b)
S .cerevisia e cells ................................................... ................ 2 5

2-5. Transmission electron micrographs (TEM) of untreated (a) and DPCD (b,c)
treated L.plantarum cells at 7 MPa, 30C, 1 h .................................................. 26

2-6. A typical batch DPCD system ........... .................................. 42

2-7. A continuous micro-bubble DPCD system .................................... ............... 43

2-8. A continuous CO2 membrane contactor system ..................................................44

2-9. A continuous flow DPCD system ..................................................................45

3.1. Schematic drawing of heat pasteurization equipment ........ ................................ 55

3-2. Schematic drawing of steps followed in preparation of storage study samples ........59

4-1. Geometry of the 3-factor 3-level Box-Behnken design......................................66

4-2. Plots of the response surface for the quadratic model with the variables XI:
Temperature (coded) and X3: %CO2 level (coded) .............................................. 69

4-3. Total aerobic plate counts (APC) of untreated control, DPCD and heat treated
coconut water during storage (DPCD treatment at 250C, 34.5 MPa,13% CO2 for
6 m in; H eat treatm ent at 740C for 15 s)......................................... ............... 71

4-4. Yeast counts of untreated control, DPCD and heat treated coconut water during
storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatm ent at 740 C for 15 s) .............................................. ............................. 71









4-5. The pH of untreated, DPCD and heat treated coconut water during storage
(DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at
7 4 C fo r 1 5 s) ...................................................................... 7 3

4-6. The Brix of untreated, DPCD and heat treated coconut water during storage
(DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at
7 4 C fo r 1 5 s) ...................................................................... 7 4

4-7. Titratable acidity (as % malic acid (w/v)) of untreated, DPCD treated and heat
pasteurized samples during storage (DPCD treatment at 250C, 34.5 MPa, 13%
CO2 for 6 min; Heat treatment at 740C for 15 s) .............. .....................................76

4-8. Mean L* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatm ent at 740 C for 15 s) .............................................. ............................. 77

4-9. Mean a* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatm ent at 740 C for 15 s) .............................................. ............................. 77

4-10. Mean b* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatm ent at 740 C for 15 s) .............................................. ............................. 78

4-11. Comparison of overall likeability of each treatment during storage .....................80

4-12.The frequency histograms of storage study aroma difference from control scores
of untreated (control) sam ples.......................................... ............................ 82

4-13.The frequency histograms of storage study taste difference from control scores
of untreated (control) sam ples.......................................... ............................ 83

4-14. Comparison of treatments for aroma difference from control scores during
sto rag e .............................................................................. 8 4

4-15. Comparison of treatments for taste difference from control scores during storage .84

4-16. Comparison of treatments for off flavor scores during storage............................85

4-17. Comparison of aromagrams of DPCD (25C, 34.5 MPa, 13% CO2, 6 min) and
heat (74C, 15 s) treated carbonated coconut water beverages obtained from
olfactory port responses (2 weeks storage at 40C). ............................................. 93

A-1. Pictures of coconut water from eight immature green coconuts at day 0 (left) and
d ay 9 (rig ht) ...................... ........................................................... 9 9

A-2. Pictures showing the steps of extraction of coconut water from coconuts ...........101









C-1.Plot of the formula relating the LRI's to the retention times for aroma compounds
in GC/O with a Carbowax column............... .............. ......... .. ............. 106

C-2. Plot of the formula relating the LRI's to the retention times for aroma
compounds in GC/O with a DB-5 column.................... ..............................107

C-3. An example of GC/MS peak identification using National Institute of Science
and Technology (N IST) library database..............................................................108

C-4. GC/MS chromatograms of the four mixed groups of standard chemicals that
were run in GC/M S for a possible confirmation...................................................110

C-5. Sample GC/MS chromatograms obtained by running fresh coconut water
sam ples. ................................... ........................... ...... ........... 112

C-6. GC/MS chromatograms of DPCD treated (coconut0011 and coconut0013) and
heat treated (coconut0013 and coconut0014) coconut water beverages
(carbonated) ..................................... ................................ .......... 116















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

PROCESSING OF COCONUT WATER WITH HIGH PRESSURE CARBON
DIOXIDE TECHNOLOGY

By

Sibel Damar

August 2006

Chair: Murat O. Balaban
Major Department: Food Science and Human Nutrition

Coconut water, the clear liquid inside immature green coconuts, is highly valued

due to its nutritional and therapeutic properties. It has been successfully used in several

parts of the world for oral rehydration, treatment of childhood diarrhea, gastroenteritis

and cholera. This juice is mostly consumed locally as fresh in tropical areas since it

deteriorates easily once exposed to air. Commercially, it is thermally processed using

ultra high temperature (UHT) technology. However, coconut water loses its delicate fresh

flavor and some of its nutrients during heating. A non-thermal process is desirable to

protect the fresh flavor and nutrient content of coconut water, which would increase

marketability of this healthy drink and availability to consumers throughout the world.

This study evaluated the effects of dense phase CO2 (DPCD) pasteurization on sensory,

physical and chemical quality of a coconut water beverage. The coconut water beverage

was formulated by acidification with malic acid to pH around 4.30, sweetened with

Splenda (0.7% w/w) and carbonated at 1.82 atm CO2 at 40C. Microbial reduction was









quantified as a function of pressure, temperature and % CO2 level. Optimum DPCD

treatment conditions for microbial inactivation were determined to be 13% C02, 25C,

34.5 MPa for 6 min. Quality attributes such as pH, Brix, % titratable acidity (%TA) and

color of DPCD treated, fresh and heat pasteurized (74C for 15 s) coconut water

beverages were measured and compared throughout refrigerated storage (4C for 9

weeks). DPCD treatment did not cause a change in pH or Brix. The color of coconut

water eventually turned pink during storage, independent of treatment. Sensory panels

showed that DPCD treated coconut water was liked as much as fresh coconut water;

whereas heat pasteurized coconut water was significantly less liked (ca=0.05) at the

beginning of storage. Flavor compounds of immature coconut water were identified.

Flavor profiles showed that heat treated coconut water had more aroma active compounds

than DPCD treated coconut water.

This study showed that a fresh-like tasting coconut water beverage can be produced

by DPCD technology with an extended shelf-life of more than 9 weeks at 40C.














CHAPTER 1
INTRODUCTION

Coconut water, as a tropical fruit juice, is highly valued and consumed in tropical

areas since it is tasty and has desirable nutritional and therapeutic properties. The total

world coconut cultivation area was estimated in 1996 at 11 million hectares (ha), and

around 93% was found in the Asian and Pacific regions (Punchihewa and Arancon

2005). Indonesia, the Philippines, and India are the largest producers of coconut in the

world. Coconut (Cocos nucifera Linn.) fruit is filled with the sweet clear liquid "coconut

water" when the coconut is about 5 to 6 months old. Coconut water has been called the

"fluid of life" due to its medicinal benefits such as oral rehydration, treatment of

childhood diarrhea, gastroenteritis and cholera (Kuberski 1980, Carpenter and others

1964). It is high in electrolyte content and has been reported as an isotonic beverage due

to its balanced electrolytes like sodium and potassium that help restore losses of

electrolytes through skin and urinary pathways. Coconut water was claimed as a natural

contender in the sports drink market with its delicate aroma, taste and nutritional

characteristics together with the functional characteristics required in a sports drink (Food

and Agricultural Organization [FAO] 2005).

The constituents of coconut water are water 94% (w/v), sugars such as glucose,

fructose and sucrose around 5% (w/v), proteins around 0.02% (w/v) and lipids only about

0.01% (w/v). It is rich in minerals such as potassium, calcium, magnesium and

manganese, and low in sodium.









Most coconut water is consumed fresh in tropical coastal areas due to its short

shelf-life. Once exposed to air, it loses most of its sensory and nutritional characteristics

and deteriorates. Commercially, juice production is carried out mostly in Indonesia, the

Philippines, and Thailand using ultra high temperature (UHT) sterilization while some of

coconut water's nutrients and its delicate flavor are lost during this thermal processing

(FAO 2005), which limits the product's marketability.

Usually juices are pasteurized by a low temperature long time (LTLT) process at

about 145F (63C) for 30 min or a high temperature short time (HTST) process at about

162F (72C) for 15 s. Resulting shelf-life is about 2 to 3 weeks under refrigeration

(lower than 7C). Heat treatment can cause significant reduction in physical, nutritive and

sensory quality of foods. Flavor changes in foods due to heating have been reported by

many studies (Shreirer and others 1977, Shaw 1982, Bell and Rouseff 2004). Non-

thermal processing methods have been receiving an increasing interest as alternative or

complementary processes to traditional thermal methods because they minimize quality

degradation by keeping the food temperature below the temperatures used in thermal

processing.

Dense phase CO2 (DPCD) technology is a non-thermal method emerging as an

alternative to traditional thermal pasteurization. It is a cold pasteurization method that

does not use heat to destroy microorganisms and enzymes, but instead uses the molecular

effects of CO2 at pressures lower than 50 MPa. Therefore, DPCD pasteurized foods are

not exposed to adverse effects of heat, and are expected to retain their fresh-like physical,

nutritional and sensory qualities.









The lethal effects of CO2 under high pressure on microorganisms have been

investigated since the 1950's. Carbon dioxide is suitable for use in foods since it is a non-

toxic, non-flammable, and an inexpensive gas. It is a natural constituent of many foods,

and has generally recognized as safe (GRAS) status. The study of Fraser (1951) is the

first research showing that CO2 can inactivate bacterial cells under high pressure. Since

then many researchers investigated effects of DPCD on microorganisms (pathogenic and

spoilage organisms, vegetative cells and spores, yeasts and molds), enzymes, and quality

attributes of foods. Within the last two decades, the number of research studies and

patents has increased, and commercialization efforts intensified. DPCD is one of the

emerging non-thermal technologies that satisfied FDA's requirement of 5 log pathogen

reduction for juice manufacturers.

DPCD technology has a great potential for use in the fruit juice industry especially

for tropical fruits that have limited availability to consumers throughout the world. This

study evaluated the use of DPCD technology with coconut water regarding microbial

inactivation, and physical, chemical and sensory quality evaluation. Objectives of this

study included quantification of microbial inactivation as a function of DPCD treatment

conditions, evaluation of beverage quality during storage, comparison of DPCD treated

coconut water beverage with fresh and heat treated coconut water beverages, and finally

the identification of flavor compounds in coconut water and comparison of flavor profiles

for heat treated and DPCD treated beverages. The demonstrated quality retention and

shelf-life extension in coconut water with DPCD technology would increase its

marketability and availability to the consumer.















CHAPTER 2
LITERATURE REVIEW

Coconut Water: Composition and Characteristics

The coconut (Cocos nucifera Linn.) fruit, egg-shaped or elliptic, consists of a

fibrous outer layer called coconut husk (mesocarp), which covers a hard layer called shell

endocarpp). Inside the shell is a kernel endospermm), which is considered the most

important part of the fruit. It is the source of various coconut products such as copra, i.e.,

the dried meat of mature fruit with 5% water content, coconut oil, coconut milk, coconut

water and coconut powder. The cavity within the kernel contains coconut water (Figure

2-1) (Woodroof 1979). This part begins to form as a gel when the coconut is about 5 to 6

months old, becomes harder and whiter as coconut matures, and the inside is filled with

coconut water (Oliveira and others 2003). An immature coconut between 6 to 9 months

contains about 750 mL of water that eventually becomes the flesh (FAO 2005).



Endosperm

SPericarp

Mesocarp





Endocarp


Figure 2-1. Cross section of coconut (Cocos nucifera) fruit









Total world coconut cultivation area in 1996 was estimated at 11 million hectares

(ha), and around 93% is found in Asian and Pacific regions (Figure 2-2) (Reynolds 1988).

The two biggest producers, Indonesia and the Philippines, have about 3.7 million ha and

3.1 million ha, respectively. India is the third largest producer. In the South Pacific

countries, Papua New Guinea is the leading producer. In Africa, Tanzania is the largest

producer while in Latin America Brazil accounts for more than one half of the total

coconut area for that region (Punchihewa and Arancon 2005).

ca- ..- ." ,;,, >.^




L"! "" [ -" :"



", -llr ----
'" r #' '.



m" 7



.i I. I
-



Figure 2-2. Coconut producing areas of the world

Coconut water has been called the "fluid of life" in many parts of the world due to

its medicinal benefits. It has been reported as a natural isotonic beverage due to

electrolytes like sodium and potassium, and its isotonic properties are demonstrated by its

osmol (the number of moles of osmotically active particles; 1 mole of glucose, which is

not ionizable, forms 1 osmol, 1 mole of sodium chloride forms 2 osmols) concentration,

which lies in the range of 300-330 mOsmol/kg (Gomes and Coelho 2005). With its high









electrolyte content, it has been studied for its potential use as an oral rehydration solution.

Comparison of coconut water with a "carbohydrate electrolyte beverage" resulted in

similar rehydration indices (SDcoconut 2005). There are many reports of its successful

use in gastroenteritis or diarrhea (Kuberski 1980). It is suggested as a readily available

source of potassium for cholera patients (Carpenter and others 1964). Coconut water

resembles blood plasma in its contents. Its successful intravenous use has been

documented (Falck and others 2000). During the Pacific War of 1941-45, coconut water

was siphoned directly from the nut to wounded soldiers for emergency plasma

transfusions (FAO 2005). Although its glucose, potassium, magnesium and calcium

levels are higher and sodium content is lower than blood plasma, studies on its

intravenous infusion show no allergenic or sensitivity reactions (Fries and Fries 1983). A

summary of the contents of coconut water and normal blood plasma is given in Table 2-1.

Campos and others (1996) determined the chemical and physicochemical

composition of a pool of coconut water from 30 green coconuts. They measured water

content, total solids, soluble solids, total sugars, reducing sugars, ash, protein, lipids, total

phenolics, total titratable acidity and turbidity (Table 2-2). Carbohydrates are the main

constituents of coconut water, and glucose and fructose are the most abundant soluble

solids in green coconuts, while sucrose is the main one in ripe coconuts (Oliveira and

others 2003).









Table 2-1 A summary cf co a


(
* Cited in Falck and others 2300
)


Table 2-2. Chemical and physicochemical composition of green coconuts
Water (g/100 mL) 94.21.90
Total solids (g/100 mL) 5.800.12
Soluble solids (Brix, 20C) 5.270.11
Total sugars (g/100 mL) 5.300.21
Reducing sugars (g/100 mL) 4.900.20
Non-reducing sugars (g/100 mL) 0.400.04
Ash (g/100 mL) 0.500.01
Protein (mg/100 mL) 19.500.50
Lipids (mg/100 mL) 11.000.60
Total phenolics (mg catechin/100 mL) 6.860.55
Total titratable acidity (mg citric acid/100 mL) 131.202.80
pH 5.200.10
Transmittance (%) 81.001.70
(Campos and others 1996)


Na K C1 Ca P4 Mg+2
Specific Glucose / Mg
Study pH meq/ meq/ meq/ meq/ meq/
y gravity L L L L L meq/
Pradera
and others 1.018 ---- 5.0 64 45.5 1.2 17 2.8 ----
1942*
Elseman
Elsema -- 5.6 4.2 53.7 57.6 1.8 9 2.4 17
1954*
Rajasurya 1.02 4.8 ---- 38.2 21.3 ---- 14.5 4.4 ----
1954*
DeSilva
DeSilva 1.02 4.9 ---- ---- ---- ---- ---- ---- 19
1959*
Olurin
lurn 1.02 5.6 0.7 81.8 38.6 ---- 3.6 3.2 25
1972*
Iqbal
1976l 1.019 5.6 5.0 49 63 2.1 12 8 4.7
1976*"
Kuberski
---- ---- 4.0 35.1 41 2.8 13.1 4 5.2
1979*
Msengi 1.023 6.0 2.9 49.9 ---- ---- 5.3 ---- 13.4
1985*
Atoiffi
t---- 4.2 9.7 43.1 39.8 1.73 ---- ---- ----
1997*
Normal
ma 1.027 7.4 140 4.5 105 0.1 5.0 2.0 1.8
plasma









Coconut water is rich in mineral composition (Table 2-3). It is high in potassium,

calcium, magnesium, and manganese, and low in sodium. Coconut water is low in fat and

proteins. It is rich in many essential amino acids such as lysine, leucine, cystine,

phenylalanine, histidine and tryptophan (Pradera and others 1942). Its arginine, alanine,

cysteine and serine percentage is higher than those of cow's milk (Maciel and others

1992). It contains ascorbic acid and B complex vitamins. Ascorbic acid content of

coconut water from a 7-9 month coconut has been reported to be 2.2 to 3.7 mg/100 mL

(Mantena and others 2003). Coconut water is low in calories with a caloric value of 17.4

kcal/100 g (Woodroof 1979).

Table 2-3. Mineral composition of tender coconut water

Minerals (mg /100 mL)
Copper 26
Potassium 290
Sodium 42
Calcium 44
Magnesium 10
Phosphorous 9.2
Iron 106
(Krishnankutty 2005)

Coconut water is mostly consumed fresh in tropical coastal areas today. In addition,

commercial juice production is carried out in Indonesia, the Philippines and Thailand by

heating with Ultra High Temperature (UHT). Although thermal processing eliminates

bacteria, it causes loss of the delicate flavor and some nutrients of coconut water.

Flavor Analysis

Introduction

Flavor is a combination of the perceived aroma, taste and trigeminal sensations

(Fisher and Scott 1997). Taste sensation has four major categories; sweet, sour, bitter,









and salty. Umami is included as the fifth category by some scientists. Trigeminal

sensations give the pungency, cooling or astringency. Taste and trigeminal components

of flavor are polar, non-volatile and water-soluble compounds. Aroma, on the other hand,

is created by the volatile compounds. A summary of the volatile and non-volatile

compounds and the examples of their sensory descriptors are given in Tables 2-4 and 2-5

(Fisher and Scott 1997).

Fruit flavors are a combination of sweet and sour tastes and the characteristic

aroma compounds. Sugars such as glucose, fructose and sucrose are responsible for the

sweetness of the fruit. Organic acids such as malic, citric, tartaric, etc. give sourness.

These compounds are common in most fruits. Most volatile constituents in fruits contain

aliphatic hydrocarbon chains, or their derivatives such as esters, alcohols, acids,

aldehydes, ketones and cyclic compounds such as lactones. These compounds are

reported as ripening products that develop from two different sources including fatty

acids by several lipid oxidation pathways, and amino acids via amino acid metabolism.

Generally, aromas of citrus fruits are created by terpenoids while that of non-citrus fruits

consists of esters and aldehydes (Fisher and Scott 1997).

Table 2-4. Volatile compound classes and their sensory characteristics
Compound class Sensory character Examples
Aldehydes Fruity, green, oxidized, Acetaldehyde, hexanal,
sweet decanal, vanillin
Alcohols Bitter, medicinal, piney, Linalool, menthol, a-terpineol,
caramel maltol
Esters Fruity Ethyl acetate, ethyl butyrate
Citrus Geraniol acetate
Ketones Butter, caramel Diacetyl, furanones
Maillard reaction products Brown, burnt, caramel, Pyrazines, pyridine, furans
earthy
Phenolics Medicinal, smokey Phenols, guaiacols
Terpenoids Citrus, piney Limonene, pinene, valencene
(Fisher and Scott 1997)










Table 2-5. Non-volatile compound classes and their sensory characteristics
Compound class Sensory character Examples
Acids:
Amino acids Sweet, sour, bitter
Organic acids Sour Citric, malic, tartaric
Polyphenolic acids Astringent, bitter Chlorogenic, caffeic
Flavonoids Astringent, bitter Flavonols, anthocyanins
Phenolics Medicinal, smokey Guaicols, phenols
Sweeteners:
Sugars Sweet, body Sucrose, glucose, fructose
High intensity sweeteners Sweet Aspartame, acesulfame-K
(Fisher and Scott 1997)

Instrumental Analysis

Gas chromatography/olfactometry (GC/O)

Gas chromatography (GC) is typically the method of choice for the analysis of

flavor compounds. Initial studies of flavor analysis were conducted using packed column

GC, which gave poor analytical results compared to today's capillary column GC.

Combining GC with mass spectrometry (GC/MS) allowed separation and identification

of numerous volatile compounds (Mistry and others 1997). It is possible to identify more

than 6900 volatile compounds by using these techniques. However, not all of these

volatiles have odor impact, only a few give the characteristic odor of the foods. GC

olfactometry (GC/O) is an important analytical tool in flavor research to characterize the

odors emerging from a sniffing port. GC/O allows the separation of odor active chemicals

from the volatile chemicals with no or minimal odor response.

Due to the complexity of the food matrix and aromas, and low concentration levels

of aroma compounds, typically in the parts per million (ppm), parts per billion (ppb) or

parts per trillion (ppt) ranges, generally isolation and concentration of the flavors are

needed prior to the analysis with GC. The most commonly used techniques are solvent

extraction, headspace sampling, and distillation methods. Each method has advantages









and disadvantages. For example, in headspace sampling, analytes are removed from the

sample without the use of an organic solvent. However, this method usually has low

sensitivity and can give poor quantitative results (Reineccius 1984). Headspace isolates

can be concentrated by the use of cryogenic or adsorbent traps. In cryogenic traps, water

is the most abundant volatile isolated from the food and should be removed by additional

steps that may cause sample contamination. Adsorbent traps offer advantage of water-

free isolates, but differential affinity of analytes for adsorbent can result in low sensitivity

for some chemicals. Solvent extraction is an accurate qualitative and quantitative method,

however, it can be laborious and its use is limited to fat-free foods. Although distillation

is an effective method, it takes a long time and impurities from the system components or

thermally induced chemical changes can be a problem. Recently, solid phase

microextraction (SPME) has found applications and is recommended as a convenient

method for sample preparation before GC analysis (Wardencki and others 2004).

Solid phase microextraction (SPME)

SPME is a relatively new sample preparation technique for rapid and solvent-free

extraction or pre-concentration of volatile compounds before analysis with GC. The key

component of SPME is the fused silica fiber coated with an adsorbent polymeric material.

This is an equilibrium technique and utilizes the partitioning of organic compounds in the

sample between the aqueous or vapor phase and the thin adsorbent film coating.

Adsorbed materials are thermally desorbed in a GC injection port. SPME is a simple,

rapid, solvent-free and inexpensive method when compared with other sample

preparation techniques such as solvent extraction, purge-and-trap, simultaneous

distillation/extraction and conventional solid-phase extraction (Yang and Peppard 1994).

Each additional step in the analytical procedure increases the possibility of analyte loss,









sample contamination and analytical error. SPME minimizes the number of steps used in

sample preparation by combining extraction and concentration steps. For volatile/semi-

volatile and non-polar/semi-polar analytes, SPME can reach detection limits of 5-50 pg/g,

with an approximate sample preparation time of 15-60 min (Wardencki and others 2004).

The effectiveness of the SPME depends on many factors such as type of fiber, sample

volume, temperature, extraction time, mode of extraction and desorption of analytes from

the fiber. The most commonly used commercially available fibers are non-polar

Polydimethylsiloxane (PDMS), semi-polar PDMS/divinylbenzene and polar

Carbowax/divinylbenzene.

Yang and Peppard (1994) used SPME liquid sampling and solvent extraction with

dichloromethane to extract flavor compounds of a fruit juice beverage and analyzed the

compounds by GC/MS. They showed comparable or higher sensitivity than solvent

extraction method for most esters, terpenoids and y-decalactone. They also analyzed a

vegetable oil for butter flavor by SPME headspace sampling and found that this

technique was effective in detection of diacetyl, 6-decalactone and 6-dodecalactone. They

reported that conventional headspace sampling method generally was more sensitive for

highly volatile compounds while the SPME headspace method picked up more of the less

volatile compounds.

Sensory Analysis

Flavor research studies the effect of changes in foods on flavor, and characterizes

these changes. Consumer acceptability or likeability of products developed with the new

technologies is a major tool for commercialization. Sensory evaluation of food provides









guidance for the maintenance, optimization and improvement of these products (Lawless

and Heymann 1998).

Sensory methods commonly used are separated into three groups: discriminant,

descriptive and affective methods. The method of choice depends on which questions are

to be asked about the product during the test. Discrimination methods answer whether

any difference exists between products, while descriptive tests answer how products

differ in specific sensory characteristics and provides quantification of these differences

(Lawless and Heymann 1998). Once differences are observed by discrimination type

tests, then descriptive tests can provide further information on the reasons for the

differences found. Affective tests are conducted to find out how well the products are

liked or which products are preferred. Examples of discrimination tests are triangle, duo-

trio and paired comparison tests. In some cases, difference-from-control test is used

instead of triangle or duo-trio tests, when the magnitude of difference from a control is

important (Miller and others 1998). This test not only assesses difference but also

quantifies the magnitude of difference.

Coconut Flavors

The desirable flavor of coconut water is sweet and slightly astringent, with a pH

around 5.6 (Maciel and others 1992). There are a limited number of studies on the

analysis of coconut flavor compounds. Lin and Wilkens (1970) identified 15 aroma

compounds in coconut meat by GC/MS analysis. Among these, 6-C8 and -C10 lactones

were the major volatile components and were described as buttery, tropical-fruity and

coconut-like. The other aroma compounds were octanal, 2-heptanol, 2-octanol, 2-

nonanol, 2-undecanol, hexanol, octanol, 2-phenylethanol, benzothiazole, ethyl decanoate









and dodecanoic acid, that were described mostly as fruity and also as nutty, rancid, green,

lemon and rose aromas.

Jayalekshmy and others (1991) determined aroma compounds of roasted coconut

meat by GC/MS. They suggested that roasting of coconut meat led to the formation of

heterocyclic aroma compounds, especially pyrazines. The 6-lactones, alcohols, esters and

fatty acids also contributed to the overall roasted coconut flavor. They isolated acid,

neutral and basic fractions from roasted coconut by selective solvent extraction and pH

adjustment. They identified pyrazines and other heterocyclic compounds, which gave the

roasted aroma, in the basic fraction. There were twenty different types of pyrazines

identified, and their amount increased with time of roasting. The GC profile of neutral

fraction was dominated by 6-lactones, and their amount decreased from 80% to 60%

during roasting. The basic and acid fractions were dominated by pyrazines and short

chain fatty acids, respectively.

Jirovetz and others (2003) identified aroma compounds in the coconut milk and

meat of ripe coconuts from Cameroon. They extracted headspace volatiles by SPME, and

identified more than thirty compounds using GC/MS. The main components of coconut

aroma were nonanal, nonanol, heptanal, ethyl octanoate, heptanol and 2-nonanol, while

coconut meat was rich in 6-octalactone, ethyl octanoate, nonanal, nonanoic acid, decanol,

decanal and nonanol. Other short chain alcohols, aldehydes, ketones, lactones, acids and

esters were present in lower concentrations. They did not detect any y-lactones or 6-C14

lactone that were reported in coconut meat by previous researchers. Although there are a

few studies regarding the flavor compounds in coconut meat or milk, there is no flavor

study with coconut water from immature fruit.









Thermal Processing Methods

Pasteurization

Pasteurization is a mild heat treatment for high-acid foods such as juices and

beverages, and low-acid refrigerated foods such as milk and dairy products. It is used in

order to inactivate vegetative cells of pathogenic microorganisms. Usually foods are

pasteurized by a low temperature long time (LTLT) process at about 145F (63C) for 30

min or a high temperature short time (HTST) process at about 162F (72C) for 15 s

(David and others 1996). The resulting shelf-life of the product is about 2 to 3 weeks

under refrigerated (lower than 7C) conditions. The pasteurization process does not

intend to inactivate all spoilage bacteria or any heat-resistant spores, thus the product is

not commercially sterile after pasteurization (David and others 1996).

Ultrapasteurization

The objective of ultrapasteurization is similar to pasteurization but it is done at

higher temperatures with shorter exposure times and extends the shelf-life about 6 to 8

weeks under refrigeration. Foods are ultrapasterized at 2800F (138C) or above for 2 s or

longer (David and others 1996). This process is usually used for dairy products, juices

and non-dairy creamers.

Ultra High Temperature (UHT)

Commercially sterile products are obtained by a UHT process at temperatures in

the range of 265 to 2950F (130 to 1450C) and holding times between 2 and 45 s. The

product is aseptically packaged after UHT processing in order to obtain a shelf stable

product with a shelf life of 1 to 2 years at ambient temperatures.









Heat Pasteurization of Juices

Common thermal processes used for juices and soft drinks are flash pasteurization,

hot filling, in-pack pasteurization and aseptic filling (Tompsett 1998, Lea 1998). Usually

flash pasteurization is done by passing juice rapidly through heated plates by HTST

treatment at 96C for 4 s, or by standard process at 800C for 20 s. In hot filling, the

product is heated in a heat exchanger above 80C (typically 87C), sent to the filler while

hot, filled into containers and held for about 2 min. Hot fill process is adequate for acidic

beverages to obtain a shelf stable product with a shelf-life of 6 to 12 months. In-pack

pasteurization is achieved by passing completely filled closed packs through a heating

and a superheated zone, and then through a pasteurizing zone for the desired period of

time, and finally through a cooling zone. Typical in-pack processing is done at 740C for

17 min. A special in-pack process is possible by heating the product above 100C in a

retort and then cooling (Lea 1998, Tompsett 1998). Aseptic filling may involve HTST

pasteurization or UHT sterilization, depending on the high-acid or low-acid character of

the juice, which is then filled into sterile containers in a sterile environment (David and

others 1996).

The choice of pasteurization method depends on the level of microbial

contamination of the raw materials and packaging, the ability of the product to withstand

heat, growth potential of microorganisms and the pH of the product. In orange and

tangerine juice processing, pasteurization does not only kill microorganisms but also

inactivates pectinesterase. Normally, temperatures above 71C are enough to kill

pathogens and spoilage bacteria in orange juice. However, temperatures between 86 and

99C are required to inactivate pectinesterase. In commercial practice, orange and

tangerine juices are flash pasteurized by heating the juice rapidly to about 92C for 1 to









40 s (Nordby and Nagy 1980). In lemon juice the pectinesterase enzyme can be

inactivated at lower temperatures (69 to 740C), and commercial pasteurization is done at

77C for 30 s.

Thermal processing methods have been shown to change the flavor of foods. For

example, the delicate flavor of fresh orange juice is easily changed by heat treatment.

Citrus processors and flavorists search for methods to make processed orange juice and

orange-flavored beverages taste more like fresh orange juice (Shaw 1982). Shreirer and

others (1977) reported that some volatile compounds such as a-terpineol and carveol,

which are formed by the oxidation of d-limonene, increased and the amount of terpene

hydrocarbons decreased in heat pasteurized orange juice.

Bell and Rouseff (2004) determined changes in the flavor of grapefruit juice after

heat processing. Sensory analysis of juices processed at 1000C for 10 min indicated

formation of a heated, pineapple, metallic, and cooked off-flavor, while the initial

unheated juice had a typical fresh grapefruit character. Analysis of flavor compounds by

GC/O showed that there was at least a 45% decrease in levels of volatile compounds

associated with fresh grapefruit juice after heat processing. A corresponding increase in

compounds associated with flavor degradation such as furaneol and methional was

observed after heating.

Non-thermal Processing Methods

Non-thermal processing methods have gained increasing interest in recent years

and several emerging technologies are under intense research to evaluate their potential

as alternatives to traditional thermal methods. Traditionally, most foods are preserved by

subjecting to temperatures between 60C to 1000C for a certain period of time (Barbosa-

Canovas 1998). The large amount of energy transferred to food during heat treatment









may initiate unwanted reactions and result in undesirable changes in the physical, sensory

and nutritional quality of food.

Quality degradation is minimized using non-thermal technologies since the food

temperature is held below the temperatures used in thermal processing. Among the

emerging non-thermal technologies are ultra high pressure (UHP), high intensity pulsed

electric fields (PEF), irradiation, oscillating magnetic fields, pulsed high intensity light,

and dense phase CO2 (DPCD). UHP and irradiation are being used in commercial

operations. One of the most important issues in the commercialization of non-thermal

technologies is regulatory approval. Processes must comply with pasteurization or

sterilization requirements of Food and Drug Administration (FDA) and also ensure the

safety of equipment operators and consumers. Each of these technologies can be used for

specific food applications; some are more suitable for liquid products whereas some are

appropriate for solids. It is important to determine the quality of non-thermally processed

foods, especially in cases where the nature of the food precludes use of thermal methods.

Evaluation of sensory, nutritional and physical changes resulting from non-thermal

processes is essential (Barbosa-Canovas 1998).

Several studies evaluated the quality of fruit juices processed by non-thermal

technologies. PEF treated orange juice had significantly higher (P<0.05) ascorbic acid,

flavor compounds and color than thermally processed orange juice (Hye and others 2000,

Min and others 2003). Jia and others (1999) showed that there was 10 to 40% loss in the

major orange juice flavor compounds after heat pasteurization while 0 to 5% losses

occurred for the same compound with PEF processing. Ayhan and others (2002) reported

that PEF processing did not alter sensory evaluation of flavor and color of fresh orange









juice. Similarly, Min and others (2003) reported higher sensory scores for flavor and

overall acceptability of PEF treated tomato juice compared to heat pasteurization. Apple

juice retained fresh like ascorbic acid levels and color after PEF processing (Akdemir and

others 2000, Liang and others 2003).

UHP processing at pressures between 100 to 800 MPa has been reported to be

effective in inactivation of pathogens without affecting taste or nutritional value of fresh

juices (Morris 2000). UHP treated citrus juices retained a fresh-like flavor with no loss of

vitamin C and a shelf-life of approximately 17 months (Farr 1990). Polydera and others

(2003) compared shelf-life and ascorbic acid retention of reconstituted orange juice

processed by heat at 80C for 30 s with that of UHP processed juice (500 MPa, 35C, 5

min). UHP processing resulted in 24% to 57% increase in the shelf-life compared to

thermal pasteurization. Sensory characteristics of UHP pasteurized juice were rated

superior and ascorbic acid retention was higher.

FDA's juice HACCP regulations require validation of 5 log pathogen reduction for

juice manufacturers. Dense phase CO2 (DPCD) is one of the emerging non-thermal

technologies that conforms to this requirement and has a great potential for commercial

use in juice pasteurization.

Dense Phase Carbon Dioxide Technology

Mechanisms of Microbial Inactivation by DPCD

Several hypotheses have been proposed to explain the lethal effects of DPCD on

microorganisms. Although the exact means are not clear, studies show that several

mechanisms may be involved. DPCD was claimed to inactivate microorganisms by:












pH lowering effect

CO2 can lower pH when dissolved in the aqueous part of a solution by forming

carbonic acid. Carbonic acid further dissociates to give bicarbonate, carbonate and H'

ions lowering extra-cellular pH by the following equations:


CO2 + H20 < H2CO3


H2CO3 <- H + HCO3


HCO3 < H+ + CO3 2


pKa = 6.57


pKa = 10.62


Meyssami and others (1992) predicted the pH of simple model liquid foods under

DPCD and obtained good correlations with the experimentally measured pH values. They

found that the presence of dissolved materials other than CO2 such as acids and salts had

a reducing effect on the lowering of pH by DPCD treatment (Figure 2-3).


7.
SMeasured pH
6 Predicted pH

5-

4 -
pH


2 -


0 10 20 30
Process Pressure, P(MPa)


Figure 2-3. Measured and calculated pH of pure water pressurized with CO2 up to 34.5
MPa









However, the internal pH of microbial cells, not the external pH, has the largest

effect on cellular destruction. When there is a sufficient amount of CO2 in the

environment, it can penetrate through the cell membrane, which consists of phospholipid

layers, and lowers internal pH by exceeding the buffering capacity of the cell. Normally,

cells have to maintain a pH gradient between the internal and external environments.

Cellular systems actively pump hydrogen ions from the inside to the outside of the cell.

These systems can be overwhelmed with sufficient CO2, reducing internal pH. It is

believed that reduced internal pH may inactivate microorganisms by the inhibition of

essential metabolic systems including enzymes (Daniels and others 1985, Ballestra and

others 1996). Ballestra and others (1996) measured the activities of eight enzymes from

E.coli cells before and after DPCD treatment (5 MPa, 15 min, 35C). These enzymes

were selectively inactivated. The activity of some enzymes having an acidic isoelectric

point such as alkaline phosphatase and P-galactosidase disappeared, whereas those with

basic isoelectric points such as acid phosphatase were slightly affected.

Hong and Pyun (2001) treated L.plantarum cells by DPCD under 7 MPa at 30C

for 10 min, and measured activity of 13 different enzymes. They also observed that

enzymes were inactivated selectively. Some enzymes such as cystine arylamidase, a-

galactosidase, a- and P--glucosidase lost their activities significantly, whereas enzymes

such as lipase, leucine arylamidase, and acid and alkaline phosphatase were little affected

by DPCD treatment. At the same time, cell viability of L.plantarum decreased by more

than 90% under these conditions. They concluded that it was uncertain whether the

observed inactivation of some enzymes was a primary cause of cell death. Evidence in









the literature does not specify which of the enzymes mentioned are critical for survival

and therefore vital in their inactivation.

Inhibitory effect of molecular CO2 and bicarbonate ion

Another suggestion to explain inactivation of bacterial enzymes is the inhibitory

effect of CO2 itself on some enzymes (Ishikawa and others 1995a). Weder (1990) and

Weder and others (1992) claimed that under a low pH environment, arginine could

interact with CO2 to form a bicarbonate complex, and inactivate the enzyme containing

this amino acid. Jones and Greenfield (1982) have shown that decarboxylases are

inhibited by excess CO2, breaking the metabolic chain (Spilimbergo and Bertucco 2003).

Ishikawa and others (1995a) obtained complete inactivation of alkaline protease

and lipase at 350C and 15 MPa treatment by using a micro-bubble system. They

compared residual activity of these enzymes by supercritical CO2 (SCCO2) to that of low

pH (3.0) and concluded that alkaline protease could be inactivated due to pH lowering by

dissolved CO2; whereas lipase must have been inactivated by a different mechanism.

They also conducted a study with glucoamylase and acid protease, showing that a higher

SCCO2 density resulted in lower residual activity of these enzymes. As a result, they

claimed that inactivation of these enzymes could be caused by the sorption of CO2 into

the enzyme molecules.

Another mechanism proposed is precipitation of intracellular calcium and

magnesium ions by the effect of carbonate (Lin and others 1993). When the applied CO2

pressure is released, bicarbonate converts to carbonate, which can precipitate intracellular

calcium, magnesium and similar ions from the cell and cell membrane. Calcium-binding

proteins are known as the most important class involved in intracellular regulation

(Aitken 1990). Certain types of calcium- and magnesium-sensitive proteins could be









precipitated by carbonate, depending on the binding site of the ion and chemical structure

of the protein. Consequently, a lethal change to the biological system is produced (Lin

and others 1993).

Physical disruption of cells

The first suggested mechanism of inactivation of microorganisms by DPCD was

the physical disruption of cells (Fraser 1951). E.coli cells were almost totally killed under

50.7 MPa in less than 5 min and were thought to burst due to the rapid release of applied

gas pressure and the expansion of CO2 gas within the cell during depressurization.

However, the extent of bursting of cells was determined by the Petroff-Hauser counting

method that uses a microscope for direct cell counting. Therefore, it is hard to conclude if

the cells were actually burst without observation with an electron microscope. Other

researchers investigated the physical rupture of cells by DPCD as a possible mechanism

of inactivation (Lin and others 1991, Nakamura and others 1994, Isenschmid and others

1995, Ishikawa and others 1995b, Ballestra and others 1996, Dillow and others 1999,

Hong and Pyun 1999, Spilimbergo and others 2003, Folkes 2004). Lin and others (1991)

claimed that yeast (S.cerevisiae) cells could be ruptured by pressurized CO2 under 6.9-

34.4 MPa for 5 to 15 h treatments. They measured total protein concentration in the

supernatant of treated cells as an indication of cell rupture, however they did not have a

direct physical observation of cells. They have shown that the amount of total proteins

released in the supernatant of DPCD treated cells was about the same amount as in the

supernatant of cells autolyzed by other disruption methods. The leakage of the proteins

into the environment depends on the size of the breach in the cell membrane. Nakamura

and others (1994) demonstrated mechanical rupture of yeast cells by DPCD treatment (4

MPa, 40C for 5 h) by scanning electron micrographs. They observed that some cells









were completely burst whereas some only lost surface smoothness and had some wrinkles

or holes on the membrane surface. Folkes (2004) also observed physical disruption of

yeast cells in beer by scanning electron micrographs (Figure 2-4). The process conditions

in a continuous dense phase CO2 pasteurizer were: pressure 27.5 MPa, temperature 21C,

CO2/beer ratio (10%), and residence time of 5 min.

Although cell rupture is possible during DPCD treatment, it is not necessary for

cell inactivation. For instance, Ballestra and others (1996) treated E.coli cells at 5 MPa

and 35C, and observed that more than 25% of cells had intact cell walls while the

viability was only 1%. They did not observe cell rupture or burst, but only some signs of

deformation in cell walls. There have been studies showing that cells were completely

inactivated even when they remained intact after treatment. For example, Hong and Pyun

(1999) demonstrated that L. plantarum cells treated with CO2 at 6.8 MPa and 30C for 60

min were completely inactivated but SEM micrographs did not show any cell rupture.

The morphological changes caused by DPCD may differ based on treatment conditions,

gas release rate, or the type of microorganism. Dillow and others (1999) observed that

SEM micrographs of S.aureus (Gram(+)), P.aeruginosa (Gram(-)), and E.coli (Gram(-))

cell walls were largely unchanged before and after DPCD treatment. However, they

found that Gram(-) cells had more defects on the cell wall after treatment. They explained

this by Gram(-) cells having thinner cell walls compared tocompared to Gram(+) cells.






















Figure 2-4. Scanning electron micrographs (SEM) of untreated (a) and DPCD treated (b)
S.cerevisiae cells

It is important to note here that cells without any rupture, i.e., with intact cell walls

could show modifications or damage in microstructural observations.

Modification of cell membrane and extraction of cellular components

Another mechanism suggested by researchers is based on the lipo- and

hydrophilicity and solvent characteristics of CO2. Kamihira and others (1987) mentioned

extraction of intracellular substances such as phospholipids by DPCD as one of the

possible mechanisms of microbial inactivation. Isenchmid and others (1995) proposed

that molecular CO2 diffused into cell membrane and accumulated there, since the inner

layer is lipophilic. Accumulated CO2 increased fluidity of the membrane due to the order

loss of the lipid chains, also called the "anesthesia effect", and the increase of fluidity

causes an increase in permeability. Lin and others (1992) suggested that once CO2 has

penetrated into the cell, it could extract cellular components and transfer extracted

materials out of the cell during pressure release. Upon extraction of essential lipids or

other vital components of cells or cell membranes, the cells are inactivated. These

hypotheses have been investigated by several researchers either by measuring the amount









of materials in the supernatant of treated cells or by some microstructural observations on

the treated cells.

Hong and Pyun (1999) have shown that although SEM observations of L.plantarum

cells had demonstrated intact cell walls after DPCD treatment, microstructural

observations by transmission electron micrographs (TEM) showed modifications in the

cell membrane with possible leakage of cytoplasm (Figure 2-5). These pictures show

enlarged periplasmic space between cell walls and the cytoplasmic membranes, and

empty spaces in the cytoplasm. In a further study in 2001, Hong and Pyun have shown

that cells treated with DPCD at 7 MPa for 10 min and 30C showed irreversible cellular

damage including loss of salt tolerance, leakage of UV-absorbing substances, release of

intracellular ions and impaired proton permeability. They have also used Phloxine B

staining on L.plantarum cells as an indication of loss of cell membrane integrity, and

shown that cell membrane has lost its integrity immediately after being exposed to high

pressure CO2.












Figure 2-5. Transmission electron micrographs (TEM) of untreated (a) and DPCD (b,c)
treated L.plantarum cells at 7 MPa, 30C, 1 h

Although the strongest effect of the above mechanisms on microbial destruction by

DPCD is still in question, researchers agree in the governing role of CO2. Several

researchers have concluded that CO2 has a unique role in inactivation of cells (Haas and









others 1989, Wei and others 1991, Lin and others 1992, Nakamura and others 1994,

Ballestra and others 1996, Dillow and others 1999, Hong and Pyun 2001).

Haas and others (1989) observed that altering external pH by acids such as

phosphoric and hydrochloric did not cause as much cell inactivation as CO2. These acids

cannot enter cells easily as CO2. This implied that the ability of CO2 to penetrate through

the cell membrane has a key role in reducing the internal pH of cells. Similarly, Wei and

others (1991) added 0. IN HC1 to the Listeria suspension to decrease pH by about 1.8

units. The same amount of pH reduction was achieved by treatment of cells with CO2

under 6.18 MPa for 2 h. The acidification by HC1 did not cause a microbial reduction

whereas treatment with CO2 caused complete inactivation.

Alternatively, Nakamura and others (1994) have shown that N2 gas when applied

under the same conditions as CO2 (4 MPa, 40C, 4 h) did not have an effect on viability

of yeast cells. Lin and others (1992) have shown that 90% of cells survived after

treatment with N2 under 6.9 MPa for 20 and 40 min whereas complete inactivation was

achieved after treatment with CO2 in less than 12 min. Similarly, Dillow and others

(1999) applied tetrafluoroethane (TFE) to bacterial cells at 38C and 11 MPa for 45 min

and compared the viability of cells with treatment of CO2 under the same conditions.

Although TFE did not result in reduction of viable cells, total inactivation was achieved

by CO2 treatment.

Inactivation of Vegetative Cells by DPCD

There are a number of studies showing that DPCD is effective in killing vegetative

forms of pathogenic and spoilage bacteria, yeasts and molds. A summary of these studies

is given in Table 2-6 including the media, treatment conditions, microorganisms, their log

reduction, and the type of system used. The microbial inactivation achieved by DPCD









changed from 2 and 12 logs, pressures under 50 MPa, and temperatures between 5C to

600C, mostly in the 25-350C range. Treatment times were significantly different

depending on the treatment system used and could be as long as 6 h when batch systems

were used, to as low as 2.5 min for continuous or semi-continuous systems.

Water activity (aw) of treatment medium and water content of the vegetative cells

were shown to have a significant role in the killing effect of DPCD. Kamihira and others

(1987) compared inactivation of wet (70-90% water) and dry (2-10% water) cells of

Baker's yeast, E.coli and S.aureus by DPCD treatment at 20 MPa for 2 h and 350C. Dry

cells were inactivated by less than 1 log whereas wet cells were inactivated by 5 to 7 logs.

Haas and others (1989) showed that DPCD was more effective as aw of the food

increased. Kumagai and others (1997) studied sterilization kinetics of S. cerevisiae cells

at various water contents and CO2 pressures. The first order sterilization rate constant, k,

was almost zero at water contents below 0.2 g/g dry matter, and increased with increasing

water content. This increase was slight at water contents above Ig/g dry matter.

Moreover, k increased with increasing CO2 pressure at an identical water content of cells.

Similarly, Dillow and others (1999) compared inactivation kinetics of E.coli cultures in

the presence and absence of water in the cell culture when treated with DPCD at 340C

and 14 MPa. They observed that small amounts of water greatly enhanced the sterilizing

effect of DPCD. It is important to note that water content of treatment medium and

therefore the water content of the cells would increase CO2 solubility in the cells, which

would explain increased microbial inactivation.

The unique role of CO2 in the inactivation of microbial cells has been shown by

many researchers and the details of their studies were listed in the "Mechanisms" section.









Generally, any effect that increases the level and rate of CO2 solubility, and therefore,

penetration of CO2 into cells in a treatment medium enhances microbial inactivation by

DPCD. For instance, CO2 solubility increases with increasing pressure, other conditions

being equal. However, this increase is limited by the saturation solubility of CO2 in the

medium. Generally, inactivation efficiency increases with higher pressure, temperature

and residence time. Nakamura and others (1994) demonstrated that the bactericidal effect

of CO2 treatment on baker's yeast dramatically increased by increasing pressure from 2

to 4 MPa, by increasing temperature from 20 to 40C, and by increasing treatment time

from 0.5 h to 3 h. Hong and others (1999) achieved a 5 log reduction for L.plantarum by

DPCD at 30C. It took 50 to 55 min to achieve this reduction at 6.9 MPa while it took

only 15-20 min to achieve the same level of reduction at 13.8 MPa. Isenschmid and

others (1995) showed that viability ofKluveromycesfragilis, S.cerevisiae, and Candida

utilis decreased with increasing CO2 pressure following a typical S-shaped curve. Sims

and Estigarribia (2002) showed that once the treatment medium is fully saturated with

CO2, the killing effect of DPCD did not change significantly with the enhancing effects

of pressure or temperature. For example, 7.5 MPa was nearly as effective as 15 MPa, and

room temperature was as effective as 3 1C in reducing E.coli cells by using a membrane

contactor system. This can be explained by the rapid increase of CO2 solubility in water

with increasing pressure up to 7.5 MPa, but pressure increases above 7.5 MPa result in

small increases in solubility (Dodds and others 1956).

On the other hand, temperature has a more complex role in increasing microbial

inactivation by DPCD. Although solubility of CO2 decreases with increasing

temperatures, higher temperatures can increase the diffusivity of CO2 and the fluidity of









cell membrane that facilitate penetration of CO2 into the cells. Another important effect

of temperature is the phase change of CO2 from sub-critical to supercritical conditions

(Tc = 31. 1C). The penetrating power of CO2 is higher under supercritical conditions, and

there is a rapid change in solubility and density of CO2 by temperature at the near-critical

region. Hong and Pyun (1999) observed that under a constant pressure of 6.8 MPa,

microbial inactivation of L. plantarum increased by a log as temperature decreased from

40C (7 log reduction) to 300C (8 log reduction). They explained this less effective

inactivation at 400C by the decrease in solubility of CO2 in this region.

Initial pH of treatment medium is an important factor affecting microbial reduction

by DPCD. Low pH environment facilitates penetration of carbonic acid, like many other

carboxylic acids (Lindsay, 1976) through the cell membrane, therefore more inactivation

is achieved as the medium pH decreases. For example, Hong and Pyun (1999)

demonstrated that under a CO2 pressure of 6.8 MPa at 300C, treatment of 25 min in

acetate buffer (pH 4.5), 35 min in sterile distilled water (pH 6.0) and 60 min in phosphate

buffer (pH 7.0) were required to achieve 5 log reduction of L.plantarum cells.

Cell growth phase or age is another factor affecting inactivation of microbial cells

by DPCD. Young cells are more sensitive than mature ones. Hong and Pyun (1999)

compared inactivation of L.plantarum cells in log phase with those in stationary phase,

and found that cells in the late log phase were more sensitive to DPCD than those in the

stationary phase. They attributed this to the ability of bacteria entering the stationary

phase of growth to synthesize new proteins that protect cells against adverse

environmental conditions (Koltter 1993, Mackey and others 1995).









Different types of bacteria have different susceptibilities to DPCD treatment. It is

hard to make comparisons since the treatment systems, solutions or conditions also differ

in these studies. Referring to specific studies, it can be concluded that some

microorganisms seem more affected by DPCD treatment. For example, Sims and

Estigarribia (2002) showed that Lactobacillusplantarum cells were more resistant to

DPCD than E.coli, S. cerevisiae and Leuconostoc mesenteroides cells. Dillow and others

(1999) treated G(+) bacteria (S. aureus, B. cereus, L. innocua) and G(-) bacteria (S.

salford, P. vulgaris, L. dunnifii, P. aeruginosa and E.coli) with DPCD. They found that

B.cereus cells were more resistant to DPCD while E.coli and P.vulgaris were more

sensitive. They suggested that the nature of the cell wall could be an important factor in

the difference in sensitivity of these bacteria. Because of their thin cell walls, G(-)

bacteria are expected to be more sensitive and their cell wall could be ruptured more

easily than that of the G(+) bacteria. More studies need to be conducted in this area to

have a clear conclusion.

The type of system used for DPCD treatment can change the microbial inactivation

rate by DPCD. Treatment systems that allow better contact of CO2 with the treatment

solution are shown to be more effective in microbial reduction because of the more rapid

saturation of the solution with CO2. Usually, batch systems require longer treatment times

in order to be effective in microbial inactivation compared to continuous systems. On the

other hand, it is possible to increase the inactivation rate of batch systems by agitation

(Lin and others 1993, Hong and Pyun 2001). Spilimbergo and others (2003) showed that

a semi-continuous system is more efficient than a batch system. Treatment of 40 to 60

min was necessary for inactivation of a wide range of bacteria with the batch system,










whereas less than 10 min was enough by using a semi-continuous system. Ishikawa and

others (1995b) obtained more than 4 orders and 3 orders higher inactivation in L.brevis

cells and S.cerevisiae, respectively, by using a micro-bubbling filter in their system.

Table 2-6. Summary of the studies on inactivation of various microorganisms
Solution Microorganism P Time Temp. System Log Reference
(MPa) (0C) redn.
aPS S.cerevisiae 20 2 h 35 Batch 7.5 Kamihira and
(9C) others 1987
Ecoli 20 2h 35 6.5 (C)
S.aureus 20 2h 35 5 (C)
A.niger 20 2h 35 5 (C)
Herbs Total bacteria 5.52 2 h 45 Batch 5-8 (C) Haas and
count others 1989
Apple juice Total bacteria 5.52 30 min 45 >3 (C)
count
Orange juice Total bacteria 5.52 30 min 55 4 (C)
count
Nutrient E.coli 6.21 2 h Room 2
broth temp.
S.aureus 6.21 2 h Room 2
temp.
Salmonella 6.21 2 h Room 2
seftenberg temp.
Distilled L.monocytogenes 6.18 2 h 35 Batch 9 (C) Wei and
water others 1991
Egg yolk S.thyphimurium 13.7 2 h 35 >8
Orange juice Total plate count 33 1 h 35 Batch 2 Arreola and
(TPC) others 1991b
Growth S.cerevisiae 6.9 15min 35 Batch 7 (C) Lin and others
medium 1992
Growth L.dextranicum 6.9 15-20 35 Batch >8 Lin and others
medium 21min min 1993
Sterile water S.cerevisiae 4 >3 h 40 Batch 8 (C) Nakamura and
others 1994
PS L.brevis 25 30 min 35 Micro- 6 (C) Ishikawa and
bubble others 1995b
S.cerevisiae 25 30 min 35 6 (C)
PS E.coli 5 20 min 35 Batch 6 (C) Ballestra and
others 1996
Sterile S. cerevisiae 15 1 h 40 Batch 8 Kumagai and
Water others1997
bMRS broth Lactic acid 6.9 200 min 30 Batch 5 Hong and
bacteria others 1997

"BHIB S.aureus 8 60 min 25 Batch 7 (C) Erkmen 1997
Whole milk Aerobic plate 14.6 5h 25 Batch >8
count











Table 2-6 Continued
Solution Microorganism P Time Temp. System Log Reference
(MPa) (0C) redn.
dSB w/ B.cereus 20.5 4 h 60 Batch 8 (C) Dillow and
polymers others 1999
L.innocua 20.5 0.6 h 34 9 (C)
S.aureus 20.5 4 h 40 9 (C)
S.salford 20.5 4 h 40 9 (C)
P.auruginosa 20.5 4 h 40 8 (C)
Ecoli 20.5 0.5 h 34 8 (C)
P.vulgaris 20.5 0.6 h 34 8 (C)
L.dunnifi 20.5 1.5 h 40 4 (C)
Growth L.plantarum 13.8 30 min 30 Batch >6 (C) Hong and
medium others 1999
ePS with L.monocytogenes 6 75 min 35 Batch 6.98 (C) Erkmen
broth 2000a
PS E.faecalis 6.05 18 min 35 Batch 8 (C) Erkmen
2000b
Fruit juice- E.faecalis 6.05 3-6 h 45 5 (C)
milk
PS Brocothirix 6.05 100 35 Batch 5.5 (C) Erkmen
thermosphacta min 2000c
Skinned Brocothirix 6.05 150 35 Batch 5 (C)
meat thermosphacta min
MRS broth L.plantarum 7 100 30 Batch >8 Hong and
min Pyun 2001
PS S.thyphimurium 6 15 min 35 Batch 7 (C) Erkmen and
Karaman
2001
PS w/broth S.thyphimurium 6 140 25 Batch 7 (C)
min
Whole milk E.coli 10 6 h 30 Batch 6.42 (C) Erkmen 2001
Skim milk E.coli 10 6 h 30 Batch 7.24 (C)
PS B.subtilis 7.4 2.5 min 38 SCh 7 (C) Spilimbergo
and others
2002
P.aeruginosa 7.4 2.5 min 38 7 (C)
Sterile water E.coli 7.5 5.2 min 24 CM' 8.7 Sims and
Estigarribia
2002
Orange E.coli 15 4.9 min 24 >6
Juice
Orange juice L.mesenteroids 15 <10 25 >6
min
Orange juice S.cerevisiae 15 <10 25 12
min
Orange juice L.plantarum 7.5 <10 35 >8
min
Orange juice S.thyphimurium 38 10 min 25 CF 6 Kincal and
others 2005
Orange juice L.monocytogenes 38 10 min 25 6
Orangejuice E.coliO157:H7 107 10 min 25 5
Apple juice E.coliO157:H7 20.6 12 min 25 5.7









Table 2-6 Continued


apS: Physiological Saline, bMRS: De Man Rogosa Sharpe, cBHIB: Brain-Heart Infusion
Broth, TSB: Tryptic Soy Broth, eCF: Continuous flow, fWM: Watermelon, gC:
Complete inactivation, hSC: Semi-continuous, 'CM: Continuous membrane

Inactivation of Spores by DPCD

Spores are highly resistant forms of bacteria to the physical treatments such as heat,

drying, radiation and chemical agents (Watanabe and others 2003a). A limited number of

studies in the literature investigating inactivation of spores by DPCD show that the extent

of inactivation achieved changes with treatment conditions, treatment systems and the

type of organism (Table 2-7).

Studies suggested that processing temperature had a significant role in inactivation

of spores by DPCD. Several researchers observed that a temperature threshold should be

exceeded in order to achieve a killing effect on bacterial or fungal spores (Enomoto and

others 1997, Ballestra and Cuq 1998, Watanabe and others 2003b). This threshold

temperature can be different for different spores. Kamihira and others (1987) did not

observe any killing effect of DPCD on B. ,,/i iil iheimehihlii spores and observed only

53% inactivation of B.subtilis spores by DPCD treatment at a relatively low temperature

(35C). Enomoto and others (1997) showed that there was not a significant inactivation of

B.megaterium spores at temperatures below 50C, and survival ratio of spores decreased

dramatically by increasing temperature from 50 to 600C. On the other hand, Ballestra and

Cuq (1998) did not observe antimicrobial activity of DPCD treatment on B.subtilis spores


Solution Microorganism P Time Temp. System Log Reference
(MPa) (oC) redn.
Carrot juice Aerobic plate 4.9 10 min 5 Batch 4 Park,and
count others 2002
WM Aerobic plate 34.4 5 min 40 CF 6.5 Lecky 2005
juice count
Mandarin Aerobic plate 41.1 9 min 35 CF 3.47 Lim and
juice count others 2006
Coconut Aerobic plate 34.5 6 min 25 CF >5 Damar and
water count ______Balaban 2005









and Byssochlamysfulva ascospores below 80C, and on A.niger conidia below 50C.

Similarly, Watanabe and others (2003b) observed that DPCD treatments at temperatures

in the range of 35C to 850C did not have a killing effect on Geobacillus

stearothermophilus spores. However, it may be possible to achieve significant amounts

of spore inactivation at relatively low temperatures by using continuous DPCD treatment

systems that are shown to be more efficient than batch systems. For instance, Ishikawa

and others (1997) achieved 6 log reduction in B. polymyxa, B.cereus, and B. subtilis

spores at 450C, 50C and 55C, respectively, by using a continuous micro-bubble system.

Micro-bubbling by the use of a filter improved the inactivation of spores by 3 log cycles.

There was only 1 log reduction of spores without micro-bubbling and 4 log reduction

with micro-bubbling under the same treatment conditions.

The mechanism of inactivation of spores by DPCD is not known. Watanabe and

others (2003a) compared the killing effect of DPCD with heat and high hydrostatic

pressure (HHP) treatments. DPCD had more lethality than HHP treatment or heat

treatment alone, showing that CO2 had a unique role in inactivation. They suggested that

inactivation mechanisms of bacterial spores by DPCD and heat were different, since

inactivation of Bacillus spores by heat treatment occurred in a single step whereas

inactivation by DPCD occurred in two steps. Ballestra and Cuq (1998) also observed two

steps in the inactivation of B.subtilis spores at 5 MPa CO2 and 80C. They suggested that

the first step of inactivation could represent penetration of CO2 into the cells that is

associated with heat activation of the dormant spores. Heat activation can make spores

more sensitive to the antimicrobial effects of CO2. However, there may be another

explanation for spore inactivation by DPCD based on the study of Furukawa and others










(2004). This study believes that DPCD is able to germinate bacterial spores even at

relatively low treatment temperatures. Approximately, 40% of B. coagulans and 70% of

B. licheniformis spores were germinated by DPCD at 6.5 MPa and 35C. Therefore,

DPCD could be the reason for germination of spores making the resulting vegetative cells

more sensitive to heat inactivation. The study of Watanabe and others (2003a) shows that

inactivation of B. coagulans and B. licheniformis spores by heat treatment only is much

lower than inactivation obtained when a combination of DPCD and the same heat

treatment is applied. In the combined treatment, DPCD is applied first and heat is applied

afterwards. Their study suggests that DPCD may decrease heat tolerance of bacterial

spores. The calculated z values with and without DPCD were the same. However, the D

values with DPCD were much smaller, indicating an upward shift in the log inactivation

vs. time curve with DPCD. The role of DPCD and heat treatments in spore inactivation

needs to be investigated more explicitly.

Table 2-7. Summary of studies on spore inactivation by DPCD
Solution Microorganism Pressure Time Temp System Log Reference
(MPa) (oC) redn.
Sterile B.subtilis 20 2 h 35 Batch 0.3 Kamihira and
water others 1987
Growth P.roqueforti 5.52 4 h 45 Batch >6 Haas and others
medium 1989
Sterile B. megaterium 5.8 30 h 60 Batch 7 Enomoto and
distilled others 1997
water
aPS B. polymyxa 30 60min 45 Micro- 6 Ishikawa and
bubble others 1997
B.cereus 30 60min 50 6
B.subtilis 30 60min 55 6
Sterile B.subtilis 5 1 h 80 Batch 3.5 Ballestra and Cuq
Ringer 1998
solution B. fulva ascospores 5 1 h 80 Batch 0.7

Sterile B.stearothermophilus 20 2 h 35 Batch 0
water









Table 2-7 Continued
Solution Microorganism Pressure Time Temp System Log Reference
(MPa) (0C) redn.
Orange S.cerevisiae 15 <10min 45 Continuous >6 Sims and
juice ascospores Membrane Estigarribia 2002
filter
Alicyclobacillus 7.5 <10min 45 >6
acidoterretis spores
G.stearothermophilus 30 2 h 95 Batch 5 Watanabe and
others 2003b
aPS: Physiological saline
Inactivation of Enzymes by DPCD

Inactivation of certain enzymes that affect the quality of foods by DPCD has been

shown by several researchers (Balaban and others 1991a&b, Chen and others

1992&1993, Tedjo and others 2000, Park and others 2002). A summary of the literature

including the enzymes treated with DPCD, the amount of activity loss achieved in these

treatments and DPCD treatment conditions is given in Table 2-8. DPCD can inactivate

certain enzymes at temperatures where thermal inactivation is not effective (Balaban and

others 1991a). Among these enzymes, pectinesterase (PE) causes cloud loss in some fruit

juices; polyphenol oxidase (PPO) causes undesirable browning in fruits, vegetables,

juices and some seafood; lipoxygenase (LOX) causes chlorophyll destruction and off-

flavor development in frozen vegetables; and peroxidase (POD) has an important role in

discoloration of foods and is used as an index for efficacy of heat treatment in processing

fruits and vegetables. The PE, PPO, POD and LOX from various foods were shown to be

effectively inactivated by DPCD. Although the number of studies on enzyme inactivation

by DPCD is limited, the studies conducted so far point to the potential of DPCD

technology in especially fruit and vegetable juice processing, where these enzymes cause

quality deterioration if not inactivated.









Studies suggest that enzyme inactivation by DPCD could be due to several causes

such as pH lowering, conformational changes of the enzyme, and inhibitory effect of

molecular CO2 on the enzyme.

Balaban and others (1991a) studied the inactivation of PE in orange juice by

DPCD. The pH of orange juice must be lowered to 2.4 for substantial PE inactivation.

However, by DPCD treatment pH of orange juice was lowered only to 3.1. Therefore,

pH-lowering alone was not sufficient to explain enzyme inactivation by DPCD. The

results of Chen and others (1992) support their approach. They used a pH control and

measured the activity of lobster PPO that was kept under pH of 5.3, which is the same as

the pH of samples achieved by DPCD treatment. Although the pH control sample

retained 35% of its original activity at 350C after 30 min, DPCD treated enzyme lost its

activity after 1 min at the same temperature.

CO2 was suggested to have a unique role in the inactivation of enzymes. Balaban

and others (1993) applied the following treatments to orange juice and observed the

decrease of PE activity. Untreated control had a decrease in PE activity of 8% after 20

days storage. Supercritical CO2 treatment (31 MPa, 40C, 45 min) showed 31%

reduction; juice acidified with HC1 to pH=3.1 and pressurized with N2 (24 MPa, 40C, 45

min) had a 36% reduction; juice buffered to pH=3.8 with citrate buffer, then treated with

supercritical CO2 (31 MPa, 40C, 45 min) reduced PE by 23%; juice pressurized with N2

(20.6 MPa, 55C, 1 h) showed an increase in PE activity. These results suggest that the

buffered juice PE activity decreased only by the molecular effect of CO2, while the

unbuffered CO2 combined the effects of pH lowering and CO2 effects. Pressurized N2 did










not lower activity. Similarly, Chen and others (1993) have shown that N2 treatment

under the same conditions as CO2 treatment did not cause any inactivation of PPO.

Table 2-8. Summary of studies on inactivation of enzymes by DPCD
Enzyme Source of Pressure Time Temp System Loss of Reference
enzyme (MPa) (C) activity
(%)
Lipase Commercial 20 2 h 35 Batch 12-22 1
(62-68% water)
c-amylase Commercial 20 2 h 35 0
(62-68%
water)
Gluco- Commercial 20 1 h 35 Batch 0 2
amylase (5-7% water)
Catalase Commercial 20 1 h 35 10
(5-7% water)
Lipase Commercial 20 1 h 35 0
(5-7% water)
Glucose Commercial 20 1 h 35 0
isomerase (5-7% water)
PEa Orange juice 26.9 145 min 56 Batch 100 3
PPOb Spiny lobster 5.8 1 min 43 Batch 98 4
PPO Brown shrimp 5.8 1 min 43 78
PPO Potato 5.8 30 min 43 91
PPO Spiny lobster 0.1 30 min 33 Batch 98.5 5
LOXc Soybean 10.3 15 min 50 Batch 100 6
PODd Horseradish 62.1 15 min 55 100
LOX Soybean 62.1 15 min 35 95
PPO Carrot juice 4.9 10 min 5 Batch 61 7
LOX Carrot juice 2.94 10 min 5 >70
PPO Muscadine 27.6 6.25 min 30 Continuous 75 8
grape juice flow
aPE: Pectinesterase, "PPO: Polyphenol oxidase, CLOX: Lipoxygenase, dPOD: Peroxidase
Kamihira and others 1987, 2 Taniguchi and others 1987,3 Balaban and others 1991b,
4Chen and others 1992, Chen and others 1993, 6 Tedjo and others 2000, 7 Park and
others 2002, 8Del Pozo-Insfran and others 2006

DPCD can change isoelectric profiles and protein patterns of PPO (Chen and others

1992). However, these changes were not caused by CO2 under atmospheric pressure

(Chen and others 1993). Chen and others (1992) obtained Circular Dichroism spectra of

untreated and treated lobster, brown shrimp and potato PPOs. Their results showed that

DPCD caused conformational changes in the secondary structures (a-helix, P-sheet, 3-

turn and random coil) of the enzymes. High pressure is also reported to cause









conformational changes in protein and enzyme molecules (Suzuki and Taniguchi 1972).

On the other hand, Hendrickx and others (1998) reported that pressures around 310 MPa

can cause irreversible damage to the secondary structure of proteins, but pressures below

it cause no change or changes that are reversible upon depressurization. DPCD pressures

are very much lower than 310 MPa, therefore, the conformational changes occurring by

DPCD may not be caused by a high pressure effect. This needs to be confirmed by

further research.

Extent of enzyme inactivation by DPCD is affected by the type and source of the

enzyme, DPCD treatment conditions such as pressure, temperature and time, and

treatment medium properties. Balaban and others (1991a) observed that higher

temperatures and pressures of DPCD treatment results in higher %PE inactivation. An

enzyme isolated from different sources has different resistance to DPCD treatment, as is

the case with heat inactivation. For example, potato PPO is more resistant to inactivation

by DPCD compared to spiny lobster and shrimp PPOs (Chen and others 1992). The

presence of other soluble compounds in the treatment medium may have a protective

effect against DPCD treatment. Tedjo and others (2000) showed that %LOX and %POD

activity increased by increasing sucrose concentration up to 40%. This could be

explained by decrease in the solubility of CO2 as sucrose concentration increases.

DPCD treatment is reported to be more effective than heat treatment in enzyme

inactivation and can inactivate enzymes at much lower pressures compared to High

Hydrostatic Pressure, an alternative non-thermal processing method. Significant amounts

of inactivation of PE, PPO, LOX and POD are possible by DPCD at temperatures lower

than 55C. Park and others (2002) achieved significant inactivation of LOX and PPO in









carrot juice at a temperature as low as 5C. Heating at 550C for 30 min results in only

about 18% and 13% inactivation of LOX and POD, respectively, while DPCD results in

total inactivation of these enzymes after 15 min treatment at the same temperature.

DPCD Treatment Systems

Several batch, semi-continuous and continuous treatment systems have been

developed since the first DPCD applications. In a batch system, CO2 and treatment

solution are stationary in a container for a certain period of time during treatment. A

semi-continuous system allows a continuous flow of CO2 through the treatment chamber,

while a continuous system allows continuous flow of both CO2 and the treatment solution

through the system.

Most of earlier studies have been performed using batch systems. A typical batch

system consists of a CO2 gas cylinder, a pressure regulator, a pressure vessel, a water

bath or heater, a CO2 release valve, and a data logger (Figure 2-6) (Hong and Pyun 1999).

At the beginning of the operation, the sample solution is placed into the pressure vessel

and temperature is set to the desired value. Next, CO2 is introduced into the vessel until

the sample in the vessel is saturated at the desired pressure and temperature. The sample

solution is left in the vessel for a certain amount of time and then the CO2 outlet valve is

opened to release the gas. Some systems contain an agitator that decreases the time to

saturate the sample solution with CO2.


































Figure 2-6. A typical batch DPCD system

In 1995, Ishikawa and others (1995a) developed a semi-continuous micro-bubbling

system that uses a cylindrical filter to micro-bubble CO2 entering into the pressure vessel.

They showed that the use of the filter significantly increased the efficiency of the system.

They could achieve three times more inactivation of enzymes using a micropore filter

than without it. They also showed that using a filter increased the concentration of

dissolved CO2 in the sample from 0.4 to 0.92 mol/L at 25 MPa and 35C. In 1998,

Shimoda and others developed a continuous micro-bubble system that was very effective

in the inactivation of microorganisms (Figure 2-7). In this system, liquid CO2 and a saline

solution were pumped through a CO2 dissolving vessel at certain flow rates. Liquid CO2

was changed to gaseous state using an evaporator and then dispersed into the saline

solution from a stainless steel mesh filter with 10 [im pore size. The micro-bubbles of

CO2 moved upwards while dissolving CO2 into the saline solution. Then, the saline

solution saturated with CO2 was passed through a heater to reach the desired temperature


2 3 7 8






List of parts
6 1 -. C02 gas cylinder
2. pressure regulator
3. needle valve for C02 inlet
4. pressure vessel
5. water bath
6. thermostatic controller
7. needle valve for C02 outlet
8. line filter
9. thermocouple
10. pressure transducer
5 12 11. data logger
12. immersible stirrer









and a suspension of microorganisms was pumped into it at this point. Another coil with a

heater was used to adjust the residence time (Shimoda and others 2001).


Control
Physiological
Saline
Suspension of
Micro-organisms
Pump J Heater (2)
HeaterHeater (2)
SResidence Coil
Thermocouple

C02 Product
Control
Valve(ll)
Micropore
Pump Filter D
Drain

Figure 2-7. A continuous micro-bubble DPCD system

A continuous membrane contact CO2 system was developed by Sims in 2001

(Figure 2-8) (Sims and Estigarribia 2002). This system consists of four in series hollow

polypropylene membrane modules. Each tubular module has 15 parallel fibers of 1.8 mm

ID, 39 cm length and 83 cm2 active surface area. A CO2 pump is used to pressurize the

system, and the test liquid is pumped continuously into the system with a HPLC pump.

This setup is very efficient in saturating the liquid with CO2 since it provides a large

contact area between CO2 and the test liquid by the use of the membranes. In the

membrane contactor, CO2 is not mixed with the test liquid but instead diffuses into it at

saturation levels instantaneously. CO2 is recycled back and re-used.




























Figure 2-8. A continuous CO2 membrane contactor system

In 1999, Praxair (Chicago, IL) developed a continuous flow DPCD system (Figure

2-9). This system consists of CO2 tanks and a CO2 pump, a product tank and product

pump, a high pressure pump, holding coils, decompression valve and a vacuum tank. CO2

and the product are pumped through the system and mixed before passing through the

high pressure pump. This pump increases the pressure to the processing levels, and the

product temperature is brought to the desired level in holding coils. Residence time is

adjusted by setting the flow rate of the product passing through holding coils. At the end

of the process, an expansion valve is used to release CO2 from the mixture. It is possible

to pull out the remaining CO2 in the product by a vacuum tank. This system has been

shown to be very effective in killing pathogens and spoilage bacteria for short periods of

time (Folkes 2004, Damar and Balaban 2005, Kincal and others 2005, Lecky 2005, Lim

and others 2006).














SCC02
Ch le Hold tube ( C

C7



Heating /
system /
Expansion
valve
--- Pump -
0 Juice D Treated
stream juice


Figure 2-9. A continuous flow DPCD system

DPCD Food Applications and Quality Effects

DPCD has been applied mostly to liquid food products, particularly to fruit juices.

To date, there is no commercial food product processed by DPCD. There are a limited

number of published studies in the literature regarding the effect of DPCD on the quality

of foods including a few test results published by companies offering commercial

systems.

Among the first food applications of DPCD is treatment of whole fruits such as

strawberry, honeydew melon, and cucumber for inhibition of mold growth. Haas and

others (1989) demonstrated that although mold inhibition is possible by DPCD treatment

of fruits, DPCD may cause severe tissue damage in some fruits even at low pressures.

Studies with orange juice shows that DPCD treatment can improve some physical

and nutritional quality attributes such as cloud formation and stability, color and ascorbic

acid retention. Arreola and others (1991a) treated fresh orange juice with DPCD in a

batch system from 7 to 34 MPa, 35 to 600C and for 15 to 180 min time periods. They also









had temperature controls that were kept under the same temperatures for the same

amount of time without DPCD treatment. Ascorbic acid retention of DPCD treated

orange juice was between 71 to 98%. Ascorbic acid retention levels of DPCD treated

samples were significantly higher than that of temperature controls. Higher ascorbic acid

retention by DPCD was explained by the exclusion of 02 from the system and lower pH

of orange juice by DPCD. Ascorbic acid has higher stability under low pH and oxidizes

easily when oxygen is present in the environment. On the other hand, cloud of orange

juice was enhanced by 1.3 to 4.0 times after DPCD treatment compared to original

untreated orange juice. Cloud stability of orange juice treated by DPCD at 29 MPa and

50C for 4 h was retained after 66 days of refrigerated storage. However, temperature

controls (50C for 4 h) and room temperature controls (25C for 4 h) lost cloud

completely during refrigerated storage. In the same study, instrumentally measured color

scores showed that DPCD treated juice was brighter than untreated juice. Sensory

evaluation of DPCD treated and untreated juices indicated that there was no significant

difference in flavor, aroma and overall acceptability of these samples. The color and

cloudiness of DPCD treated juice were preferred over those of untreated juice.

Park and others (2002) treated carrot juice with a combined effect of 4.9 MPa

DPCD and 600 MPa ultra-high pressure. They observed reduction of pectin

methylesterase (PME) activity by 65%, and a cloud loss of 47%. This suggests that the

cloud loss in different food systems, even with the same enzyme (PME), could follow

different mechanisms, and cloud retention in e.g. orange juice does not necessarily imply

cloud retention in carrot juice.









Later studies using continuous systems also show nutritional and sensory quality

retention and improvements in the physical attributes of orange juice treated with DPCD.

Kincal and others (2006) obtained up to 846% cloud increase in orange juice by DPCD

treatment (38 MPa, room temperature, 10 min). There were no significant changes in pH

and Brix of treated samples. Small, but statistically insignificant increase in L* and a*

values of color occurred by DPCD. Sensory evaluations of DPCD treated and untreated

orange juice were not significantly different. Ho (2003) used the continuous flow system

of Praxair (Chicago, IL) and reported that there were no significant differences between

physical attributes (pH, Brix and titratable acidity), nutritional content (vitamin C and

folic acid) and aroma profile for untreated and DPCD treated orange juice.

Folkes (2004) used continuous DPCD technology for pasteurization of beer and

compared physical and sensory quality attributes of DPCD treated beer with that of fresh

(untreated) and heat pasteurized beer. The aroma and flavor of DPCD treated beer was

not significantly different from fresh beer even after 1 month storage at 1.670C, but heat

treated beer was found significantly different than others in taste and aroma at the end of

storage (ca=0.1). DPCD treated beer had significantly less foam capacity and stability

compared to heat pasteurized beer, but not at levels detrimental to the finished product

quality. On the other hand, beer haze was significantly reduced by DPCD.

Lim and others (2006) treated mandarin juice with DPCD using the continuous

flow system by Praxair and measured the pH, Brix, titratable acidity, cloud and color

after DPCD treatment at 13.8-41.4 MPa, 25-45C and 7-9 min. DPCD treatment

enhanced the cloud up to 38.4%, increased lightness and yellowness, and decreased









redness of mandarin juice. DPCD treated samples had higher titratable acidity than

untreated samples. The pH and Brix did not change after DPCD treatment (ca=0.05).

It is important to conduct studies regarding the consumer likeability of food

products that are processed by DPCD since the consumer is the target in

commercialization of this technology.

Objectives of the Study

The objectives of this study were:

i. To quantify microbial reduction in coconut water as a function of treatment conditions

such as pressure, temperature, time and CO2 level

ii. To evaluate quality of DPCD treated coconut water during storage

iii. To compare untreated fresh, DPCD- and heat-treated coconut water by sensory

evaluation

iv. To identify flavor compounds in coconut water and compare the flavor profile of

untreated, DPCD- and heat-treated coconut water














CHAPTER 3
MATERIALS AND METHODS

Preliminary Experiments with Coconuts

Juice Extraction and Initial Quality Tests

Eight immature green coconuts (Malaysian Dwarf) were obtained from Homestead,

FL. A V2 in Makita Drill (Buford, GA) was used to drill two holes on opposite sides of

coconuts and the water was poured into 1L glass bottles. Each bottle was numbered from

1 to 8 and stored in a refrigerator (4C). Weight of coconuts ranged between 1.85 to 2.40

kg and coconut water extracted from these ranged between 435 to 490 g. Brix was

between 6.3 and 6.6, while pH ranged between 5.35 and 5.50. Total aerobic plate counts

(APC) were between zero count and >190 cfu/mL, and there was no yeast and mold

(YM) growth initially (Table A-i). APC and YM counts were repeated at day 9 for

selected bottles and increase in counts were observed (Table A-2). Presence of PPO and

POX enzyme activity in coconut water was confirmed by following the method described

by Campos and others (1996).

Pinking of Coconut Water

Coconut water from the eight coconuts in each bottle changed color during

refrigerated storage. The pictures of the coconut water in each bottle at day 0 and day 9

were given in Figure A-1. Some of the bottles showed browning of coconut water at day

zero. This could be due to enzymatic browning that was accelerated by introducing

phenolic compounds from the outer surface of the green shell, as well as heating and

metal contact during drilling of the coconuts.









Preliminary tests were conducted to understand the mechanisms causing or

accelerating pinking in coconut water.

In Test 1, coconut water extracted from one immature green coconut was divided

into two. One part was placed into 20 mL glass test tubes and divided into five treatment

groups (three tubes/group). Treatments were control (no treatment)(1), frozen and thawed

at 4C the next day(2), N2 bubbling for 15 min (3), heating at 800C for 5 min (4) and

heating at 800C for 5 min while exposed to the air (5). All tubes were stored under

refrigeration (4C). Tubes were observed for color at days 0, 4, 7, 9 and 12. On day 7, one

of the three tubes from control, frozen/thawed and heating (closed caps) groups were

removed from that group and bubbled with air for 15 min. Color observation results are

given in Table A-3. Two out of three N2 bubbled tubes and all open heated tubes turned

pink earlier than others on day 4. On day 12, one tube of N2 bubbled and two unaerated

controls, frozen/thawed and heated (closed cap) tubes were still colorless whereas all

aerated tubes, open heated tubes and two N2 bubbled tubes were pink. Although it is hard

to draw a clear conclusion on the effect of heating or N2 bubbling based on this test,

aeration seems to accelerate pinking.

In Test 2, the second part of the coconut water was placed into 50 mL opaque

plastic cups and were exposed to different treatments such as ascorbic acid (100 ppm)(1),

potassium metabisulfite (40 ppm)(2) or 0. IN HC1 addition to lower the pH to 4.0(3) and

3.0(4). Two cups were untreated and used as control (pH=4.8). The color observations

were done every day until day 12 and also 3 months later (Table A-4). Control cups

turned pink on day 12 whereas others were still colorless. At the end of 3 months, all









cups other than ascorbic acid added and potassium metabisulfite added cups turned pink.

Ascorbic acid and potassium metabisulfite seem to stabilize the color of coconut water.

Test 3 was conducted to observe the effect of aeration and heating on pinking of

coconut water. Coconut water extracted from a coconut was divided into 20 mL glass

tubes, three tubes in each treatment. Treatments were control (1), heated at 850C for 5

min (2), boiled for 5 min (3), and air bubbled for 15 min (4). Color observations on day 6

and day 10 showed that all heated and aerated tubes eventually turned pink, whereas

control tubes were still clear at the end of 10 days refrigerated storage (Table A-5). These

results suggested that aeration and heating might accelerate pinking. It is unlikely that

pinking is due to microorganisms since boiling did not prevent it.

Tests with Commercial Coconut Water Drinks

In order to understand some properties of commercially available coconut water,

six different brands of coconut water drinks were obtained from the market and their

sensory evaluation was made by an informal tasting. The measured pH and Brix values

and the contents of these products are given in Table A-6. Four of these products were in

aluminum cans while two others were in Tetrapak boxes. The pH of these drinks changed

between 4.12 and 5.16 while the Brix was in the range of 5.6 to 10.8. Cooked, metallic,

soapy and artificial coconut flavors were recognized by some of the panelists. Products

with lower Brix were mostly found to have a bland or no taste whereas the ones with

higher Brix were usually found to be too sweet.

Extraction of Coconut Water from Coconuts

About 1,140 immature green coconuts (Cocos nucifera, Malaysian Dwarf) were

obtained from growers (El Salvador Farm) in Homestead, Florida. Coconuts were left in

a commercially available bleach solution (1.0% (v/v)) and rinsed with water before









cutting. Washed coconuts were passed through a band saw and cut horizontally in one

end. The liquid inside was taken to a clear glass bottle by the use of a peristaltic pump,

and checked for color, smell and taste. Any turbid, or abnormally colored liquid was

discarded. Clear liquid was placed in 3 gallon plastic pail containers that were kept in ice.

Once each pail was full, the juice was frozen at -200C immediately in order to prevent

any microbial or enzymatic activity. This procedure was used to mix juices from many

coconuts and make the sample homogeneous as much as possible. Although all the

coconut water could not be mixed into one batch, the liquid was a representative of a

broad number of coconuts. During experiments whenever needed, the pails were taken

randomly into 40C cold room and thawed. Pictures in Figure A-2 show steps used in

extraction of coconut water.

Formulation of Coconut Water Beverage

Preliminary tests were performed to determine the necessity for acidification,

sweetening and carbonation of coconut water. Safety considerations against C. botulinum

required acidification. Food grade citric acid (Presque Isle, North East, PA), malic acid

(Presque Isle, North East, PA) and pHase (Jones-Hamilton, Walbridge, OH) were

compared by preliminary tasting for their suitability to sweeten coconut water. Malic

acid was chosen as the most suitable acid and added to coconut water to lower the pH to

4.30. Malic acid is naturally present in coconut water and was preferred over citric acid

and pHase by the panelists. Preliminary tasting showed that a sweetener was needed to

compensate for the sourness caused by acidification. Splenda (McNeil-PPC, Fort

Washington, PA), which is basically a chemically modified form of sucrose, was used as

the sweetener and the amount was determined by informal tasting. Splenda has no

caloric value and was preferred over other artificial sweetners because it gives relatively









higher sweetness (600 times that of sucrose) and lack of strong aftertaste. Brix of

coconut water did not change after Splenda addition. Finally, carbonated coconut water

was compared to non-carbonated for likeability by informal tasting. Carbonation was

done at 40C and 1.82 atm CO2 pressure. It was decided to carbonate coconut water after

acidification and sweetening because carbonated samples were preferred over non-

carbonated by panelists.

DPCD Processing Equipment

Continuous-flow DPCD System

A continuous high pressure CO2 machine of 55.16 MPa pressure and about 0.8

liters/min flow rate capacity (Praxair Co., Chicago, IL) was used for pasteurization of

coconut juice. The components of the system and their functions were described in

section "DPCD treatment systems" of Chapter 2.

The system was run at a juice flow rate of 417 g/min in order to obtain 6 min

residence time in the holding tube (79.2 m length and 0.635 cm ID). Sterile water was run

through the system until the desired levels of pressure, temperature and CO2 level were

reached. Coconut water was then poured in the juice tank and the first 3.5 L of coconut

water were discarded. Approximately 1 L of treated coconut water was collected into a

sterile 1 L glass bottle at the exit valve. Processed coconut water was cooled down

immediately at 40C until further use. Whenever the treatment parameters were changed,

sterile water was run through the system until the desired levels were reached. The

equipment was cleaned after each use as described below.

Cleaning of the Equipment

Oxonia and Principal solutions (Ecolab, St. Paul, MN) were the chemicals used to

sanitize the equipment. Concentrations of solutions were determined as 0.38% Principal









and 0.44% Oxonia solutions (v/v) with the help of an Ecolab representative. The

equipment was first cleaned with 26.5 L of Principal solution and then with 22.7 L of

Oxonia solution the day before the experiment. On the day of the experiment, 24 L of

sterile distilled water was passed through the equipment. At the end of the experimental

run, the same sanitization procedure was followed. Previous cleanability studies on

DPCD equipment shows that a concentration of 0.5% Principal solution and 0.28%

Oxonia solution were sufficient to confirm that the equipment was sanitized (Lecky

2005).

Heat Pasteurization Equipment

Heat pasteurization equipment consisted of a water bath (Precision Scientific

Group, Chicago, IL) that was set to the pasteurization temperature (74C), two 5.4 m

stainless steel tubing (0.476 cm ID) and a peristaltic pump (Figure 3.1). Coconut water

was pumped by the peristaltic pump at a flow rate of 385 mL/min through the first

stainless steel tubing (placed in the water bath) in order to be heated to 740C and then

passed through a second stainless steel tubing at 740C for 15 s. D value of

L.monocytogenes at 740C is 0.72 s and its z value is 5.56C (Freier 2001). Treatment for

15 s gives 20 log cycles reduction in this microorganism. Coconut water exiting the

second tubing was immediately cooled to approximately 10C by passing through 3.2 m

of stainless steel tubing (0.476 cm ID) that was placed in ice slush. Heat treated coconut

water was collected in sterile glass containers (6 L) and placed in the cold room and at

4C.













Pasteurized
juice


0?


Figure 3.1. Schematic drawing of heat pasteurization equipment

Carbonation Equipment

Untreated, DPCD and heat pasteurized coconut water samples were carbonated by

using a Zahm & Nagel Pilot Plant Carbonator (Zahm & Nagel Co., Buffalo, NY) with a

capacity of around 7.5 liters. Carbonator was cleaned by soap, distilled water and alcohol

before each use. Coconut water at 40C was placed in the carbonator unit that was kept in

ice throughout carbonation in order to keep the temperature of the juice at about 40C. CO2

gas was sent from the gas tank (BOC Group, NJ) to the carbonator and the air remaining

in the carbonator was replaced by CO2 gas. Next, the CO2 pressure was brought to 1.82

atm and CO2 was bubbled through the juice until all juice inside the carbonator was

collected. Carbonated coconut water was immediately filled into glass champagne bottles

of 750 mL capacity each and capped with metal caps. All carbonated water bottles were

stored at 40C.









Optimization of DPCD Treatment Conditions for Microbial Reduction

Aging of Coconut Water

Aging of coconut water was necessary to bring the initial microbial load of coconut

water to 107 colony forming units (cfu/mL). Frozen coconut water kept in plastic pails at

-20C, was thawed for 1 week at 4C and then formulated by the addition of malic acid to

lower the pH to 4.3 and 0.7% (w/w) Splenda with a final Brix of 6.0. Then the coconut

water was aged at room temperature (24C) for about 46 h in order to increase microbial

load to >107 cfu/mL.

Experimental Design

Response surface methodology (RSM) was used for the design and optimization of

DPCD treatment conditions for microbial reduction. DPCD process variables were

pressure, temperature, CO2 to juice ratio (w/w) and residence time. Experimental

conditions were determined by a 3-factor, 3-level Box-Behnken design, which is one of

the Response surface designs. Residence time was decided to be 6 min, and kept constant

throughout the treatments since long times would not be economically feasible.

Independent variables were pressure (13.8, 24.1, 34.5 MPa), temperature (20, 30,

40C) and CO2 to juice ratio (7, 10, 13 g CO2/ 100 g juice). The maximum pressure level

was chosen as 34.5 MPa because this pressure can be achieved safely considering the

limitations of the system, where 55.16 MPa is the maximum. The minimum pressure

level was chosen as 13.8 MPa since below that pressure level a significant microbial

reduction was not expected based on previous studies. Minimum temperature was

determined by the limitations of the equipment and had to be chosen as the room

temperature at the time of the experiment. Middle temperature value was 30C that was a

close to the critical temperature for CO2 (3 1C). Maximum temperature (40C) was in









supercritical range, and higher temperatures than this could affect the quality of the juice.

Dependent variable was log reduction in aerobic microbial load (cfu/mL) of juice after

treatment. Microbial log reduction was calculated for each experimental run as;

log[(initial number of cfu /mL)([number of cfu/mL after treatment)]. Fifteen

experimental runs were determined by applying Box-Behnken coded design. The codes

and conditions for each variable are shown in Table 3-1. The following equations give

the relation between the codes (Xl, X2, X3) and the variables (T, P and % CO2 level):

Xl= 0.10 *T(oC)- 3.0

X2=0.097 P(MPa)- 2.33

X3=0.333 % C02(g CO2/ 100 g juice) 3.33

Table 3-1. Three factor-3 level Box-Behnken experimental run coded variables and
conditions
Coded % CO2
Coded Coded C02/juice Temperature Pressure Level
RUN# T: X1 P: X2 ratio: X3 (0C) (MPa) (w/w)
1 -1 -1 0 20 13.8 10
2 1 -1 0 40 13.8 10
3 -1 1 0 20 34.5 10
4 1 1 0 40 34.5 10
5 -1 0 -1 20 24.1 7
6 1 0 -1 40 24.1 7
7 -1 0 1 20 24.1 13
8 1 0 1 40 24.1 13
9 0 -1 -1 30 13.8 7
10 0 1 -1 30 34.5 7
11 0 -1 1 30 13.8 13
12 0 1 1 30 34.5 13
13 0 0 0 30 24.1 10
14 0 0 0 30 24.1 10
15 0 0 0 30 24.1 10
XI: Code for Temperature, X2: Code for Pressure, X3: Code for % CO2 level









Storage Study

A storage study was conducted for 9 weeks and samples were taken at weeks 0, 2,

3, 5 and 9 in order to evaluate microbial, physical (pH, color, titratable acidity, Brix)

and sensory attributes of untreated (fresh control), DPCD and heat pasteurized coconut

water beverage samples. Flavor profiles of stored samples were also analyzed

instrumentally. Storage study was ended at the 9th week since the microbial load for

untreated coconut water exceeded 105cfu/mL and the flavor was undesirable.

At the beginning of the storage study, frozen coconut water was thawed at 40C and

formulated by malic acid and Splenda addition. DPCD treated samples were treated at

previously determined optimum conditions (25C, 34.5 MPa, 13% CO2), and heat treated

samples were pasteurized at 74C for 15 s. All samples were then carbonated and capped

in 750 mL champagne bottles, and stored at 40C until further needed. These steps are

shown in a schematic drawing (Figure 3-2). Untreated control samples were prepared

fresh as described above for each week of sensory panels, whereas the DPCD and heat

pasteurized samples were stored samples. DPCD and heat pasteurized samples were

analyzed for microbial load prior to the taste panels in order to ensure the safety of these

samples.






59


Coconut water
I
(Sweetening, Acidification)




Heat treatment DPCD Untreated
74C, 15 s 34.5 MPa, 25C, 13% C02, (Control)
6 min




Carbonation Carbonation Carbonation



Storage
4C, 9 weeks


Figure 3-2. Schematic drawing of steps followed in preparation of storage study samples

Microbial Tests

Aerobic plate count (APC), and yeast and mold count (YM) of untreated, DPCD

and heat pasteurized samples were determined by using 3M Petrifilms (3M

Microbiology, St.Paul, MN). The pH of coconut water was first adjusted to around 7.0

with IN NaOH and then 10-fold serial dilutions were prepared by adding 10 mL of

coconut water into 90 mL of Butterfield's phosphate buffer (Hardy Diagnostics, Santa

Maria,CA). Two replicates of each dilution were prepared and each was plated on two

replicates of 3M Petrifilms. Aerobic plate petrifilms were incubated at 350C for 48 hr,

while yeast and mold petrifilms were incubated at 250C for 5 days before counting. The

petrifilms with the cfu's between 20 and 200 were taken into consideration and the

average cfu's corresponding to the dilution was calculated.









pH

Orion (EA 920) pH meter (Boston, MA) was used for pH measurements. The pH

meter was calibrated using pH 4 and pH 7 standard solutions (Fisher Scientific, NJ) on

each test day. The pH measurements were done in triplicate.

Titratable Acidity (%TA)

A Brinkmann Instrument (Brinkmann Instruments Co., Westbury, NY) consisting

of Metrohm 655 Disomat, Metrohm 614 Impulsomat and Metrohm 632 pH-meter was

used for titration of coconut water samples. Samples were placed in a vacuum oven at

room temperature (22C) and 0.75 atm vacuum for 1 hr in order to remove CO2 gas

before titrating. 20 mL of coconut water sample was titrated to an end point of pH 8.2 by

using standardized 0.1 N NaOH and the amount of NaOH used for titration was recorded.

Percent titratable acidity (w/v) was expressed as % malic acid and calculated by the

following equation:

%TA= (mL of NaOH used) (Normality of NaOH) (meq of malic acid = 0.067) (100)/

(mL of sample)

%TA measurements were done in triplicate for each sample.

Brix

A Fisherbrand hand held refractometer with a 0 to 180 Brix scale (Fisher

Scientific, Pittsburg, PA) was used for Brix measurements. 2-3 drops of coconut water

were placed onto the prism and the reading was recorded. Measurements were done in

duplicate.

Color

Color of coconut water samples was measured in a CIE L* (Lightness) a*

(Redness) b*(Yellowness) color scale by using the Colorgard 14 system (BYK-Gardner









Inc., Columbia, MD). Quartz halogen lamp (2845 K) was used as the light source, and

allowed to warm up for 10 min prior to measurements. The system was calibrated using

black (Zero reference) and white standard (L, a*, b*: 94.31, -0.92, -0.50) tiles. A

standard measurement was done by placing a glass cup filled with 50 mL of distilled

water and the white tile placed on top of the cup in a facedown position. The same

procedure was followed with the coconut water samples. The cup was rinsed with

distilled water and wiped with Kimwipes between samples. Measurements were done in

triplicates.

Sensory Evaluation

Sensory panels were conducted during storage at weeks 0, 2, 3, 5 and 9 in order to

evaluate overall likeability, aroma, taste and off flavor of untreated, DPCD and heat

pasteurized samples. University of Florida FSHN Dept.'s taste panel facility (University

of Florida, Gainesville, FL) consisting of 10 private booths with computers was used to

conduct sensory panels. Samples were stored capped in champagne bottles in an ice bath

before being poured into 60 mL plastic cups, in order to prevent carbonation loss. Each

sample was assigned with a randomly selected three-digit code, and placed in cups on a

tray in all possible combinations of order. Red light was used in the panelist booths in

order to prevent bias on samples due to pinking of some samples. Panelists were asked to

answer some demographic questions at the beginning, and then were offered with an

untreated (fresh control) reference, and three samples (fresh control, DPCD treated, heat

treated). Panelists were asked to rate aroma and taste difference of each sample from the

given reference (continuous 15 cm line scale with values from 0 to 15) using difference-

from-control test. In addition, overall likeability (9 point hedonic scale), off flavor (6

point scale) and their purchase intent for each sample were asked. Panelists took a bit of









cracker and a sip of water to rinse their mouth between the samples. Fifty untrained

panelists evaluated the samples at each storage week. Compusense 5 software

(Compusense Inc., Ontario, CA) was used to design and conduct the test, and to collect

and analyze the data. Sample ballots that were used in sensory panels are given in Table

E-10.

Flavor Analysis

Solid phase micro-extraction (SPME) was used to extract aroma compounds from

coconut water. The SPME fiber was a 1 cm StableFlex PDMS/CAR/DVB fiber (Supelco,

St.Louis, MO) which is a bipolar phase fiber suitable to extract high and low volatile

compounds. 10 mL of coconut water was placed into 40 mL glass vials and brought to

42-45C in a water bath. SPME fiber was inserted into the headspace of the vial and

extraction was held under continuous stirring at 42-450C for 45 min using a magnetic stir

bar. SPME fiber was inserted into the GC injection port and exposed for 5 min for

desorption of aroma compounds. GC/O (HP 5890 Series II) equipment with a FID

detector was used to separate and analyze the aroma compounds. Two different columns

were used in GC/O; a non-polar DB-5 column (Zebron, 30 m x 0.32 mm ID x 0.50 |tm

FT) and a polar Carbowax column (Restek, 30 m x 0.32 mm ID x 0.5 |tm df).

Temperature programming conditions for GC/O using each column are given in Table 3-

2. With each column, two persons sniffed twice each SPME extract. Sniffers used a

continuous scale slide marked as low, medium and high to rate the intensity of the sniffed

compound and also indicated aroma descriptors of each sniffed compound at the

corresponding retention time. The chromatograms for both the FID and sniff port were

recorded and saved. C5-C20 alkane standards were run at each experiment day and their









retention times were recorded. Their literature linear retention indices (LRI's) were

plotted against their retention times and the equation relating the LRI's as a function of

retention times was obtained by using the Excel graph options. The same equation was

used to calculate LRI's of the sniffed compounds at the corresponding retention times.

Examples of LRI calculations of standard alkanes and the formulas relating LRI's to the

retention times are given in Table C-l and Figure C-l, respectively, for the Carbowax

column, and in Table C-2 and Figure C-2, respectively, for the DB-5 column. An

aromagram was constructed by plotting average sniff intensity (average of sniff port peak

areas) against the calculated LRI's.

A GC/MS (Perkin Elmer; Wellesley, MA) equipment with quadrupole-ionization

detector was used for identification of flavor compounds in coconut water. This

equipment used TurboMass 5.01 (Wellesley, MA) software for the integration and

analysis, and a NIST (MS Research 2.0) database as the library of the compounds for the

identification. The SPME extracts were injected and exposed through the injection port

for 5 min. GC-MS temperature programming conditions were; 40C (initial) to 240C

(final) at a 7C/min ramp rate and with a 9.5 min holding time. Each peak on GC/MS

chromatogram was first integrated and then searched through the NIST database for the

identification by using the software. The software gave a list of compound names, that

matched the peak with the degree of match for each listed compound over 1000. An

example of this identification procedure including the chromatogram and the NIST

identification sheet is shown in Figure C-3. C5-C20 alkane standards were used to obtain

an equation relating retention times of compounds to the LRI's.









Table 3-2. Temperature programming conditions used for GC/O runs with DB-5 and
Carbowax columns.
Column Initial Final Ramp Final Detector Detector Injector
Type Oven Oven rate holding A B Temp.
Temp. Temp. (C/min) time Temp. Temp. (C)
(C) (C) (min) (C) (C)
DB-5 40 265 7 5 270 110 220

Carbowax 40 240 7 5 250 110 220


Data Analysis

Response surface regression analysis of Box-Behnken experimental data was

performed using SAS 9.1 software program (Cary, NC). A 3-D Response surface plot

was obtained using STATISTICA 6.0 (Tulsa, OK). The optimal conditions for pressure,

temperature and CO2 level were determined by considering the statistical significance (p

<0.10) of each variable on microbial reduction.

The significance of difference between treatment means for the storage study data

(pH, %TA, Brix, color (L*, a*, b*), sensory attributes) was determined by analysis of

variance (ANOVA) using SAS 9.1 software (Cary, NC) at a significance level of a=0.05.

The means for each treatment were compared using Duncan's multiple comparison test

(a=0.05) to determine statistically different samples. Effects of storage time and

interaction effects were also included in the ANOVA analysis.














CHAPTER 4
RESULTS AND DISCUSSION

Formulation of Coconut Water Beverage

Regulatory and consumer likeability aspects were considered in the formulation of

the coconut water based beverage. FDA regulations regarding low acid foods require that

action be taken to inhibit the growth of C.botulinum. Coconut water had a pH of around

5.0, and therefore, it must be lowered to below 4.6. Informal taste panels were conducted

to decide on the suitability of different organic acids and commercially available pH

lowering compounds. Malic acid was liked the most and was used to lower the pH to 4.3.

Splenda (McNeil-PPC, Fort Washington, PA) was also added as a sweetener at about 0.7

% (w/w) to compensate for the resulting sourness. Preliminary tasting studies also

showed that carbonated coconut water was preferred over non-carbonated. Therefore,

coconut water beverage was formulated as a carbonated, acidified and sweetened

beverage with a pH of 4.3 and Brix of 6.0.

Objective 1: Quantification of Microbial Reduction in Coconut Water as a Function
of Treatment Conditions

To quantify microbial reduction in coconut water as a function of DPCD treatment

conditions, response surface methodology (RSM) was used. The number of experimental

runs and the treatment conditions at each run were determined by using a 3-factor 3-level

Box-Behnken experimental design. This design is one of the response surface designs

that allows fitting of a quadratic model and has the advantage of requiring fewer number

of runs compared to other response surface designs when three factors are used. The Box-









Behnken design suggests a sphere in the cubic process space where the surface of the

sphere is tangential to the midpoints of the each edge of the cubic space (Figure 4-1).

Center point experiments were replicated three times.












1 Q5 0 0 4.5
Figure 4-1. Geometry of the 3-factor 3-level Box-Behnken design

Three factors of this design that represented independent variables in the RSM

model were Xi:Temperature (coded), X2:Pressure (coded) and X3: CO2 level (coded).

The dependent variable was Y: log microbial reduction. Coconut water that was thawed

and formulated by acidification and sweetening was aged at room temperature (240C) to

reach an initial load of 107cfu/mL. Next, 15 experimental runs that were determined by

Box-Behnken design were conducted at the three levels of temperature, pressure and CO2

levels (Table 3-1). Table 4.1 shows the experimental conditions of each run and the

measured log reduction in total numbers of aerobic bacteria. The log reductions were

calculated by subtracting final log numbers of bacteria from initial log numbers. Initial

and final numbers of bacteria were determined by taking average cfu/mL counts on

petrifilms with the cfu's less than 200 cfu/mL. The average initial and final aerobic plate

counts (APC) + standard deviations at each experimental condition are given in Table B-

1.









Table 4-1. Log microbial reductions at each experimental point determined by Box-
Behnken design
Log Log
microbial microbial
CO2 level reduction reduction
T P (g/lOOg experimental predicted Residual:
RUN# Xl X2 X3 (C) (MPa) juice) (A) (B) (A-B)
1 -1 -1 0 20 13.8 10 4.92 4.90 0.02
2 1 -1 0 40 13.8 10 5.03 5.15 -0.12
3 -1 1 0 20 34.5 10 4.90 4.90 0.00
4 1 1 0 40 34.5 10 5.61 5.15 0.46
5 -1 0 -1 20 24.1 7 4.47 4.25 0.22
6 1 0 -1 40 24.1 7 5.40 5.34 0.06
7 -1 0 1 20 24.1 13 5.42 5.66 -0.24
8 1 0 1 40 24.1 13 4.66 5.06 -0.40
9 0 -1 -1 30 13.8 7 5.30 5.15 0.15
10 0 1 -1 30 34.5 7 4.71 5.15 0.56
11 0 -1 1 30 13.8 13 5.90 5.72 0.18
12 0 1 1 30 34.5 13 6.18 5.72 0.46
13 0 0 0 30 24.1 10 5.58 5.38 0.20
14 0 0 0 30 24.1 10 4.99 5.38 -0.39
15 0 0 0 30 24.1 10 5.22 5.38 -0.16
XI: Coded variable for Temperature (T); X2: Coded variable for Pressure (P); X3: Coded
variable for CO2 level

The RSM analysis of data was done using SAS 9.1 statistical software program

(Cary, NC). First, the following quadratic model that included three variables Xl, X2 and

X3 was used and the RSM regression was conducted on the data:

Y= a+ b*X1 + c*X2 + d*X3 + e*X1*X1 + f*X2*X1+ g*X2*X2 + h*X3*X1 + i*X3*X2

+ j*X3*X3

where Y: log microbial reduction, XI: Temperature (coded), X2: Pressure (coded), X3:

CO2 level (coded) and the letters from a to j represent corresponding coefficients for each

parameter of this model. The SAS code and output of the analysis are given in Table B-2

and B-3, respectively. The regression coefficient R2 was 0.76 for this model. Significance

of each parameter was decided at ca=0.1 level and the parameters with p value > 0.1 were









excluded from the model. Results showed that only the parameters X3 and X3*X1 were

significant, therefore any parameter with X2 (Pressure) variable were excluded from the

model. Similarly, Sims and Estigarribia (2002) reported that increasing CO2 pressure

from 7.5 to 15 MPa did not significantly increase microbial reduction.

Next, another RSM regression analysis was performed by using the modified

model that involves only the parameters with variables Xl and X3:

Y= a + b*X1 + c*X3 + d*X1*X3 + e*X*X1 + f* X3*X3

The SAS output of this analysis is given in Table B-4. The regression coefficient

R2 was 0.63 for the model. The model with the estimated coefficients gives the prediction

of log microbial reduction (log red) as a function of temperature (coded) and CO2 level

(coded): log reduction = 5.381 + 0.124*Temp + 0.284*C02 0.355*Temp2 -

0.423*CO2*Temp + 0.05*CO22

Coefficients were determined for the coded values of each variable. The log reductions

predicted at fifteen experimental runs using this equation are close to the experimental

log reductions (Table 4-1). Three-dimensional plots of the response surface for this

equation are given in Figure 4-2.

Apparently, there is not an optimum point on the surface plot at which a(log

reduction) /a(Temp)= 0 and a(log reduction/a(CO2)=0 gives the highest microbial

reduction. The surface plot shows that at lower- and mid-temperatures, microbial

reduction increases as CO2 level increases. However, at higher-temperatures this behavior

changes, and either CO2 level is not effective or causes a decrease in microbial reduction.

The amount of dissolved CO2 has a primary role in microbial reduction. CO2 solubility is

affected by temperature change and decreases as temperature increases (Dodds and others








69



1956). Therefore, increased CO2 does not cause increased microbial reduction at higher


temperatures due to its limited solubility. On the other hand, highest microbial reductions


were achieved at temperatures close to middle temperature (i.e. temperatures around 25-


30C) and highest CO2 level (i.e. CO2 levels around 13%). Therefore, the optimal


conditions of DPCD treatment for microbial reduction in coconut water were selected to


be 25C and 13% (g C02/100 g juice). Predicted log microbial reduction at these


conditions is 5.77. Predicted log microbial reductions at different levels of temperature


and CO2 using the model can be found in CD file: "predicted log reductions.doc".


Figure 4-2. Plots of the response surface for the quadratic model with the variables XI:
Temperature (coded) and X3: %C02 level (coded)


6.6
6.25





- 438

0 494 13 o
5508
2210 5.40

m 5 64 O 7 30
m above 20
Temperature oC





o 6 .6 4
6.2

M- 5.0
4.6
5508
40
S4 38
452
480 30
494 13
522 10
5 36 20
s50 7 o0o G&
S5 64 a0
Above









Objective 2: Evaluation of Physical, Chemical and Microbial Quality of DPCD
Treated Coconut Water Beverage during Storage

The storage study was conducted at 40C for 9 weeks for "untreated", DPCD treated

and heat treated coconut water beverage. Untreated samples were obtained by thawing

the fresh frozen coconut water and formulating it by acidification, sweetening and

carbonation. Heat- treated samples were pasteurized at 740C for 15 s after sweetening and

acidification. DPCD treated coconut water was processed at the previously determined

optimum conditions (Temp=25C, CO2 level=13%) for microbial reduction after

sweetening and acidification. The pressure was 34.5 MPa and treatment time was 6 min.

Heat and DPCD treated samples were carbonated after treatments. Samples were tested

for microbial growth, pH, titratable acidity, Brix and color throughout storage.

Microbial quality of coconut water beverages was evaluated by measuring total

aerobic bacteria (APC) and yeast and mold (YM) counts. The plot of APC results for

each treatment during storage time are shown in Figure 4-3 and the data (cfu/mL) is

given in Table D-1. One tail t-tests (ca=0.05) were used to determine whether there was

significant difference in APC and YM counts of each treatment between week 0 and

week 9 (Table D-2). Data showed that number of aerobic bacteria in untreated coconut

water stayed almost unchanged during the first 6 weeks but showed significant increase

after week 6 and reached > 105 cfu/mL at the end of 9 weeks. There is only one data point

after week 6 to show that increase, therefore further study would be useful to understand

the extent of this increase between weeks 6 and 9. In addition, the comparison of

carbonated coconut water with non-carbonated coconut water for microbial counts would

help to understand if carbonation was the reason for no microbial increase during the first

6 weeks.












1000000

100000

10000


1000

100


--Control
--DPCD


Heat
10 -- Heat
1


0.1 2 4 6 8 1P
0.18 1
Storage time (Weeks)

Figure 4-3. Total aerobic plate counts (APC) of untreated control, DPCD and heat treated
coconut water during storage (DPCD treatment at 250C, 34.5 MPa,13% CO2
for 6 min; Heat treatment at 740C for 15 s)


24.00

19.00

14.00

a 9.00

4.00

-1.00


-- Control

DPCD

Heat


0 2 4 6 8


Storage time (Weeks)


Figure 4-4. Yeast counts of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatment at 740C for 15 s)

Numbers of aerobic bacteria decreased significantly in DPCD and heat treated

samples. The lack of oxygen in the bottles caused by carbonation might have caused the









decrease in the microbial growth. It is important to note that untreated coconut water had

initial microbial loads of around 3 logs, but DPCD or heat treatments unexpectedly did

not cause total inactivation that must be achieved by pasteurization. In order to

understand the real cause for the presence of microorganisms after treatments, every step

of the process was reevaluated for the possibility of contamination. The carbonation

process was a possible cause since this step is conducted in non-aseptic conditions. The

carbonation process was repeated by using sterile water under similar conditions to

coconut water, and the initial and final microbial counts of sterile distilled water showed

that carbonation might cause contamination by up to 3 logs. The APC counts for distilled

water before and after carbonation are given in Table D-3. Heat treated samples were

apparently less contaminated than DPCD treated samples. The decrease in the aerobic

bacteria growth from week 0 to 9 was 1 log in DPCD treated samples and approximately

2 logs in heat treated samples.

Yeast counts of all treatments were low throughout storage. There was no

detectable mold growth while yeast counts were only around 1 log initially and decreased

to no growth by the end of storage (Figure 4-4). Yeast counts for each treatment are given

in Table D-4.

Measured pH values of untreated, DPCD treated and heat treated coconut water are

given in Table D-5 and the plot of pH during storage is shown in Figure 4-5. Statistical

analysis of pH data by analysis of variance (ANOVA) suggests a significant storage time

and treatment interaction (Table D-6). DPCD treated samples had significantly lower pH

than other treatments (ca=0.05). However, the pH means of treatments are 4.199, 4.197

and 4.190 for heat treated, control and DPCD treated samples, respectively. Although









these values are statistically significantly different, they are exactly the same values for

two significant figures, i.e. 4.20. It is suggested that the high accuracy of the pH meter in

the triplicate measurements lowers the sum of squares for errors and causes this result.

The pH of the samples did not change much during storage and was fluctuating around

4.20. Theoretically, a pH change was not expected during storage except for microbial

problems. However, microbial data do not support such a decrease. The slight

fluctuations in pH for the samples could be explained by sample-to-sample differences.



4.26
4.24
4.22
4.2 Control
4.18 DPCD
4.16 Heat
4.14
4.12
4.1
0 2 3 5 9
Storage time (Weeks)
Figure 4-5. The pH of untreated, DPCD and heat treated coconut water during storage
(DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at
74C for 15 s)

Brix values for treatments during storage were close, and the maximum change in

Brix was 0.15 units. This could possibly be due to sample-to-sample variation. Mean

Brix values of treatments were 6.04, 6.0 and 6.0 for control, DPCD and heat treated

coconut water, respectively. Theoretically, a change in Brix of samples was not expected

during storage unless there is evaporation or fermentation of the samples. Samples were

tightly capped in glass champagne bottles and microbial data or pH data do not support

such changes. Standard errors for Brix measurements are zero, which indicates the high









repeatability of the measurements. Statistical analysis of data showed significant weekly

changes and treatment differences since sum of squares of the error term is too low as a

result of high repeatability in the measurements. The data is given in Table D-7 and the

plot of the data during storage is given in Figure 4-6. The SAS output of ANOVA for

Brix data is shown in Table D-8.


6.25
6.2
6.15
6.1 -
6.05 Control
L 6 DPCD
a a i oDPCD
5.95 OHeat
5.9
5.85
5.8
5.75
0 1 2 3 5 9
Storage time (Weeks)

Figure 4-6. The oBrix of untreated, DPCD and heat treated coconut water during storage
(DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat treatment at
74TC for 15 s)

Titratable acidity for untreated, DPCD and heat treated coconut water were

measured during storage and expressed as % malic acid (w/v) equivalents (Table D-9).

Statistical analysis of data by ANOVA showed that DPCD treated samples had

significantly higher titratable acidity (mean = 0.282 g malic acid / 100 mL coconut water)

whereas untreated and heat treated samples had mean values of 0.259 and 0.266 g malic

acid / 100 mL coconut water, respectively (Table D-10). The weekly mean % titratable

acidity values for each treatment are given in Figure 4-7. The reason for higher overall

titratable acidity of DPCD treated samples may be insufficient removal of CO2 during

vacuum treatment. DPCD treated samples were expected to have higher amount of CO2









due to the residual dissolved CO2 after DPCD application. Increase in titratable acidity of

juices by DPCD was observed also by studies of Kincal D. (2000) for orange juice and

Lim and others (2006) for tangerine juice.

During storage, titratable acidity may change due to acid generation by microbial

growth. However, there was no increasing trend in % TA for the samples during storage.

The only treatment that shows a significant increase in microbial growth was the

untreated control sample, but it did not show an increase in titratable acidity during the

last week of storage. Although heat treated samples did not show a microbial increase,

titratable acidity showed some fluctuations during storage. These could be due to the

bottle-to-bottle variations during carbonation.

Color of coconut water samples was measured in CIE color scale as L*, a* and b*

values and the data is shown in Table D- 1. Data from week 0 were omitted because of

measurement errors. The plots of L*, a* and b* values against storage weeks are

presented in Figure 4-8, 4-9 and 4-10, respectively. The data shows slight changes in

L*,a*,b* values for treatments during storage. L* values of the samples, representing

lightness, decreased from week 0 to 5 and then increased slightly at week 9. The a*

value, which represents redness on the positive scale and greenness on the negative scale,

increased from week 2 to 5, then decreased for heat treated sample, and increased up to

week 9 for the untreated control sample; whereas it increased from week 2 to 3 and then

decreased for the DPCD treated sample. These results need to be considered with caution

because some of the samples started pinking from the first day of storage. Color

measurements were done on randomly selected bottles at each storage week. Therefore,

there was large variation in redness for even the same treatment sample from one bottle









to another, depending on the initiation of pinking. From the preliminary experiments, it

was known that once the coconut water in a bottle starts pinking, the intensity of pinking

increased during storage. Normally, one would expect an increase in a* value for all

treatments because independent of the treatment, all bottles eventually showed pinking

during storage. The changes in L*, a* and b* values of the treatments could be due to

bottle to bottle variations and it is not possible to make a clear conclusion based on this

data.



0.35

0.3

0.25
0.2 ( Control
S* DPCD
E 0.15 CHeat
0.1
I-
0.05
0
0 2 3 5 9
Storage time (Weeks)

Figure 4-7. Titratable acidity (as % malic acid (w/v)) of untreated, DPCD treated and heat
pasteurized samples during storage (DPCD treatment at 250C, 34.5 MPa, 13%
CO2 for 6 min; Heat treatment at 740C for 15 s)
















SControl
*DPCD
OHeat


2 3 5 9
Storage time (Weeks)

Figure 4-8. Mean L* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 25C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatment at 74C for 15 s)


* Control
*DPCD
0 Heat


Storage time (Weeks)

Figure 4-9. Mean a* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 25C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatment at 74C for 15 s)










4.5
4
3.5
3
SControl

e. I-OHeat
1.5
1
0.5
0
2 3 5 9
Storage time (Weeks)

Figure 4-10. Mean b* values of untreated control, DPCD and heat treated coconut water
during storage (DPCD treatment at 250C, 34.5 MPa, 13% CO2 for 6 min; Heat
treatment at 740C for 15 s)

Objective 3: Comparison of Untreated Control, DPCD and Heat Treated Coconut
Water by Sensory Evaluation

Consumer panels of 50 untrained panelists were used to evaluate overall likeability,

aroma, taste and off flavor of untreated (control), DPCD and heat treated coconut water

beverage during storage at 40C in glass champagne bottles. Panels were conducted at

weeks 0, 2, 3, 5 and 9. The taste panel data during storage is presented in Table E-1.

Overall likeability of samples was rated on a 9 point scale where the score 1=

dislike extremely, and 9= like extremely. ANOVA was conducted to see if there were

significant differences in overall likeability of samples due to treatment or storage time

effects. The SAS output of ANOVA is shown in Table E-2. The means of overall

likeability scores for each treatment for the overall storage time shows that untreated

control (mean=5.03a) and DPCD treated sample (mean=4.95a) were liked the most and

heat treated sample (4.58b) was liked significantly less than the other samples. Results

showed that there was significant storage time-treatment interaction at a=0.05, therefore,









overall likeability of treatments was changing at different rates during storage time. For

this reason, overall likeability of different treatments was compared separately at each

storage time by ANOVA. The mean overall likeability scores and standard errors are

given for treatments at each storage week in Table E-3. Figure 4-11 shows the

comparison of each treatment for overall likeability scores at different storage weeks.

Initially, DPCD treated and untreated samples were liked significantly more than heat

treated sample. However, starting from the 2nd week, overall likeability of samples

moved close to each other and this difference became insignificant. It is hard to explain

the reason for this change. There could be some flavor and aroma change in the samples

to cause a change in overall likeability scores. Since the samples were carbonated and

stored in glass bottles, flavor change due to oxidization is not expected. From the

previous studies, a change in the overall likeability due to storage time could be possible

since microbial growth during storage could affect flavor and aroma. Kincal and others

(2005) reported that microbial load increased in DPCD treated samples during storage.

However, the results of microbial tests showed that there was no increase in the microbial

counts of DPCD or heat treated samples. Therefore, the change in overall likeability

should not be due to microbial changes.






80



1= dislike extremely 9= like extremely

9
8
7
6 aa aa Control
.a / DPCD
84 --0 Heat
3
02
1
0
0 2 3 5 9
Storage time (Weeks)
Figure 4-11. Comparison of overall likeability of each treatment during storage

A difference from control test was conducted to evaluate the taste and aroma of

heat and DPCD treated samples. Fresh untreated coconut water was given to the panelists

as a reference control every week, and panelists were asked to rate the difference in taste

and aroma of three samples from this reference control. One of the samples was the same

as the reference control. Ideally, the difference from reference control for the control

sample should be rated as zero by the panelists since they are the same. Although most of

the panelists rated this as close to zero, some panelists rated this difference as high as 10.

The taste and aroma difference data for control samples were sorted from lowest to

highest scores at each week and the frequency of each score was plotted as histograms.

Figure 4-12 and Figure 4-13 show the histograms of aroma difference and taste

difference scores, respectively, for the control samples. Scores greater than 6 for taste

difference and greater than 5 for aroma difference from control were excluded from the









data, and the ANOVA was conducted on this new 'corrected' data. This correction was

needed to exclude the effect of outlying panelists from the statistical analysis results.

Statistical analysis of corrected aroma difference from control data by ANOVA

showed that DPCD and heat treated coconut water treatments were not significantly

different in the overall mean aroma scores (Table E-4). Storage was not significantly

affecting the aroma scores of the samples (ca=0.05). The overall mean scores for aroma

difference were 2.12a, 1.92a and 1.15b for DPCD-treated, heat-treated and untreated

control samples. The weekly comparison of treatments for the aroma scores shows that

DPCD and heat treated samples were not rated significantly different for aroma (Figure

4-14). The mean aroma difference from control scores of the panelists at each week are

given in Table E-5.

The SAS output of the ANOVA of the taste difference-from control data is in Table

E-6. The overall mean values for taste difference scores were 2.08a, 3.67b and 4.17' for

the control, DPCD and heat treated coconut water, respectively. Treatments were

significantly different for the taste scores. On the other hand, weekly ANOVA results

showed that DPCD and heat treated samples were rated significantly different at week 0

only, and this difference was insignificant starting at week 2 until the end of storage

(Figure 4-15). The mean taste difference-from control scores for panelists at each week

are given in Table E-7. These results confirm the overall likeability of the samples

throughout storage. Heat treated samples were rated significantly higher for the taste

difference from control at week 0 and liked the least. The low intensity levels of flavor

and aroma in coconut water may cause larger relative errors where comparing differently







82


treated samples, and mask the protective effects of DPCD compared to thermal

treatments.


Week 0 aroma difference (control)


*l .
0 1 2 3 4 5 6 7 8 9 10
Score



Week 3 aroma difference (control)





E.l....


0 1 2 3 4 5 6 7 8 9 10
Score



Week 9 aroma difference (control)


0 1 2 3 4 5 6 7 8 9 10
Score


Week 2 aroma difference (control)


30
25
20
15
2 10
5
0


- ... l- -
0 1 2 3 4 5 6 7 8 9 10
Score



Week 5 aroma difference (control)


0 1 2 3 4 5 6 7 8 9 10
Score


Figure 4-12.The frequency histograms of storage study aroma difference from control
scores of untreated (control) samples


30

20

2 10

0


30

20

2 10

0








30

O 20
20


0


-


-

-












Week 0 taste difference (control)


15

10

5

0


0 1 2 3 4 5 6 7 8 9 10
Score


Week 3 taste difference (control)


0 1 2 3 4 5 6 7 8 9 10
Score


0 1 2 3 4 5 6 7 8 9 10
Score


Week 5 taste difference (control)


0 1 2 3 4 5 6 7 8 9 10
Score


Week 9 taste difference (control)


15

O 10
10
e

0


0 1 2 3 4 5 6 7 8 9 10


Score



Figure 4-13.The frequency histograms of storage study taste difference from control
scores of untreated (control) samples


15

o 10
10

5

0


15

10



0


Week 2 taste difference (control)











0= not different 15= very different

15
14
2 13
12
m 11
8 10
9 Control
7 0 DPCD
6 OHeat
cd 5
S4
O 3 ba ba
b 2 Ca Ca b b

0 r
0 2 3 5 9

Storage time (Weeks)

Figure 4-14. Comparison of treatments for aroma difference from control scores during
storage


0= not different 15= very different


15
14
13
S 12
m 11
) 10
9 9 Control
| 7 DPCD
S5 a a aHeat

2
0

0 2 3 5 9

Storage time (Weeks)


Figure 4-15. Comparison of treatments for taste difference from control scores during
storage

Panelists were also asked to rate off flavor in the samples on a 6-point scale. The

ANOVA of the data suggested that heat treated samples had significantly higher overall

mean off-flavor scores (mean=2.99b) than untreated (mean=2.68a) and DPCD treated









(mean=2.66a) coconut water (Table E-8). Weekly comparison of the treatments for off

flavor formation showed that significant difference between treatments was only

occurring at weeks 0 and 2, and became insignificant starting from week 3 (Figure 4-16).

The weekly mean off-flavor scores are in Table E-9. These results also confirm the

overall likeability and taste scores for treatments. These results suggest that heat treated

samples had some off flavor at the beginning of storage which caused a significantly

higher rating for taste difference from untreated control and lowest rating for likeability

of heated samples initially. However, in later weeks either this off flavor was masked by

other flavors, or DPCD treated samples also developed off flavors and overall likeability

or taste difference scores for treatments became closer.


1= none 6= extremely intense
6

5
"A Control
4- a a
ab a a *DPCD
3 OHeat




0 2 3 5 9
Storage time (Weeks)

Figure 4-16. Comparison of treatments for off flavor scores during storage

Overall mean values and comparison of means for overall likeability, taste and

aroma differences from control and off flavor scores for each treatment are summarized

in Table 4-2.