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1 THERMAL INACTIVATION OF ACID ADAPTED AND NON ADAPTED STATIONARY PHASE SHIGA TOXIN PRODUCING ESCHERICHIA COLI (STEC), SALMONELLA SPP. AND LISTERIA MONOCYTOGENES IN ORANGE JUICE By ZEYNAL TOPALCENGIZ A THESIS PRESENTED TO TH E GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2012
2 2012 Zeynal Topalcengiz
3 To my mother Mrs. Guler Topalcengiz, my father Mr. Muhsin Topa lcengiz, my brother, Mr. Muharrem Yekta Topalcengiz, my sister in law, Mrs. Medine Topalcengiz, and my lovely nephew, Mr. Hasan Topalce ngiz, for their endless support
4 ACKNOWLEDGMENTS I would like to thank my advisor Michelle D. Danyluk for the opportunity great understanding and support during my research. I thank especially lab mates Ms. Angela M Valadeza, Ms. Thao Nguyen, M s. Rachel Mcegan, Ms. Loretta M Friedrich, and all other Ms. Gwen Lundy Mr. L uis Martinez, and Mr. Brian Buzzie for technical assistance. I also would like to thank Mr. Mihai C Giurcanu for statistical assistance. I thank my friends in Gainesville who shared their experiences and knowledge with me, Mr. Engin Kilic, Mr. Ilker Avan, M r. Yavuz Yagiz. I also thank my friends, Mr Umit Erdem Algun, Mr. Egemen Tuncay, Mr. Emir Altug. I thank my committee members, Dr. Jos I. Reyes, Dr. Rene Goodrich Schneider, and Dr. Reza Ehsani for their helpful discussion and recommendations I also would like to thank my parents and my brother for their endless support and encouragement.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 7 LIST OF FIGURES ................................ ................................ ................................ ......................... 8 ABSTRACT ................................ ................................ ................................ ................................ ..... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 11 2 LITERATURE REVIEW ................................ ................................ ................................ ....... 15 Fruit Juice, Pathogens, and Process ................................ ................................ ........................ 15 Fruit Juice Pathog ens ................................ ................................ ................................ .............. 15 Escherichia coli ................................ ................................ ................................ ............... 16 Salmonella ................................ ................................ ................................ ....................... 18 Listeria monocytogenes ................................ ................................ ................................ ... 20 Cryptosporidium parvum ................................ ................................ ................................ 22 History of Fruit Juice Outbreaks ................................ ................................ ............................. 23 Orange Juice ................................ ................................ ................................ ........................... 25 Orange Juice Process ................................ ................................ ................................ ....... 26 Thermal Orange Juice Pasteurization and Alternatives ................................ ................... 27 Acid Adaptation and Thermal Resistance ................................ ................................ .............. 28 Acid Adaptation ................................ ................................ ................................ ............... 28 The ATR of E. coli ................................ ................................ ................................ ... 30 The ATR of Salmonella ................................ ................................ ........................... 31 The ATR of Listeria ................................ ................................ ................................ 32 Thermal Tolerance and Experi mental Thermal Treatments ................................ ............ 33 Thermal resistance of E. coli ................................ ................................ .................... 34 Thermal resistance of Salmonella ................................ ................................ ............ 35 Thermal resistance of Listeria ................................ ................................ .................. 36 3 MATERIAL AND METHODS ................................ ................................ .............................. 38 Juice ................................ ................................ ................................ ................................ ........ 38 Strains Used ................................ ................................ ................................ ............................ 38 Inoculum Preparation ................................ ................................ ................................ .............. 39 Thermal Treatment of Inoculated Juice ................................ ................................ .................. 40 Microbiological Analysis ................................ ................................ ................................ ........ 41 Statistical Analysis ................................ ................................ ................................ .................. 41
6 4 RESULTS ................................ ................................ ................................ ............................... 47 Time Interval Determination ................................ ................................ ................................ .. 47 D and z value Determination for Strains and Serotypes of Pathogens Used .......................... 47 D and z values of STEC Strains ................................ ................................ ............................. 48 D values of Salmonella Serotypes ................................ ................................ .......................... 49 D values of L. monocyt ogenes Strains ................................ ................................ .................... 50 Heat Resistance of All Pathogens ................................ ................................ ........................... 51 5 DISCUSSION ................................ ................................ ................................ ......................... 74 6 CONCLUSIONS AND FUTURE WORKS ................................ ................................ ........... 80 LIST OF REFERENCES ................................ ................................ ................................ ............... 82 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ......... 94
7 LIST OF TA BLES Table page 3 1 Isolates used for thermal processing experiments. ................................ ............................ 43 3 2 Time intervals used in heat treatment expe riments of STEC strains to plot regression lines in determination of D values. ................................ ................................ .................... 44 3 3 Time intervals used in heat treatment experiments of serotypes to plot regression lines in determination o f D values. ................................ ................................ .................... 45 3 4 Time intervals used in heat treatment experiments of L. monocytogenes strains to plot regression lines in determination of D values. ................................ ........................... 46 4 1 Linear regression equations and R 2 of STEC strains used in D value calculations at 56, 58, 60C.. ................................ ................................ ................................ ..................... 53 4 2 Linear regression equations and R 2 of Salmonella serotyp es used in D value calculations at 55, 58, 60C.. ................................ ................................ ............................. 54 4 3 Linear regression equations and R 2 of L. monocytogenes strains used in D value calculations at 56, 58, 60C.. ................................ ................................ ............................. 55 4 4 Linear regression equations and R 2 of strains used in z value calculations.. ..................... 56 4 5 D and z values of STEC strains from linear regression equation s.. ................................ ... 57 4 6 D and z values of Salmonella serotypes from linear regression equations.. ...................... 58 4 7 D and z values of L. monocytogenes s trains from linear regression equations.. ............... 59 4 8 Average z values of STEC and L. monocytogenes strains and Salmonella serotypes. ...... 60
8 LIST OF FIGURES Figure pag e 4 1 Linear regression lines of STEC strain at 56C to estimate D value. ................................ 61 4 2 Linear regression lines of STEC strain s at 58C to estimate D value. ............................. 62 4 3 Linear regression lines of STEC strains at 60C to estimate D value. .............................. 63 4 4 Linear regres sion lines of Salmonella serotypes at 55C to estimate D value. ................ 64 4 5 Linear regression lines of Salmonella serotypes at 58C to estimate D value ................. 65 4 6 Linear regression lines of Salmonella serotypes at 60C to estimate D value. ................ 66 4 7 Linear regression lines of L. monocytogenes strains at 56C to estimate D value ........... 67 4 8 Linear regression lines of L. monocytogenes strains at 58C to estimate D value. .......... 68 4 9 Linear regression lines of L. monocytogenes strains at 60C to estimate D value. ........... 69 4 10 Linear regression lines of STEC strains to estimate z value.. ................................ ............ 70 4 11 Linear regression lines of Salmonella serotypes to estimate z value.. ............................... 71 4 12 Linear regression lines of L. monocytogenes strains estimate z value.. ............................. 72 4 13 Semilogaritmic plot of D values versus temperature.. ................................ ....................... 73
9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requiremen ts for the Degree of Master of Science THERMAL INACTIVATION OF ACID ADAPTED AND NON ADAPTED STATIONARY PHASE SHIGA TOXIN PRODUCING ESCHERICHIA COLI (STEC), SALMONELLA SPP. AND LISTERIA MONOCYTOGENES IN ORANGE JUICE By Zeynal Topalcengiz August 2012 Cha ir: Michelle D Danyluk Major: F ood Science and Human Nutrition Thermal inactivation parameters of stationary phase, non adapted and acid adapted pathogens, primarily as cocktails of multiple strains have been studied in various juice products. All D val ues for STEC, Salmonella and L. monocytogenes in orange juice were obtained using strain cocktails. The objective of this study was to evaluate the heat resistance of individual strains of stationary phase non adapted and acid adapted Shiga toxin producin g Escherichia coli (STEC), Salmonella spp., and L. monocytogenes in orange juice. Three STEC, L. monocytogenes, and Salmonella strains/serotypes were evaluated. STEC and Salmonella isolates were grown in TSB, and L. monocytogenes strains grown in BHI, sup plemented with 1% glucose for acid adaption, and inoculated into single strength pasteurized orange juice without pulp. Inoculated juice was sealed into microcapillary tubes. Microtubes were immersed into water baths at 56, 58, and 60 C for STEC and L. m onocytogenes strains and at 55, 58, and 60 C for Salmonella serotypes, removed at predetermined time intervals, and placed immediately onto ice. Thermally treated and sterilized tubes were crushed in 0.1% peptone using a sterile glass rod for microbiologi cal analysis. Populations of STEC and Salmonella were enumerated on TSA supplemented with 0.1% sodium pyruvate; BHI agar supplemented with 0.1% sodium pyruvate was used for L. monocytogenes strains.
10 Different strains in the same species respond ed to heat d ifferently. Thermal tolerance was increased significantly ( P < 0.05) for acid adapted STEC strains, however, acid adaptation did not improve heat resistance for Salmon ella spp., and L. monocytogenes strains at most temperatures tested. Salmonella serotypes are less heat resistant, at all temperatures tested, than L. monocytogenes and STEC. Shiga toxin producing Escherichia coli especially strain O111, are the most heat resistant at 56 and 58C; L. monocytogenes strains are the most thermal tolerance at 60 C. Combining individual results of all pathogens tested, the formula of log D = 8.2 0.14 T (C) was used to calculate a general process for orange juice at 71.1C. Using this equation, a 5 log reduction of all three pathogens in single strength orange ju ice requires 5.29 s at 71.1C, with a z value of 7.1C.
11 CHAPTER 1 INTRODUCTION Pathogenic, foodborne microorganisms remain a significant food safety concern in food production. To inactivate these pathogens, thermal pasteurization is one of the most comm only applied and effective technique (MacGregor and Farish, 2000). The thermal inactivation parameters of acid adapted and non adapted stationary phase Escherichia coli O157:H7 Salmonella spp, and Listeria monocytogenes, primarily as cocktails of multiple strains has been studied in various food products; most of these studies were conducted a decade ago (Mazzotta, 2001; Mak et al., 2001; Singh et al., 2008) Fruit juices are widely consumed product with a pasteurization step durin g process (Ngadi et al., 2010). In thermal fruit juice pasteurization, the success of the process depends on the establishment of appropriate heat application times and temperatures. Validation of these parameters through elaborated studies with appropriate methodologies is essen tial. Several outbreaks related to fruit juices occurred in 1990s, threatening public health (Vojdani et al., 2008). The Centers for Disease Control and Prevention (CDC) report a total of 21 outbreaks of E. coli O157:H7, Salmonella spp., and Crytosporid i u m, associated with the consumption of unpasteurized fruit juice between 1995 and 2005 (Vojdani et al., 2008). Outbreaks of E. coli have been associated with the consumption of unpasteurized apple cider and apple juice. Outbreaks of Salmonella serotypes hav e been linked mainly to unpasteurized orange juice consumption. Listeria monocytogenes has not been implicated in any outbreak related to fruit juice consumption (FDA, 2001). The routes of contamination of these pathogens have not been completely identifie d in any of the juice outbreaks. The use of dropped fruit, non potable water, and the presence of cattle and wildlife in, or close to, the production or processing environment are included as likely sources of juice contamination (Harris et al., 2003).
12 Ina ctivation of pathogens before consumption of fruit juices has become mandatory as a result of outbreak s associated with fruit juice. Food and Drug Administration (FDA) published a juice final rule (66 FR 6137) in January, 2001, requiring that all the juice processors must comply with the Hazard Analysis Critical Control Points (HACCP), and achieve a 5 log reduction of the pertinent pathogenic microorganism (FDA, 2001). Pasteurization in the juice industry can be achieved by many different technologies; howe ver thermal pasteurization is the most common. High temperature short time (HTST) pasteurization is commonly used as a heat treatment technique with optimum temperatures and time parameters. Current thermal treatment of fruit juices and apple cider appea rs to be efficient in the prevention of microbial spoilage and elimination of pathogens. Pasteurization parameters for fruit juice with the use of cocktails of target microorganisms have been studied by numerous researchers (Mazzotta, 2001; Mak et al., 200 1; Singh et al., 2008) and appropriate time and temperature parameters in pasteurization have been recommended as 3 s at 71.1 C by FDA. The pasteurization paramet ers. Pasteurization may also ca use unavoidable change of physico chemical, nutritious, and sensory properties of juice (Lee and Coates, 2002; Moshonas et al., 1993; Moshonas and Shaw, 1995). Determination of the appropriate thermal inacti vation parameters c onstitute essential variables for the designation of juice pasteurization to produce safe, stable, and quality juices. In the absence of pasteurization, spoilage microorganism can take advantage of favorable environment of fruit juices, grow, and spoil pr oducts. However, foodborne pathogens do not normally grow in fruit juices. Acidic foods, like most fruit juices, were not recognized as a vehicle of foodborne pathogens until the first confirmed outbreak of E. coli O157:H7 related to unpasteurized apple
13 ci der in 1991 (Besser et al., 1993). Some foodborne pathogens can develop acid adaptation systems that induce cross protection, and makes them more resistant against other environmental stresses (Leyer and Johnson, 1993; Bearson et al., 1997; Ryu et al., 199 8; Ryu and Beuchat, 1998), thus, increasing their ability to survive in juice long enough to cause an outbreak. Escherichia coli O157:H7, Listeria monocytogenes Salmonella spp., and Cryptosporidium parv um can tolerate low pH values and survive in fruit j uices and juice concentrates longer than non adapted cells (Hsin Yi and Chou, 2001; Oyarzabal et al., 2003; Gahan et al., 1996). The acid adaptation of L. monocytogenes E. coli O157:H7, and Salmonella spp, also increases the heat resistance of these bacte ria in apple, orange, white grape juices, apple cider, cantaloupe, and watermelon juice (Mazotta, 2001; Ryu and Beuchat, 1998; Sharma et al., 2005). Research comparing acid adapted and non adapted strains used various strains and serotypes of each microor ganism as cocktails with similar temperature values (52, 56, 57, 60, 62, 64C). Fruit juice pasteurization parameters should be studied in more detail due to differences among the methodologies applied and the use of cocktails rather than individual strain s in currently published studies. The number of current studies regarding thermal inactivation of acid adapted microorganisms in the fruit juices is limited, and compounded by the use of strain cocktails. In this master thesis, we evaluate the effect of ac id adaptation on the heat resistance of individual isolates of Shiga toxin producing E. coli (STEC), Salmonella spp. and Listeria monocytogenes in single strength orange juice. The purpose of this study is to evaluate the thermal inactivation responses of acid adapted and non adapted stationary phase Shiga toxin producing E. coli (STEC) Salmonella spp. and L. monocytogenes by using individual strains of each organism in single strength orange juice, respectively. We inoculated each microorganism separatel y for a respective determination of thermal inactivation values, rather than inoculating as cocktail where
14 strain to strain variability may be lost or masked, as previous studies have done. Also, the specific objectives of this study are to determine how a cid adaptation of each pathogen affects the thermal inactivation values in the absence of potential microbial interactions and metabolic end products among different species, t o estimate thermal death time parameters ( D and z value) for acid adapted and non adapted stationary phase of these pathogens and to validate current studies about thermal death time parameters ( D and z value ) for acid adapted and non adapted stationary phase pathogens in orange juices Results of this project should provide more detailed and clarified information about the effect of acid adaptation of stationary phase microorganisms on thermal inactivation of bacterial survival, and should lead to be a better understanding of the determination of pasteurization parameters for pro cessors.
15 CHAPTER 2 LITERATURE REVIEW Fruit Juice, Pathogens, and Process while other carbonated or noncarbonated beverages that contains less than 100 % juice are ca lled 21 CFR 102 (FDA, 2010) vegetables, purees of the edible portions of one or more fruits or v egetables, or any concentrates are ready for direct consumption, and are obtained by the extraction of cell ular juice from fruit tissues. Fruit juices are signi ficant commodities in the global market with the consumers from all age groups. Health advantages, freshness and preferable taste and flavor properties, are some of the reasons for t he popularity of fruit juices. In 2009, the U.S. juice industry in had a v alue over $22 bi llion, volume in excess of 16 billion liters, and accounted for 30.8% of the global juices market value (Datamonitor, 2010). According to the U.S. Department of Agriculture/Economic Research Service in 2008, out of the 25.7 gallons of bever ages consumed per capita, 6.9 gallons were fruit juice (USDA ERS, 2010). Fruit Juice Pathogens Fruit juices can be spoiled by microorganisms; particularly yeasts, molds, and acid tolerant bacteria, due to their high wa ter activity and sugar content. Foodbo rne pathogens do not normally grow to levels of spoilage in fruit juices, primarily due to the high acid content of many of these juices. Nonetheless outbreaks, including the first confirmed outbreak of Escherichia coli O157:H7 associated with apple cider in 1991 (Besser et al., 1993), led researchers to reevaluate
16 the ability of foodborne pathogens to survive for extended periods in high acid food products (Moody, 2003) Shiga toxi genic E. coli (STEC), Listeria monocytogenes Salmonella spp., and Cryptospo ridium parv um can tolerate the acidic environment and survive in fruit juices and juice concentrates longer than init ially expected. The ability of some of these pathogens to adapt to the acidic environment increases the risk (Hsin Yi and Chou, 2001; Oyar zabal et al., 2003 ; Gahan et al., 1996). With the exception of L. monocytogenes STEC Salmonella spp and C. parvum are implicated as the primary cause of many fruit juice associated outbreaks. Although not linked to any fruit juice associated outbreaks; Li steria species have been isolated from unpasteurized apple and apple raspberry blends in a survey of fifty juices (Sado et al. 1998) Escherichia coli The genus Escherichia is a typical member of enterobacteriaceae that live in the intestinal system of hum ans and warm blood animals. Escherichia coli are gram negative, rod shaped facultative anaerobes, some species of which may causes a large variety of diseases including diarrhea, urinary tract infections, sepsis and meningitis (Meng et al., 2007). The opti mum temperature for growth is 37C, but growth has been observed between 8C and 45 C (Meng et al., 2007). Generic E. coli is commonly used as an indicator of fecal contamination indicator in water supplies as up to 1% of the fecal bacterial flora of mamma ls is compromised of E.coli (Windfield and Groisman, 2003). The serotypes of E. coli the capsul es. A total of 173 O antigens, 56 H antigens, and 103 K antigens have been identified (Orskov and Orskov, 1992 ). Based on virulence pro perties, mechanisms of pathogeni city, clinical symptoms, and different O:H serotypes, E. coli isolates that induce diarrh eal diseases have been classified into groups called pathotypes (Meng et al., 2007). Enterotoxigenic E. coli
17 (ETEC), Enteroinvasive E. coli (EIEC), Enterohemorrhagic E. coli (EHEC), Enteropathogenic E. coli (EPEC), diffuse adhering E. coli and Enteroaggre gative E. coli (EAEC) represent the six pathotypes of E. coli that are responsible for outbreaks due to the consumption of co ntaminated food and beverages. Escherichia coli O157:H7, (EHEC; STEC) were first recognized as human pathogens following two outbre aks of bloody diarrhea (hemorrhagic colitis; Riley et al. 1983). Although numerous serotypes of EHEC including O26, O111, Sorbitol fermenting O157:NM have been implicated as the cause of hemorrhagic colitis, E. coli O157:H7 remains the primary cause of hem orrhagic colitis in the U.S., which may be followed by the fatal hemolytic uremic syndrome (HUS) complication, especially among children (Karch et al., 2005). The cause of the bloody diarrhea and HUS is the ability of EHEC to produce a called Shiga toxin ( or verotoxin, STEC, or verotoxin producing E. coli (VTEC); Griffin, 1998). The name of toxin comes from its similarity to toxin produced by Shigella dysenteriae STEC strains are not part of the natural microflora of fresh jui ce or the fruit used in the production of fresh juice. It is believed that the presence of E. coli in fruit juice is a consequence of fecal contamination emanated from other reservoirs, such as cattle, wild birds, deer, rodents, goats, sheep, cats, dogs (N ielsen et al., 2004). In the light of numerous juice outbreaks STEC can tolerate the low pH of juice and survive to cause illnesses. The effect of environmental stresses on the resistance of E. coli O157:H7 have been studied in different types of buffer, media, models, and food. Acid adapted E. coli O157:H7, grown in medium supplemented with 1 % glucose, increased the thermal resistance in apple cider and orange juice; however the strain, type of organic acid, acid adaptation, and acid shocking affected t he acid resistance (Ryu and Beuchat 1998). Acid adapted E. coli O157:H7 survive longer than non adapted cells in fruit
18 juices under refrigeration (Hsing Yi and Chou, 2001). Escherichia coli O157:H7 survived at detectable levels in apple, orange, pineapple, and write grape concentrates in 12 weeks period storing at 23C (Oyarbazal et al., 2003) and at levels of 10 3 CFU/100 mL from orange juice concentrates stored for 147 day at 12.2C with 10 7 CFU/100 mL initial concentrations (Larkin et al., 1995). In app le juice, E. coli O157:H7 survives up to 24 days at 4C. (Miller and Kaspar, 1994; Fratamico et al., 1997; Splittstoesser et al., 1996). Salmonella Although Salmonella Typhi was discovered in 1880 by Karl Joseph Eberth, and isolated in 1884 by Georg Theodo r August Gaffky, the name of this microorganism comes from the name of one of the two researchers, Daniel Elmer Salmon, a U.S. veterinary surgeon and Theobald Smith, who isolated Salmonella cholera suis from ill hogs in 1885 (ICMSF, 1996). Salmonella spp. are gram negative, facultative anaerobic, non spore forming, rod shaped bacterium in t he family Enterobacteriaceae. Non typhoidal Salmonella infections cause salmon ellosis, whose symptoms include diarrhea, fever, vomitin g, nausea, and abdominal pain. The m ost vulnerable groups to Salmonella infection are young children, the elderly, and immunocompromsied individuals. Although some members of this family are nonflagellated, member of Salmonella genus predominantly have peritrichous flagella, and are motile ( Maure Salmonella bongori and Salmonella enterica represent the two species of the genius Salmonella Salmonella enterica are subdivided into seven subspecies: Salmonella enterica subsp. enterica (I), Salmonella enterica subsp. salmonae (II), Salmonella enterica subsp. arizonae (IIIa), Salmonella enterica subsp. diarizonae (IIIb), Salmonella enterica subsp. houtenae (IV), Salmonella bongori (V), and Salmonella enterica subsp. indica (VI), that induces the majority of Salmonella causing di seases among human and the warm blooded animals
19 (Cooke et al., 2007).According to Kauffmann White scheme, an antigenic formula for Salmonella serotypes, there are over 2,600 Salmonella s erotypes ( Popoff et al., 2004). Salmonella can be easily cultured in t he laboratory, and it is easy to see visible colonies after 24 h incubation at about 37 C. They can survive a wide range of pH, between 4.5 and 9.0; with the optimal pH for growth around neutrality (Rovira et al., 2006). The maximum growth temperature is just above body temperature, and most serotypes do not grow below 7 C, however some serotypes can grow as low as 2 C and as high as 54 C (Baker et al., 1986; Droffner and Yamamoto, 1992). Recent outbreaks of Salmonella have been associated with consumptio n of fresh tomatoes, raw/undercooked shell eggs, lettuce, unpasteurized orange juice, cantaloupes, chocolat e, alfalfa sprouts, and poultry (Yaun, 2002; CDC, 2012) Salmonella can tolerate and adapt to acidic conditions, and high acidic foods, such as unpa steurized orange juice and tomatoes have caused outbreaks (Yuk and Schneider, 2006; Leyer and Johnson, 1992). There are a number of ways Salmonella may be introduced to fruit juices from harvest to the final product. Although pathogen contamination routes have not been definitively confirmed in any juice outbreak, the use of dropped fruit, nonpotable water, and the presence of cattle, deer, or, in one case, amphibians, in or near production of fruit appear to be a reoccurring theme (Harris et al., 2003). D anyluk et al. (2010) concluded that under typical postharvest handling practices Salmonella population on the orange peel surface did not grow or penetration in to juice for intact fruit, even in the presence of minor wounds or natural light labeling; surv ival on the peel surface exceeded 45 days. The long term survival on fruit surfaces may be a potential contamination risk for processing equipment. However, microbial levels detected in fresh juice were 90 99% lower than levels found on corresponding frui t surface, indicating that much of the surface contamination on fruit can be eliminated by commercial citrus juice extraction. Valero et
20 al. (2010) indicate that high initial microbial population level (10 5 to 10 6 CFU/m L ) in citrus juice may occur as a con sequence of insufficient sanitation, poor hygiene practices, deteriorated fruits, and poor equipment sanitation, potentially increasing the risks of foodborne pathogens in juice. Salmonella can adapt to extreme acid environments in a similar manner as E. c oli and can survive in apple juice with a pH of 3.6 for 30 day at 22 C (Goverd et al., 1979). In orange juice, Parish et al. (1997), describe the survival of Salmonella serotypes Gaminara, Hartford, Rubislaw, and Typhim u ri u m in pH 3.5, 3.8, 4.1, and 4.4, and stored at 0 and 4 C with 106 CFU/m L initial concentration survived in detectable numbers more than 26 days at pH 3.5, 46 days at pH 3.8, 60 days at pH 4.1, and 73 days at pH 4.4 According to CDC (2010), an estimated 1.4 million cases of salmonellosis occur annually in the U.S. resulting in approximately 400 fatalit i es. Historically, a number of Salmonella outbreaks were linked to consumption of unpasteurized juices. Listeria monocytogenes Listeria monocytogenes differs from E. coli and Salmonella due t o severity of illness it causes, the high case fatality rate, long incubation period prior to consumption, unique growth capabilities, and ability to survive. Listeria monocytogenes is a gram positive, rod shaped, non sporeforming, motile, facultative anae rob e and the causes a disease called listeriosis. The onset time for serious listeriosis symptoms may be five weeks, complicating traceback investigations. Severe listeriosis symptoms may include meningitis, septicemia, and spontaneous miscarriage or sti ll birth following early influenza like symptoms including nausea, vomiting, persistent fever, and diarrhea. Newborns, immunocompromised people, elderly, and pregnant are the high risk population of listeriosis. Listeria monocytogenes is a critical public health concern due to the 20 30% mortality rate (Ramaswamy et al., 2007). Each year an estimated 500 deaths out of 2,500
21 infections are linked to Listeria in the U.S., making it the leading cause of death due to foodborne bacterial pathogens (CDC, 2000). V arious changes in social patters with complex interaction have resulted in the emergence of listeriosis as an important public health concern (Swaminathan et al., 2007). These factors include: increase of life expectancy; increase of immunocompromised popu lation due to AIDS and organ transplantation; centralization and consolidation of food production and processing; increase of demand for Ready to eat, refrigerated, frozen, and short time cooking foo ds (Swaminathan et al., 2007). Within the six Listeria s pecies, ( L. monocytogenes, L. inanovii, L welshimeri, L. seeligeri,L.murayyi and L. grayi ) in the genus Listeria only L. monocytogenes and L. ivanovii are considered pathogenic. An estimated 90% of human L. monocytogenes isolates belong to serotype 1/2a 1/2b, and 4b in total of 13 serotypes of L. monocytogenes (Dalgaard, 2006). Serotype 4b is an epidemic strain that causes sporadic human cases worldwide. The U.S. has a zero tolerance policy regarding the detection of L. monocytogenes in foods and bevera ges. In 1981, the first confirmed outbreak of Listeria with 41 cases and 8 deaths was associated with consumption of contaminated coleslaw Nova Scotia, Canada (Montville and Mathews, 2005). Following this outbreak, investigations demonstrated that the pri mary transmission of listeriosis was from the consumption of contaminated food (Swaminathan et al., 2007). Listeria monocytogenes can be found in soil, mammalian feces, sewage, water, and decaying vegetation and can grow temperature range of 2 to 45C (Sw aminathan et al., 2007). Also it is a pschrotroph that can multiply at temperatures as low as 0.4 C (Dalgaard, 2006). Listeria also has the ability to survive in the high salinity conditions, and grow between pH 4.4
22 and 9.6, depending on type of acid and incubation temperature (Swaminathan et al., 2007). These survival capabilities make L. monocytogenes very durable under ex treme environmental conditions. Listeria monocytogenes is commonly associated with foods such as ready to eat foods, meat and poultry, milk products (particularly cheese), and seafood. Although L. monocytogenes has not been implicated in any juice outbreaks it has been reported that acid adaptation causes Listeria become more resistant to other environmental stresses as a result of cros s protection (Mazotta, 2001). Listeria monocytogenes survives in white grape juice for 12 days, in apple cider for 24 days, and in orange juice for 61 days, respectively in different storage temperatures (Piotrowski, 2003). Acid adapted L. monocytogenes ce lls grew in orange juice (pH 2.6) stored at 37C, by almost 10 4 CFU/mL in 6 h (Caggia et al., 2007). Also, Parish and Higgins (1989) demonstrated that population of Listeria cells decreased 10 6 CFU/mL in orange juice stored at 4C in 25 days at pH 3.6 and in 43 days at pH 4.0. Cr yptosporidium parv um Cryptosporidium is a single celled, protozoan parasite that was first recognized by Ernest Edward Tyzzeras in the gastric glands of laboratory mice in 1907 (Fayer, 2008). It was not identified until 1976 when tw o distinct research groups reported separate cases of cryptosporidiosis in humans (Nime et al., 1976; Meisel et al 1976). The taxonomic classification of Cryptosporidium only became clear in 1990s when studies based on molecular taxonomy indicated the gen us is much more complicated than morphology and host specify can define. Thirteen Cryptosporidium species are identified, including C. parv um, C. muris, C. felis and C. wrairi, that infect mammals; C. baileyi and C. meleagridis that infect birds; C. serpen tis and C. saurophilum linked to the infection of reptiles, and C. nasorum tropical fish infections (WHO,
23 2006). The most common human isolate is C. parv um which has been linked to consumption of unpasteurized fruit juices. The symptoms of infection inclu de diarrhea; abdominal cramps, headaches, nausea, and vomiting. In individuals with weakened immune system, such as immunocompromised people (AIDS), elderly, and the very young children symptoms can be more severe. Onset of symptoms takes up to twelve days after consumption of contaminated food or water. Cryptosporidium is transmitted through the fecal oral route, beginning with ingestion of the Cryptosporidium oocycst that survives for a long time in the environmental conditions (Robertson et al., 1992) H istory of Fruit Juice Outbreaks High acid foods, such as fruit juices, with pH 4.6 or below are generally considered as safe to consume directly by researchers and agencies like Food and Drug Administration (FDA) (FDA, 2010). The assumption is that while f ruit juices provide favorable environments for spoilage by yeasts, molds and acid tolerant bacteria due to the high water activity and sugar content; the high acidity would prevent pathogenic bacteria from being a problem. However, outb r eaks associated wit h the consumption of fruit juice increased in the 1990s (Mazotta, 2001). In 1991, fresh pressed apple cider contaminated with E. coli O157:H7 caused four children in southern Massachusetts to be hospitalized with HUS (Besser et al., 1993). A total of 23 HU S cases were reported. Cattle raised by the cider press operator and grazed in the field adjacent to the mill were identified as the possible source of contamination resulting in apples being contaminated with manure from equipment, handling of workers, co ntaminated water, or contaminated storage areas. In 1996, the Seattle King county Department of Public Health reported an outbreak of E. coli O157:H7 associated with Odwalla brand unpasteurized apple juice and Odwalla juice mixtures containing apple juice, resulting in a voluntary nationwide
24 recall that ended with 70 cases (14 HUS) and one death due to the use of dropped fruits and the localization of apple orchard near cattle farm (CDC, 1996) Since 1990, a number of outbreaks involving salmonellosis have been reported mostly associated with fresh (unpasteurized) orange juice. An outbreak of Salmonella enteric serotype Hartford resulted in 62 case patients from 21 states during May and June 1995 because of unpasteurized locally produced orange juices (Cook et al.1998). An outbreak of Salmonella Muenchen causing 423 cases and 1 death in Canada was due to consumption of unpasteurized orange juice, produced by a single producer and distributed widely in the United States and Canada (CD C, 1999; CCDR, 1999). A to tal of 21 juice associated outbreaks 10 related to apple juice and cider, 8 linked to orange juice, and 3 caused by other kind of juices occurred from 1995 through 2005, with 1366 total illnesses (Vojdani et al., 2008). As a result of these outbreaks, the U.S. Food and Drug Administration published a juice rule (66 FR 6137) in January, 2001, requiring all processors of 100% juice must comply with Hazard Analysis Critical Control Points (HACCP) to achieve 5 log reduction of pathogenic microorganisms (U.S FDA, 2001). This regulation made HACCP mandatory in juice processing and packaging plants. The potential hazards for juice fall into three categories a) chemical including pesticide residues, food allergens, toxins and heavy metal contamination; b) Physi cal including glass and metal fragments; and c) biological including pathogen microorganisms, such as Salmonella, STEC and Cryptosporidium The US FDA has also declared patulin, a mold toxin, as a potential hazard in apple juice that must be controlled. T he control of these hazards is explained in guidance for industry regarding juice HACCP hazards and controls published by FDA in March 3, 2004 (FDA, 2004).
25 Prior to the development of a HACCP plan, juice processors must have Good Manufacturing Practices (G MP) requirements and Sanitation Standard Operating Procedures (SSOPs) in place as prerequisite programs. If juice is not pasteurized, the U.S. FDA requires a warning statement to inform consumers about potential food safety risks related to the product (FD A, 1998) that reads THIS PRODUCT HAS NOT BEEN PASTEURIZED AND THEREFORE MAY CONTAIN HARMFUL BACTERIA THAT CAN CAUSE SERIOUS ILLNESS IN CHILDREN, THE ELDERLY AND PERSON S WITH WEAKENED IMMUNE SYSTEMS. Under HACCP, fruit juices must be processed to achieve a 5 log pathogen reduction (21 CFR 120.24) of This is most commonly achieved through a thermal pasteurization treatment. However, citrus juice processors who produce fresh juice have an option to apply a cumulative 5 log reduc tion plan to fruit surfaces. After HACCP has been made mandatory to juice processors, no outbreak associated with the consumption of pasteurized juice has occurred. Orange Juice Originating in Southeast Asia, orange trees are grown in China, Brazil, U.S. Spain, Mexico, and Turkey (Orange book, 1998). Approximately 55 million tones of oranges are produced throughout world annually with 40% of this production processed and consumed as orange juice. Brazil and Florida in the U.S. represent the highest global orange juice producers ( USDA, 2011 juice, 413 kg peel, rag, and seeds, 30 kg pulp (Orange book, 1998). Orange juices can be classified as ready to drink (RTD) orange juices, fo rtified orange juices, concentrated orange juices, and RTD orange products which may not be called as orange juice, such as orange nectars, orange juice drinks, and orange flavor drinks (Orange book, 1998).
26 Orange Juice Process The process of juice roughly includes four main steps: washing, sorting, extraction, and pasteurization. The first step of juice production is to wash the harvested fruits. The purpose of the washing is to reduce microbial population on fruit, eliminate pesticide residues, and remove the foreign matters such as soil, sand, and mud. Spoiled fruit should be discarded before washing in order to prevent other fruits from contamination. Washing efficiency is described by the total number of microorganisms present on fruit surface before a nd after washing (Dauthy, 1995). Removal of partially or completely decayed fruit and any foreign substance is the most significant factor in the preparation of fruit for production of fruit juice. Determination of variety and maturity of fruits is also pa rt of initial sorting. Sorting can be done automatically or by hands. The purpose of extraction process is to separate the liquid phase of fruit from solid particles. Mainly, two types of extractors manufactured by different companies are common in citrus processing (Ramaswam y, 2005). The Brown extractor (automatic machinery corporation) and the rotary press involve cutting the fruit in half and reaming the each half of fruit. The reaming extraction methods results with separation of white skin lining, segm ent walls, cell tissue, piece of skin, and seeds in the juice and these residues must be taken away from juice (Rutledge, 2001). The John Bean Technologies Corporation (JBT) citrus juice extractors do not require fruit to be halved prior to extraction, and ju ice processors (Lozano, 2006). The principle of JBT machines depends on squeezing the fruit through hole from the center of bot tom cup shaped set of fingers. After extraction of juice, pasteurization is ap plied to obtain safe an d more stable final products.
27 Thermal Orange Juice Pasteurization and Alternatives Pasteurization is the main step of orange juice production that eliminates foodborne pathogens, and inactivates spoilage microorganisms. Pasteurizatio n also helps to stabilize cloudy appearance by inactivating enzymes, especially pectin methyl esterase (PME) to prevent cloud loss. Pasteuriz ation parameters chosen must reduce the number of pertinent microorganism at least 5 log in accordance with FDA reg ulations. Pasteurization may also cau se unavoidable changes in of ph y si cochemical, nutritious, and sensory properties of juice (Lee and Coates, 2002; Moshonas et al., 1993; Moshonas and Shaw, 1995). Currently, high temperature short time (HTST) pasteurizat ion is used as a heat treatment for citrus pasteurization. In HTST, the optimal temperature is 76.6 87.7C with a holding time between 25 and 30 s (Moyer and Aitken, 1980), and specifically for orange juice, conventional HTST treatment is 98 C, 21 s (Rivas et al., 2005). Alternatives to the thermal pasteurization of orange juice include microwave processing, ohmic heating, stem and hot water treatments, high pressure processing, UV radiation, ele ctromagnetic fields, ozone, irra d i ation, chemical treatment, a nd pulsed field electric. (NACMCF, 2006). Pulsed electrical field (PEF), high pressure treatment microwave heating, and gamma radiation are some of the last developments in juice processing (Kulwant and Kuldip, 2006). PEF is a commonly studied non therma l processing technology, based on the use of an external electric field to destabilize cell membranes by applying high voltage pulses (generally 20 (Cserhalmi, 2006). As a non thermal pasteurization technique, UV radiation pasteurization is an FDA approved technique to reduce pertinent microorganism population by 5 log in some juices (FDA, 2011). This technique also doubles shelf life of refrigerated juice compare to untreated or unpasteurized juice (Piotrowski, 2003).
28 Acid Adaptation and Thermal Resistance Acid Adaptation Foodborne pathogens are exposed to various kinds of stresses in the environments and in the gastrointestinal system of their hosts. During all steps of food production foodborne pathogens encou nter to different stresses, including pH, temperature, and osmotic stress. To endure these stressful conditions, pathogens develop pr otective mechanisms to survive. Some bacteria species, including E. coli O157:H7, Salmonella spp., and L. monocytogenes are able to protect themselves from acidic conditions as a result of acid adaptation. As a result of acid adaptation of E. coli O157:H7, Salmonella spp, and L. monocytogenes not only enhanced the survival capability of these pathogens, but also increased th eir heat resistance in apple, orange, and white grape juices (Mazzotta, 2001). Acid adaptation of E. coli O157:H7 increased Decimal reduction times ( D values) in apple cider and orange juice (Ryu and Beuchat, 1998) and also significantly increased D valu es of Salmonella and E. coli O157:H7 in watermelon and cantaloupe juice (Sharma et al., 2005). Detailed descriptions of acid adaptation mechanisms employed by these pathogens, especially in stationary phase, are available. Factors influencing the acid tole rance response (ATR) The acid tolerance response (ATR) is one of survival protection mechanisms used by microorganisms to survive in low pH environments after its induction as a result of growth in moderately low pH or exposure to mild acidic conditions ( lvarez Ordez et al., 2012). There are a number of factors influencing the ATR system. The presence of inhibitory and other chemicals may also affect the development of ATR (Rowbury 1995), as may the type of acid for E. coli O157:H7 (Beales, 2004).
29 The induction of acid tolerance differs with environmental factors and the physiological state of the cells. The effectiveness of ATR directly depends on the pH of the adaptation conditions and the duration of adaptive period (Davis et al., 1996), and is also dependent on the state of the cell for some organisms. L. monocytogenes achieved greater acid resistance in stationary phase than in log phase (Davis et al., 1996). Habituation to acidic environments at different pH ranges affects the increase of acid to lerance differently for L. monocytogenes (increase at pH 5.0 6.0), E. coli O157:H7 (increase at pH 4.0 5.5), and S Typhimurium (increase at pH 4.0 5.0) (Koutsoumanis and Sofos, 2004). Salmonella Typhimurium cells in log phase may reach their maximum acid tolerance capacities when they are habituated at pH around 5.5, however, in stationary phase lower pH values, around 4.5, are required (lvarez Ordez et al.(2012). In the log phase, S. Typhimurium in pH 4.3 for 10 to 20 min developed less effective acid tolerance than cells in pH 3.3 for 30 to 40 min (Foster, 1993) and log phase cells that were exposed to different pH values between pH 5.0 and 8.0 for 1 hour before acid challenge varied in their acid resistance (Kwon and Rickie 1998). Escherichia co li and Salmonella enteritidis incubated at 42 45C were more acid resistant than cells grown at37C; gro wth at 20 25C decreased the acid tolerance beyond that developed at 37C (Humprey et al., 1993). Temperature is another factor that influences ATR de velopment. At 10C, S Typhimurium lower acid tolerance developed compare to the acid tolerance of adapted cells at 25, 37, and 45C ( lvarez Ordez et al., 2010). Under different acidic conditions and incubation temperatures between 5 and 35C, the aci d tolerance of L. monocytogenes increased with increases in temperature (USDA ERRC, 2003). The ATR system of bacteria can be induced by exposure to various acids including citric, acetic, lactic, malic, hydrocholoric, ascorbic ( lvarez Ordez et al. 2009 ). Gluconic acid
30 (pentahydroxycaproic acid) is a final production of enzyme glucose oxidase in fun gi and enzyme glucose dehyrogen ase in bacteria. When glucose is oxidized by one of these enzymes, gluconic acid is formed after hydrolization step of glucono del ta lactone (Ramachandran et al., 2006). Gluconic acid induces the ATR system as a result of glucose fermentation during bacterial growth (Buchanan and Edelson, 1996). The ATR of E. coli Resistance against low pH can provide a competitive advantage for E coli O157:H7 in acidic foods, heat treatment and passing through the stomach of a host. Generally, ATR is induced by exposing bacteria to moderately low pH (5.5 or lower for E. coli ) that promote expression of acid shock proteins. Acid shock proteins pre vent protein denaturation and refold proteins that have already been denatur ed (Diez Gonzalez and Kuruc, 2009). Several compounds, including glucose, glutamate, aspartate, FeCl3, KCl, and L proline can induce habituation at neutral pH (Rowbury et al., 1989 ). The mechanisms of acid adaptation of E. coli O157:H7 has been studied in different types of acidic condition and three ATR systems are commonly accepted (Foster 2000). These systems include: acid induced oxidative system, an acid induced arginine depend ent system, and a glutamate dependent system (Lin et al., 1996). A cid resistance system 1, called oxidative or glucose repressed system, is one of the characterized ATR systems in stationary phase. This glucose repressed system is different from the other ATR systems as it does not require exogenous amino acids in acid adaptation process. It does however require the RpoS sigma factor (Ma, 2003). The key regulators of the acid P (cAMP), and cAMP receptor protein (CRP) ( Castanie Cornet et al., 1999). RpoS is critical for the gene expression transition period from log phase to stationary phase, and for survival under acid stress. However, its role in type 1 ATR is questioned (Ric hard and Foster, 2003). While the
31 structural components of ATR 1 remain to be completely defined, it is a known component of E. coli O157:H7 survival capability under acidic conditions at pH values above 3 (Audia et al., 2001). Acid resistance system 2, a glutamate dependent system, occurs during stationary phase, and acts independent of RpoS. This system includes several proteins that are located in different part of cells and extracellular glutamate The system is located at the cell membrane and glutamat e decarboxylase isoenzymes (GadA and Gad B), structural components of this system that uses intracellular glutamate aminobutyric acid (GABA), present in the cytoplasm (Hersh et al., 1996). In glutamate induced system, Glutamate decarboxylase isoenzymes pro tects cell from acid stress by consuming one proton then releasing CO2 in acid resistance process. Finally, glutamate aminobutyric acid (GABA) is carried out of cell by simultaneous uptake of glutamate. The last resistance system ATR 3, the arginine depend ent system, is similar to ATR 2, where the decarboxylation of arginine to agmatine and the interchange of arginine and agmatine form the basic mechanisms of this system. ATR 3 system is induced at low pH numbers under anaerobic conditions in rich medium. W hen grown under acidic conditions, E. coli O157:H7 cells have an internal pH between 4 to 5, and the AdiA enzyme, which is one of two enzymes synthesized by two arginine decarboxylase genes starts to function. AdiA, PLP dependent enzyme, is the enzyme that catalyzes the exchange of argi nine to agmatine (Foster 2004). The ATR of Salmonella In the last two decade, a number of studies, most using S. Typhimurium, have described Salmonellas ATR mechanisms. While most experiments have focused on the serotype S Typhimurium, similar adaptive responses have been reported for other serotypes ( lvarez Ordez et al., 2012). Salmonella including Agona, Anatum, Gaminara, Mbandaka, Michigan, Montevideo, Poona, Reading and Saintpaul have all been readily adapted in appl e, orange, and
32 tomato juice or broth supplemented with glucose (Yuk and Schneider, 2006; Bacon et al., 2003) After acid adaptation in apple or tomato juice, cells were the most acid tolerant in simulated gastric fl uid (Yuk and Schneider, 2006). Salmonella also use three ATR responses to survive in acidic conditions including the general sigma factor (RpoS) response induced in log or stationary phase, and an arginine dependent system ( Diez Gonzalez and Kuruc, 2009). The induction of ATR during the log phase leads to the production of more than 50 proteins, and time to development is generally faster than in stationary phase. It is dependent on the RpoS system and gene the fur (Hall and Foster, 1996). RpoS is responsible for controlling the expression of at l east 10 acid shock proteins (ASPs; Baik et al., 1996; Lee et al., 1995), while Fur (Ferric uptake regulation) is a regulatory gene that works as a transcriptional repressor and a controller of ASPs (Hall and Foster, 1996). The stationary phase dependent AT R is activated when cells are exposed to low pH for a long time. The adaptation period is slower during gradual decrease of pH (Diez Gonzalez and Kuruc, 2009). In both cases, the ATR is ompR dependent. OmpR (Osmolarity Response Regulator) is a regulator th at is responsible for the control of the expression of acid induced virulence operon ssrAB and required for the OmpR/EnvZ regulation system (Bang et al., 2002). In addition to the log and stationary ATR, S. Typhimurium has an arginine dependent ATR, what i ncludes an arginine decarboxylase (AdiA) and antiporter, that is only activated under anaerobic conditions (Kim et al., 2002). AdiA consumes the acidic proton by converting arginine into agmatine in the cytoplasm ( lvarez Ordez et al., 2012). The ATR of Listeria Listeria monocytogenes develops one of the most advanced ATR in mildly acidic conditions (pH 5 6; Barak et al., 2005). At least four different acid stress response mechanisms
33 can be activated in L. monocytogenes (Becker et al., 1998). The activat ion of one system only occurs in log phase, and is induced by low pH (Ferreira et al., 2003). Davis et al. (1996) showed that maximum acid resistance of Listeria in log phase was observed when the cells were exposed to pH 5.0 for 1 h prior to challenge at pH 3.0, even though medium levels of protection were developed by exposure to pH values ranging from 4.0 to 6.0. When L. monocytogenes enters the dependent ATR system develops (Becker et al., 1998). The third system independ ent mechanism that may also develop in stationary phase (Becker et al., 1998). A GAD based ATR system has also been described (Diez Gonzalez and Kuruc, 2009) and consists of three glutamate decarboxylas e genes ( gadD1, gadD2 and gadD3 ) similar to that of E. coli (Cotter et al., 2001). Thermal Tolerance and Experimental Thermal Treatments A number of experimental methodologies have been used to conduct therma l tolerance experiments. The use of a water bath being the most common method to hold the inoculated m edium or food at constant temperature for a give time. An alternative method is the inoculation of an already heated medium or food on a heating mantle or hot plate stirrer. For liquid food samples, including fruit juices, three variations on these methods are common. Microcapillary tubes are a very effective method, and preferred by several researchers (Sharma et al., 2005; Splittstoesser et al., 1996). In this method, low volumes of inoculated juice in microcapillary tubes are held in waterbaths for prede termined time intervals, following the set time, tubes are cooled immedieately on ice. Alternatively, juice can be heated in advance with a heating mantle priot to inoculation, inoculated with microorganisms, and samples pulled at predetermined time interv als from th larger ino culated sample(Mazotta, 2001). Finally, a submerged coil heating apparatus, consisting of a narrow internal diameter (~2.0828 mm) bore stainless steel coil that is completely submerged in a heat controlled water bath can be used (Enac he et al., 2011). This
34 device is the newest technology for thermal tolerance experiments, and is gaining popularity (Enache et al., 2011). Thermal tolerance experiments may use either cocktail of multiple strains or single strains, depending on the purpose of study. The use of single strain allows researchers to see variability among the strains, and to have more precise conclusions. For example, L. monocytogenes strains from Brie cheese (Brie 1), a cabbage outbreak (LCDC), a human isolate from milk relate d outbreak (Scott A), and another human isolate have significantly different D values at 56C (16.0, 10.4, 7.4, 5.7 min, respectively; Golden et al., 1988); and STEC E. coli serogroups O26, O45, O103, O111, O121, O145, and O157:H7 have D values at 56C ran ging between 2.14 min (O26) 8.37min (O157:H7) in apple juice (Enache et al., 2011). The use of cocktail of strains mimics what may occur in practice, and as all strains are used at once, these studies are often much quicker to complete. The use of cocktai ls also allow s insight into the effect of possible strain interaction on results. However, the most or least resistant strain in a cocktail may cause mi sleading result due to masking. Heat treatments that are used by food processors to eliminate pathogens are often based on experimental data. Heat resistance of distinct Salmonella serotypes, E. coli and L. monocytogenes strains have been studied in numerous types of food products and laboratory media. The thermal tolerance of these pathogens varies dependin g on the formulation of media and characteristic of foods including total solids, acidity, and water acti vity (Doyle and Mazotta, 2000). Thermal resistance of E. coli Escherichia coli is one of the most commonly studied microorganisms in thermal tolerance researches due to its high potential risk of outbreak in different type of foods, high thermal resistance response, and ease of study. Survival of E. coli O157:H7 isolate 204P is
35 increased by fat presence in ground beef (7%, 10%, and 20% fat), pork sausag e (7%, 10%, and 30% fat), chicken and turkey (3% and 11% fat) from D values at generated at 60 C (Ahmed et al., 2006). The D values (min) varied in different meat samples as follows: 0.45 0.47 in beef, 0.37 0.55 in pork sausage, 0.38 0.55 in chicken and 0. 55 0.58 in turkey. When heat resistance of E. coli was tested by using low (3%) and high fat (11%) turkey with mixture of 8% NaCl, 4% sodium lactate, and 0.5% polyphosphate at 52, 55, 57, and 60 C. the addition of additives enhanced survival of E. coli (Ko trola and Conner, 1997). In fruit juices, the D value s provided in literature are primarily for E. coli The heat resistance of E. coli O157:H7was not affected by increasing the soluble solid of apple juice from 11.8 to 16.5Brix ; but was altered by the r eduction of pH from 4.4 to 3.6 by addition of malic acid and the addition of sorbic and benzoic acid at 52 C (Sp littstoesser et al., 1996). The acid adaption of stationary phase E. coli O157:H7 increased the thermal inactivation activation time compared t o non adapted stationary phase cells in apple, orange, and white grape juices, where D values at 60 C for acid adapted and non adapted E. coli O157:H7 were 1.5 and 0.8 min in apple juice, 1.7 and 1.1 min in orange juice, and 1.2 and 0.7 min in white grape jui ce, respectively (Mazotta 2001). Serogroup variability of E. coli can affect the thermal resistance, where D values of stationary phase and acid adapted cells of E. coli strains from serogroups O26, O45, O103, O111, O121, O145, and O157:H7 were invest igated by using an immersed coil apparatus at various temperature. At 60C E. coli O157:H7 and O103 showed the highest D values, 1.37 and 1.07 min, respectively (Enache et al., 2011). Thermal resistance of Salmonella The thermal inactivation parameters of S almonella may be affected by the type of experiment, methodology, culture conditions (e.g. acid or none adapted), strains and heating mechanism. The thermal resistance of Salmonella in whole eggs, egg yolks, and whites has been
36 widely studied under conditi ons of variable pH, water activity (including salt and sugar concentrations), age and incubation temperature of Salmonella inoculum, previous heat shock, storage conditions of eggs, the presence of lactic acid, hydrogen peroxide, EDTA, nisin, and p olyphosp hate (Doyle and Mazotta 2000). Salmonella survives in egg yolk better than in egg white, and heat resistance differs according to serotype. In milk and dairy products, an increase in concentration of total solid in milk enhances the heat resistance of Sal monella where S. Typhimurium at 55 C have 4.7 min D value at 10% solids but when the solid content increases to 42%, the D value; increased to 18.3 min. Salmonella Seftenberg is often considered one of the most heat tolerant Salmonella serotypes. D value s for S. Se ftenberg in ground beef at 55, 60 and 65 C we re 211.4, 13.2, and 3.4 min, respectively, and are higher than reported D values for other Salmonella serotypes in the ground beef and E. coli O157:H7 in the same conditions (Doyle and Mazotta, 2000). Chocolate, wheat and corn flour, corn soy milk blends, shellfish, coconuts and pecans, alfalfa seeds are some of the other foods that heat resistance of Salmonella have been studied. The number of studies evaluating thermal inactivation of Salmonella in fruit juices is limited. The acid adaptation of Salmonella increases its thermal resistance in apple, orange and white grape juice at different temperatures (Mazotta, 2001). D values of Salmonella in phosphate buffered saline (PBS) and apple juice at 55 C are similar at 0.51 and 0.49 min, respectively (Gabriel and Nakano, 2009). Thermal resistance of Listeria Similar to Salmonella thermal resistance of L. monocytogenes is affected by strain variability, heating mechanism, previous growth conditions of cel ls, and other factors, such as acid and heat shock. The effectiveness of thermal inactivation on L. monocytogenes in meat and poultry is affected directly by fat content of meat, age of inoculums, heat source, and exposure of
37 the bacteria to acid, heat sho ck and preservatives (Doyle et al., 2001). When inoculated on fresh pork, L. monocytogenes have greater D values at 62 C compare to cells inoculated into three month old ground pork (Kim et al., 1998). In high fat (30.5%) ground beef, L. monocytogenes surv ives better at 57.2 and 62.8 C than cell in low fat (2%) ground beef (Fain et al., 1992). Slow heating time (1.3 C/min) also leads to increase Listeria survival compared to fast heating (8 C/min) in ground pork (Kim et al., 1994). Thermal tolerance studie s on L. monocytogenes have been conducted in various kinds of milk and dairy products including raw milk, sterile milk, whole milk, skim milk, cream, and butter. Listeria monocytogenes survives better in raw milk than in sterile milk and in skim milk tha n in whole milk at temperatures below 63 C (Bradshaw et al., 1995). During cheese production, the heat tolerance of L .monocytogenes varies by type of cheese and applied temperatures. In egg and egg products, Listeria is more thermal resistant than Salmone lla serotypes tested under the same experimental conditions (McKenna et al., 1991; Muriana et al., 1996). Addition of 10% sodium chlorine or 10% sucrose increased the thermal tolerance of Listeria (Palumbo et al., 1995). Listeria monocytogenes strain Scott A was more durability to heat than strain LCDC, and was more heat tolerance at pH 5.6 than at pH 4.6 (Beuchat et al., 1986). Thermal tolerance of Listeria has also been extensively studied fish and shellfish, vege tables and other meat products.
38 CHAPTER 3 MATERIAL AND METHODS Juice One brand of 100% pure and single strength pasteurized orange juice without preservatives and pulp was purchased from a local supermarket (Publix, Winter Haven, FL). The pH of juice was obtained via Denver model UB 5 pH meter w ith a combination electrode ( Denver Instrument Inc., Arvada, CO, USA). The soluble solid content of juice was measured by using The Leica Mark II plus digital refractometer (Buffalo, NY, U SA). Juice was filled into 50 mL conical centrifuge tubes (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) aseptically, and stored at 20 C until use. One tube of juice was used for each experimental trial. Strains Used All strains used in this study are listed in Table 3 1. Three strains of shiga toxin producin g Escherichia coli three serotypes of Salmonella, and three strains of Listeria monocytogenes were toxin producing E. coli strains including E. coli O111 (MDD339; clinical i solate from an apple cider outbreak of 2004, New York), E. coli O157:H7 (MDD338; clinical isolate from an apple juice outbreak of 1991, Massachusetts) and E. coli O157:H7 (F4546; clinical isolate from a sprout outbreak of 1997 ) were obtained from Dr Dany The Salmonella serotypes include Typhimurium (ATCC 1402 8; orange juice outbreak of 1999 ), Gaminara (CDC H0622); orange juice outbreak of 1995), and Muenchen (MDD30; orange juice outbreak of 1999). The three L. monocytogenes strain s from Dr L. monocytogenes (LCDC 81 861; raw cabbage outbreak of 1981), L. monocytogenes (Scott A; human milk outbreak of 1983), and L. monocytogenes (v7; milk associated outbreak of 1985).
39 In oculum P reparation All str ains, stored at 80 C, were converted to working cultures by streaking on non selective Tryptic Soy Agar (TSA; Becton, Dickinson and Company, Sparks, MD, USA) plates for E. coli and Salmonella, and on Brain Heart Infusion (BHI) agar (Becton, Dickinson and Company, Sparks, MD, USA) plates for L. monocytogenes Plates were incubated at 37 2 C for 24 2 h. Inoculum preparation was different for non adapted and acid adapted cells. For non adapted stationary phase inoculum preparation, one isolated colony fro m each strain of E. coli O157:H7, and Salmonella were grown in Tryptic Soy Broth (TSB;Becton, Dickinson and Company) at 37 2 C for 18 h. One isolated colony from each L. monocytogenes strain was incubated in BHI broth at 37 2 C for 18 h. One loopful of overnight growth was transferred to a new tube of broth and incubated at 37 2C for 18 h. For acid adapted stationary phase inoculum preparation, strains of E. coli and Salmonella were grown in TSB supplemented with 1% glucose (10 g/l), TSBG; Fisher Sc ientific, Lawn, NJ, USA) at 37 2 C for 18 h as described by Buchanan and Edelson (1996). For strains of L. monocytogenes BHI supplemented with 1% glucose (10 g/l) BHIG was used at 37 2 C for 18 h. One loopful (10L) of overnight growth was transferred to a new tube of broth and incubated at 37 2 C for 18 h. The addition of glucose to the broth induces acid production by the E. coli, Salmonella, and L. monocytogenes strains, resulting in an acidic environment and the development of acid adaptation. F ollowing incubation, cells were collected by centrifugation at 3000 x g for 10 min (Allegra X 12, Beckman Coulter, Fullerton, CA) The s upernatant was removed and 10 mL of 0.1% peptone (Becton, Dickinson and Company) water was vortexed with the pellet to w ash cells. Cells were centrifuged and the washing step was repeated. After the cells had been
40 washed three times, the pellets were resuspended in 5 mL orange juice to obtain the desired concentration of cells (10 8 10 10 CFU/mL ). Thermal Treatment of Ino culated J uice Each strain of STEC, Salmonella and L. monocytogenes was inoculated into orange juice as described above. Two sterile microcapillary tubes (1.5 1.8 (ID) 90 mm; Kimble Kontes, Vineland, NJ, USA) with one head heat sealed, were each injecte d with 50 L of inoculated orange juice. The aseptic injection was achieved using a sterile 20 gauge 4 inch deflected point needle (Popper and Sons, Inc ., Hyde Park, NJ, USA), and 1 mL syringe (Luer Lok Tip, Franklin Lakes, NJ, USA). The open end of the mi cro capillary tubes was sealed with a Bunsen burner flame. To prevent cracking of the tubes from sudden temperature change before thermal treatment, the microcapillary tubes were held at room temperature for 30 min prior to the beginning of the experiment Inoculated, sealed, microcapillary tubes of each strain were immersed into water baths (LAUDA Brinkmann, ECO Line RE120, Ger many) at desired temperatures. Two sealed microcapillary tubes filled with inoculated orange juice were placed in a preheated cult ure tubes in water bath during heat treatment. Time and temperature values were chosen a ccording to preliminary tests. Briefly, to determine each time interval, the time required for a one log reduction on average was used as reference according to result of preliminary experiments. To estimate D values the inoculated juice was exposed to the selected temperature until an average five log reduction was achieved Last time interval used was determined based on total of five log reduction of cells from initi al concentration. Total of a t least seven time intervals were used. Three temperatures: 55, 58, and 60 C for Salmonella ; 56, 58, and 60 C for E. coli and L. monocytogenes were tested. Temperature and time intervals for each strain are available in Table s 3 2, 3 3, and 3 4. After heat treatment, microcapillary tubes were poured immediately
41 onto ice, and then further cooled and sterilized by immersing 70% e thanol at room temperature for one minute. Excessive alcohol was removed by rinsing microcapillary t ubes in sterile deionized water at room temperature. Thermally treated and sterili zed microcapillary tubes (0.1 mL inocula ted juice) were added into 10 mL of ste rile 0.1% peptone water in 15 mL conical centrifuge tubes (Becton, Dickson and Company), result ing in an initial 1:100 dilution. Microcapillary tubes were crushed using a sterile glass rod, and centrifuge tubes were vortexed prior to microbiological analysis. Six replicates were used for each time interval at all temperatures; duplicate samples of e ach time interval were examined in triplicate. Microbiological A nalysis Populations of E. coli Salmonella and L. monocytogenes in thermally treated juice were determined by spread plati ng samples and dilutions (0.1 mL ) onto TSA for E. coli and Salmonella and BHI agar for L. monocytogenes supplemented. All media was supplemented with 0.1% sodium pyruvate (Sigma Aldrich, St Louis, MO, USA; TSAP or BHIP) to enhance the recovery of injured cells (Knudsen et al., 2001; Yamamoto and Harris, 2001). Plates were incubated at 37 2 C for 24 48 h depending on strain to achieve full recovery of survived and injured cells prior to counting. To increase the sensitivity and lower the li mit of detection to 2 log CFU/mL 1 mL of the initial dilution (10 2 ) was spr ead o ver 4 plates (0.25 mL /plate). Statistical A nalysis The number of enumerated pathogens (log CFU/mL ) after heat treatment was plotted on the y axis versus heatin g time (min) on the x axis. Linear regression was used to determine best fit lines, and to calcul ate D values of each strain by analyzing data with JMP software (Version 9.0.2 SAS Insti tute Inc., Cary, NC, USA 2010). z values ( C) were determined by calculating inverse slope of regression line from the plot of log D value versus the temperature for the D value. D values of acid adapted and non adapted strains at the same temperature were compared
42 statistically ( P < 0.05). D values of E. coli, Salmonella, and L. mon ocytogenes strains at the same temperature were compared statistically ( P < 0.05) in the same specie group. Mean z values of each species were compared statistically by using Analysis of Variance (ANOVA) ( P < 0.05). To find out statistical significan ces among the D values, the Analysis of Covariance (ANCOVA a combination of ANOVA and linear regression) was used via JMP software (SAS Institute Inc.).
43 Table 3 1. Isolates used for thermal processing experiments. Strain or serotype Strain designation Year of outbreak Sources E. coli O157:H7 F4546 1997 Clinical alfalfa sprout outbreak E. coli O157:H7 MDD338 1991 Clinical apple juice outbreak Massachusetts E. coli O111 MDD339 2004 Clinical apple cider outbreak New York S. Typhimurium ATCC 14028 19 99 Orange juice outbreak S. Gaminara CDC HO662 1995 Orange juice outbreak S. Muenchen MDD30 1999 Orange juice outbreak L. monocytogenes LCDC 81 861 1981 Raw cabbage outbreak L. monocytogenes Scott A 1983 Human milk outbreak L. monocytogenes v7 1985 Milk associated outbreak
44 Table 3 2. Time intervals used in heat treatment experiments of STEC strains to plot regression lines in determination of D values. 56 58 60 Time intervals (min) Non Acid Non Acid Non Acid T1 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c T2 1.5 a,b,c 1.5 a,b,c 1 a,b,c 1 a,b,c 0.5 a,b,c 0.5 a,b,c T3 3 a,b,c 3 a,b,c 2 a,b,c 2 a,b,c 1 a,b,c 1 a,b,c T4 4.5 b 4.5 3 a,b,c 3 a,b,c 1.5 a,b ,c 1.5 a,b,c T5 6 a,b,c 6 a,b,c 4 a,b,c 4 a,b,c 2 a,b,c 2 a,b,c T6 7.5 b 7.5 5 a,b,c 5 a,b,c 2.5 a,b,c 2.5 a,b,c T7 9 a,b,c 9 a,b,c 6 b 6 b 3.0 a,b,c 3.0 a,b,c T8 12 a,b,c 12 a,b,c 7 a,c 7 a,b,c 3.5 a,c 3.5 a,c T9 15 a,c 15 a,b,c 4 a T10 18 c a Time intervals for E. c oli O157:H7 from human feces sprout outbreak (F4546) b Time intervals for E. coli O157:H7 from apple juice outbreak Massachusetts (MDD338) c Time intervals for E. coli O111 from apple cider outbreak New York ( MDD339)
45 Table 3 3. Time intervals used in heat t reatment experiments of serotypes to plot regression lines in determination of D values. 56 58 60 Time intervals (min) Non Acid Non Acid Non Acid T1 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c T2 0.5 a,b,c 0.5 a,b,c 0.17 a,b,c 0.1 7 a,b,c 0.17 a,b,c 0.17 a,b,c T3 1 a,b,c 1 a,b,c 0.33 a,b,c 0.33 a,b,c 0.33 a,b,c 0.33 a,b,c T4 1.5 a,b,c 1.5 a,b,c 0.5 a 0.5 a 0.5 a,b,c 0.5 a,b,c T5 2 a,b,c 2 a,b,c 0.67 a,b,c 0.67 a,b,c 0.67 a,b,c 0.67 a,b,c T6 2.5 a,b,c 2.5 a,b,c 1 a,b,c 1 a,b,c 0.83 a,b,c 0.83 a,b,c T7 3 b,c 3 b,c 1.33 b,c 1.33 b,c 1 a,b,c 1 a,b,c T8 3.5 a 3.5 a 1.67 a,b,c 1.67 b,c T9 4 b 4 b,c 2 b,c 2 b,c T10 5 a 5 a a Time intervals for S. Typhimurium from orange juice outbreak (ATCC 14028) b Time intervals for S Gaminara from orange juice outbreak (CDC H 0622) c Time intervals for S. Muenchen from orange juice outbreak (MDD30)
46 Table 3 4. Time intervals used in heat treatment experiments of L. monocytogenes strains to plot regression lines in determination of D values. 56 58 60 Time int ervals (min) Non Acid Non Acid Non Acid T1 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c 0 a,b,c T2 1 a,b,c 1 a,b,c 0.5 a,b,,c 0.5 a,b,c 0.25 a,b,c 0.25 a,b,c T3 2 a,b,c 2 a,b,c 1 a,b,c 1 b,c 0.5 a,b,c 0.5 a,b,c T4 3 b 3 a,b 1.5 1.5 a,b 0.75 0.75 a T5 4 a,b,c 4 a,b,c 2 a,b,c 2 a ,b,c 1 a,b,c 1 a,b,c T6 5 a,b,c 5 a,b 2.5 a,b 2.5 a,b 1.25 a 1.25 a T7 6 a,b 6 a,b,c 3 a,b,c 3 a,b,c 1.5 a,b,c 1.5 a,b,c T8 7 7 a 3.5 3.5 a 1.75 1.75 a T9 8 a,b,c 8 b,c 4 a,b,c 4 b,c 2 a,b,c 2 b,c T10 5 c 5 c 2.5 b,c 2.5 b,c a Time intervals for L. monocytogenes from raw ca bbage outbreak ( LCDC 81 861 ) b Time intervals for L. monocytogenes from human milk outbreak ( Scott A ) c Time intervals for L. monocytogenes V7 from milk associated outbreak (v7)
47 CHAPTER 4 RESULTS Time Interval Determination The most appropriate time interva ls for sampling each strain and serotype at a particular temperature were determined during preliminary thermal death time experiments. Time intervals used to determine D values of each strain varied both among the species and the strains or serotypes of each species. Acid adaptation also influenced the use of different time intervals, even for the same strain at the same temperature. The maximum applicabl e time intervals, by pathogen, we re: Salmonella serotypes at 55C, 5 min; STEC and L. monocytogenes s trains at 56C; 18 and 8 min, and STEC Salmonella and L. monocytogenes at 58 and 60C: 7, 5, 2 min and 4, 2.5, 1 min, respectively. D and z value Determination for Strains and Serotypes of Pathogens U sed To estimate D values of each strain and serotypes o f pathogens tested, populati on of survived cells (log CFU/mL ) from thermal destruction trea tment versus time (min ) were plotted to obtain linear curve at desired temperatures. Linear regression lines for both acid adapted and non adapted STEC strains were plotted on the same plots to compare regression trends at all temperatures tested, and shown in Figures 4 1, 4 2, 4 3. Similar plots were prepared for Salmonella serotypes and L. monocytogenes strains to observe linear curves as shown Figures 4 4, 4 5, 4 6 and Figures 4 7, 4 8, 4 9, respectively. The equations of these linear regression lines were used to calculate D values. All equations of thermal death curves used to calculate D values of STEC strains, Salmonella serotypes, and L. monocytogenes strains w e re listed in Tables 4 1, 4 2, and 4 3, respectively. Similar to D value determination, z values were calculated by using the linear regression equations of plots of log D values versus temperature (C) for each strain. Linear regression
48 lines of z values for STEC strains, Salmonella serotypes, and L. monocytogenes strains we re shown in Figures 4 10, 4 11, 4 12, respectively. Also, equations of these linear curves to calculate z v alues we re listed in Table 4 4. D and z values of STEC S trains D and z value of STEC strains grown in TSB and TSBG and heated at 56, 58, and 60C were shown in Table 4 5. D values for acid adapted and non adapted E. coli O157:H7 (F4546) at 56, 58, and 60C are 2.81 0.06 and 2.72 0.07 min, 1.35 0.07 and 1.28 0.07 min, and 0. 61 0.06 and 0.52 0.05 min, respectively. A similar trend was seen for E. coli O111 (MDD339), where D values for acid adapted and non adapte d cells at 56, 58, and 60C are 3.41 0.06 and 3.05 0.06 min, 1.60 0.05 and 1.39 0.04 min, and 0.61 0.05 and 0.54 0.05 min, respectively. D values for acid adapted E. coli O157:H7(MDD338) we re similar to the other two STEC strains at 56, 58, and 60C, and they are 3.21 0.08 an d 1.31 0.04 and 0.61 0.08, respectively. However, D values for non adapted strain MDD338, are significantly lower than as for the other two STEC strains at 56, 58C ( P < 0.05), where D values for non adapted form of this strain at 56, 58, 60C are 1.93 0.07, 1.04 0.12, and similar to others 0.52 0.13, respectively. z value s for acid adapted and non adapted STEC st rain F4546, MDD338, and MDD339 we re, 6.0 and 5.6, 5.5 and 7.0, and 5.4 and 5.3, respectively. Acid adapted STEC have higher D values than non adapted cells, at all temperatures for all strains with the exception of strain F4546 at 56, 58C ( P < 0.05). D values for E. coli O111 we re higher at 56, 58C evaluated than those of both E. coli O157:H7 strains ( P < 0.05). D value differences between acid adapted and non adapted strain MDD338 cells were greater than in strai n MDD339 and strain F4546 at 56 and 58 C. With the exception of D values for strain F4546 at 56 and 58C, all non adapted STEC strains have significantly lower heat resistance compare to acid adapted STEC strains ( P < 0.05). Both acid adapted and non adap ted strain E.
49 coli O111 had the highest D values at all temperatures, where the D values for acid adapted and non adapted strain E. coli O111 at 56, 58, and 60C are 3.41 0.06 and 3.05 0.06 min, 1.60 0.05 and 1.39 0.04 min, and 0.61 0.05 and 0.54 0.05 min, respectively (Table 4 5). Although D values for both acid adapte d and non adapted STEC strains we re significantly different from each other in the same adaptation group at 56 and 58C ( P < 0.05), no difference exists among strains for both aci d adapted and non adapted groups at 60C ( P > 0.05) in the same adaptation group. Particularly, D values for aci d adapted STEC strains at 60C we re the same (0.61 08) ( P > 0.05) (Table 4 5). D values of Salmonella S erotypes D and z values of acid and non acid adapted Salmonella serotyp es, heated at 55, 58, and 60C we re shown in Table 4 6. All Salmonella species demonstrated similar responses to heat at all temperatures. D values for acid adapted and non adapted S. Typhimurium at 55, 58, and 60C are 1.03 0.08 and 0.98 0.09 min, 0.28 0.10and 0.30 0.10 min, and 0.17 0.09 and 0.17 0.09 min, respectively. A similar trend was seen for S. Gaminara, where D values for acid adapted and non adapted cells at 55, 58, and 60C are 0.89 0.06 and 0.80 0 .10 min, 0.36 0.09 and 0.36 0.07, and 0.20 0.08 and 0.17 0.017 min, respectively. D value for acid adapted S. Muenchen at 55C temperatures is lower than D values of S Typhimurium ( P < 0.05), and equal to those D values of S Gaminara. However, S Mu enchen had the highest heat resistance for both acid adapted and non adapted cells at 58 and 60C. D values for acid adapted and non adapted S. Muenchen at 55, 58, and 60C are 0.89 0.09 and 0.80 0.12 min, 0.36 0.09 and 0.36 0.07 min, and 0.20 0.08 and 0.17 0.09 min. z values for acid adapted and non adapted S Typhimurium, S Gaminara, and S Muenchen are 6.3 and 6.5, 7.5 and 7.1, and 6.5 and 6.7, respectively (Table 4 6).
50 With the exception of D values for acid adapted and non adapted S Mu enchen at 60C ( P < 0.05), there was not a significant difference between D values for acid adapted and non adapted Salmonella serotypes at all temperatures ( P > 0.05). D value differences among serotypes for both acid adapted and non adapted groups show ed different trends according to temperature. D values for both acid and non adapted S Typhimurium at 55 and 58C we re significantly different from D values for both acid and non adapted S Gaminara and S Muenchen in the same adaptation group ( P > 0.05). H owever, D values for both acid and non adapted S Muenchen at 60C are significantly different from D values for both acid and non adapted S Typhimurium and S Muenchen in the same adaptation group ( P < 0.05). D values of L. monocytogenes S trains In Table 4 7, D and z value of L. monocytogenes strains grown in BHI and BHIG and heated at 56, 58, and 60C are shown. Non adapted L. monocytogenes strains always had higher D values than acid adapted strains except for strain LCDC 81 861 at 55C. D values for ac id adapted and non adapted L. monocytogenes LCDC 81 861 at 56, 58, and 60C we re 1.34 0.08 and 1.59 0.07 min, 0.92 0.09 and 0.91 0.08 min, and 0.48 0.14 and 0.55 0.06 min, respectively. A similar trend was seen for other L. monocytogenes stra in Scott A, where D values for acid adapted and non ada pted cells at 56, 58, and 60C wer e 1.73 0.06 and 1.82 0.05 min, 0.99 0.06 and 1.05 0.07 min, and 0.62 0.07 and 0.65 0.06 min, respectively. Also, D values for acid adapted and non adapted L. monocytogenes v7 resembled to other two L. mon ocytogenes strains at 56C, b ut lower at 58, and 60C. They we re 1.47 0.10 and 1.65 0.07 min, 0.85 0.10 and 0.90 0.09 min, and 0.49 0.08 and 0.52 0.05 min, respectively. z values for acid adapte d and non adapted L. monocytogens strai n LCDC 81 861, Scott A, and v7 we re, 9.0 and 8.7, 9.0 and 8.9, and 8.4 and 8.0, respectively.
51 With the exception of D values for acid adapted and non adapted strain LCDC 81 861 at 56C ( P < 0.05), there wa s not a sign ificant difference between D values for acid adapted and non adapted L. mo nocytogenes strains at all temperatures ( P > 0.05). D values for non adapted L. monocytogenes strains we re significantly different from each other in the same adaptation group at 56 C; however, only strain D values of non adapted strain Scott A had significantly different D values at 58 and 60C in the non adapted strain groups ( P < 0.05). For acid adapted strain group, different trend was seen. D values for acid adapted L. monocytoge nes strain s we re significantly different from each other in the same adaptation group at 56 and 60C ( P < 0.05), whereas they we re not significant at 58C ( P > 0.05). Heat Resistance of All P athogens D values of STEC strains, Salmonella serotypes, and L. m onocytogenes strains were calculated by using linear regression equations as presented in Tables 4 1, 4 2, and 4 3, respectively. A total of three STEC strains, three Salmonella serotypes, and three L. monocytogenes strains were tested to determine the hea t resistance of the pathogens in single strength orange juice (pH 3.87 0.01; Brix corrected 12.24). Salmonella serotypes we re less heat resistance than STEC and L. monocytogenes strains. All acid adapted and non adapted STEC strains had the highest D va lues at 56 and 58C; one of acid adapted and non adapted L. monocytogenes strain Scott A wa s the most heat tolerant strain at 60C. Acid adaptation caused change of heat resistance for all STEC strains at all temperatures except for strain F4546 at 55 and 58C ( P < 0.0 5). However, it had almost no significant effect on Salmonella serotypes and L. monocytogenes strains except for S Muenchen at 60C and L. monocytogenes strain LCDC 81 861 at 56C ( P > 0.05). An analysis of variance showed that average z valu es of three L. monocytogenes strain are significantly different from average z values of three STEC strains and three Salmonella serotypes ( P < 0.05) (Table 4 8).
52 A semilogaritmic plot of D value versus temperature for STEC strains, Salmonella serotypes an d L. monocytogenes strains was used to evaluate the heat resistance of these pathogens on the same plot (Figure 4 13). The highest D value for acid adapted and non adapted strains and serotypes were plotted to calculate minimum process time at 71.1C. A li near regression line for each acid adapted and non adapted pathogens were obtained. These linear regression lines were extrapolated to higher temperatures to cover higher processing temperatures including milk pasteurization temperature (71.1C). Extrapola ted regression lines from the highest D value for STEC and L. monocytogenes strains crossed around 59C. To determine process parameters that would provide d the thermal inactivation of all three pathogens, a regression line covering all regression lines fo r all three pathogens was drawn above all regression lines for strains and serotypes with the highest D values. The equation of this cover line (log D = 8.2 0.14 T (C)) was used to calculate a general process for orange juice at 71.1C. The calculations via this equation yielded that achievement of 5 log reductions of all three pathogens in orange juice requires 5.29 seconds at 71.1C with a z value of 7.1C
53 Table 4 1. Linear regression equations and R 2 of STEC strains used in D value calculations at 56 58, 60C. Non adapted pathogens were grown in TSB and acid adapted pathogens were grown in TSB supplemented with 1% glucose (TSBG) (n = 6) Linear regression equations of strains and R 2 at temperatures E. coli 56C 58C 60C Strain Adaptation Equat ions R 2 Equations R 2 Equations R 2 O157:H7 (F4546) Non y = 0.3681x + 9.3693 0.95 y = 0.7841x + 9.6966 0.95 y = 1.9080x + 9.8733 0.97 Acid y = 0.3554x + 9.6283 0.97 y = 0.7392x + 9.7800 0.97 y = 1.6454x + 9.7084 0.95 O157:H7 (MDD338) Non y = 0. 5187x + 8.9160 0.95 y = 0.9576x + 8.5780 0.88 y = 1.9387x + 8.5726 0.85 Acid y = 0.3117x + 9.1629 0.94 y = 0.7612x + 9.1664 0.92 y = 1.6411x + 9.0879 0.93 O111 (MDD339) Non y = 0.3272x + 9.2080 0.97 y = 0.7216x + 9.3120 0.93 y = 1.8423x + 9.6380 0.97 Acid y = 0.2930x + 9.2772 0.97 y = 0.6269x + 9.3018 0.92 y = 1.6338x + 9.5688 0.97
54 T able 4 2. Linear regression equations and R 2 of Salmonella serotypes used in D value calculations at 55, 58, 60C. Non adapted pathogens were grown in TSB and acid adapted pathogens were grown in TSB supplemented with 1% glucose (TSBG) (n = 6) Linear regression equations of strains and R 2 at temperatures Salmonella 55C 58C 60C Serotype Adaptation Equations R 2 Equations R 2 Equations R 2 Typhimurium (A TCC 14028) Non y = 1.0203x + 8.3253 0.91 y = 3.3286x + 8.6029 0.91 y = 5.8235x + 8.8061 0.93 Acid y = 0.9721x + 8.4866 0.94 y = 3.5334x + 9.0032 0.93 y = 5.8561x + 9.0074 0.93 Gaminara (CDC H0622) Non y = 1.2465x + 8.9191 0.90 y = 3.0724 x + 8.8656 0.92 y = 6.9899x + 9.2921 0.94 Acid y = 1.1263x + 9.0258 0.96 y = 3.1410x + 9.3009 0.97 y = 6.7254x + 9.4836 0.94 Muenchen (MDD30) Non y = 1.2567x + 8.7988 0.87 y = 2.7575x + 8.6942 0.94 y = 5.8555x + 8.8314 0.92 Acid y = 1. 1233x + 9.0139 0.92 y = 2.8116x + 8.8308 0.93 y = 5.0934x + 8.9485 0.93
55 Table 4 3. Linear regression equations and R 2 of L. monocytogenes strains used in D value calculations at 56, 58, 60C. Non adapted pathogens were grown in BHI and acid adapted pathogens were grown in BHI supplemented with 1% glucose (BHIG) (n = 6) Linear regression equations of strains and R 2 at temperatures L. monocytogenes 56C 58C 60C Strain Adaptation Equations R 2 Equations R 2 Equations R 2 LCDC 81 861 Non y = 0.62 76x + 9.3324 0.96 y = 1.0985x + 9.4175 0.94 y = 1.8173x + 9.2424 0.96 Acid y = 0.7472x + 9.2413 0.93 y = 1.0922x + 9.0574 0.93 y = 2.0636x + 8.8346 0.82 Scott A Non y = 0.5500x + 9.5018 0.98 y = 0.9529x + 9.4533 0.96 y = 1.5380x + 9.450 3 0.96 Acid y = 0.5790x + 9.3232 0.96 y = 1.0112x + 9.3661 0.96 y = 1.6027x + 9.3616 0.96 v7 Non y = 0.6061x + 9.4928 0.96 y = 1.1067x + 9.5121 0.92 y = 1.9095x + 9.4006 0.97 Acid y = 0.6794x + 9.4639 0.92 y = 1.1735x + 9.5026 0.92 y = 2.0601x + 9.5129 0.94
56 Table 4 4. Linear regression equations and R 2 of strains used in z value calculations. Non adapted STEC strains and Salmonella serotypes were grown in TSB and acid adapted STEC strains and Salmonella were grown in TSB supplement ed with 1% glucose (TSBG). Non adapted L. monocytogenes strains were grown in BHI and acid adapted L. monocytogenes strains were grown in BHI supplemented with 1% glucose (BHIG). Linear regression equations of strains to estimate z value R 2 Strain or s erotype Adaptation E. coli O157:H7 (F4546) Non y = 0.1796x + 10.505 0.99 Acid y = 0.1658x + 9.7404 0.99 E. coli O157:H7 (MDD338) Non y = 0.1424x + 8.2647 0.99 Acid y = 0.1803x + 10.593 0.99 E. coli O111 (MDD339) Non y = 0.1880x + 11.023 0.99 Acid y = 0.1869x + 11.012 0.99 S. Typhimurium Non y = 0.1537x + 8.4280 0.99 (ATCC 14028) Acid y = 0.1590x + 8.7331 0.98 S. Gaminara Non y = 0.1400x + 7.6411 0.99 (CDC H0622) Acid y = 0.1332x + 7.2671 0.99 S. Muenchen Non y = 0.14 86x + 8.1025 0.97 (MDD30) Acid y = 0.1528x + 8.3717 0.99 L. monocytogenes Non y = 0.1153x + 6.6519 0.99 LCDC 81 861 Acid y = 0.1115x + 6.3891 0.98 L. monocytogenes Non y = 0.1118x + 6.5138 0.99 Scott A Acid y = 0.1114x + 6.4707 0.99 L. m onocytogenes Non y = 0.1254x + 7.2340 0.99 V7 Acid y = 0.1193x + 6.8472 1.00
57 Table 4 5 D and z values of STEC strains from linear regression equations. Non adapted pathogens were grown in TSB and acid adapted pathogens were grown in TSB supplemented with 1% glucose (TSBG). Capital letters in rows indic ate significant difference in D values between acid adapted and non adapted cells within each temperature and strain. Lower case letters, within columns, indicate significant difference in D values ( P < 0.05) STEC Strain D value SD (min) at temperatures* 56C 58C 60C z value (C) Adaptation Non Acid Non Acid Non Acid Non Acid O157:H7 (F4546) 2.720.07Aa 2.810.06 Aa 1.280.07Aa 1.350.05Aa 0.520.05Aa 0.610.06Ba 5.60.10 6.00.05 O157:H7 (MDD338) 1.930.07Ab 3.210.08Bb 1.040.12Ab 1.310.04Bb 0.520.13Aa 0.610.08Ba 7.00.07 5.50.09 O111 (MDD339) 3.050.06Ac 3.410.06Bc 1.390.04Ac 1.600.05Bc 0.540.05Aa 0.61 0.05Ba 5.30.11 5.40.14
58 Table 4 6 D and z values of Salmonella seroty pes from linear regression equations. Non adapted pathogens were grown in TSB and acid adapted pathogens were grown in TSB supplemented with 1% glucose (TSBG). Capital letters in rows indicate significant difference in D values between acid adapted and non adapted cells within each tem perature and serotype. Lower case letters, within columns, indicate significant difference in D values ( P < 0.05) Salmonella Serotype D value SD (min) at temperatures* 55C 58C 60C z value (C) Adaptation Non Acid Non Acid Non Acid Non Acid Typhimirium (ATCC 14028) 0.980.09A a 1.030.08Aa 0.300.10Aa 0.280.10Aa 0.170.09Aa 0.170.09Aa 6.50.17 6.30.28 Gaminara (CDC H0622) 0.800.10Ab 0.890.06Ab 0.330.09Ab 0.320.05Ab 0.140.08Ab 0.150.08Ab 7.10.10 7.50.10 Muenchen (MDD30) 0.800.12Ab 0.890.09Ab 0.360.07Ab 0.360.09A b 0.170.09Aa 0.200.08Ba 6.70.33 6.50.21
59 Table 4 7 D and z values of L. monocytogenes strains from linear regression equations. Non adapted pathogens were grown in BHI and aci d adapted pathogens were grown in BHI supplemented with 1% glucose (BHIG). D value SD (min) at temperatures* Listeria Strain 56C 58C 60C z value (C) Adaptation Non Acid Non Acid Non Acid Non Acid LCDC 81 861 1.59 0.07A a 1.34 0.08B a 0.91 0 .08 A a 0.92 0.09 A a 0.55 0.06 A a 0.48 0.14 A a 8.7 0.06 9.0 0.31 Scott A 1.82 0.05 A b 1.73 0.06 A b 1.05 0.07 A b 0.99 0.06 A a 0.65 0.06 A b 0.62 0.07 A b 8.9 0.10 9.00.1 0 V7 1.65 0.07 A c 1.47 0.10 A c 0.90 0.09 A a 0.85 0.10 A a 0.52 0.05 A a 0.49 0.08 A c 8.0 0.06 8.4 0. 01 Capital letters in rows indicate significant difference in D values between acid adapted and non adapted cells within each temperature and strain. Lower case letters, within columns, indicate significant difference in D values ( P < 0.05)
60 Table 4 8. Average z values of STEC and L. monocytogenes strains and Salmonella serotypes. Non adapted STEC strains and Salmonella serotypes were grown in TSB and acid adapted STEC strains and Salmonella were grown in TSB supplemented with 1% glucose ( TSBG). Non adapted L. monocytogenes strains were grown in BHI and acid adapted L. monocytogenes strains were grown in BHI supplemented with 1% glucose (BHIG) Species Adaptation Average z values (C) STEC Non 6.0a Acid 5.6a Salmonella Non 6.8a Acid 6. 8a L. monocytogenes Non 8.5b Acid 8.8b aWithin the same type of adaptation, z values with different letter in the same column are significantly different ( P < 0.05)
61 Figure 4 1. Linear regression lines of STEC strain at 56C to estimate D val ue. A) E. coli O157:H7 from human feces sprout outbreak (F4546), B) E. coli O157:H7 from apple juice outbreak Massachusetts (MDD338), C) E. coli from apple cider outbreak New York O111 (MDD339). ( adapted (n=6).
62 Figure 4 2. Linear regression lines of STEC strains at 58C to estimate D value. A) E. coli O157:H7 from human feces sprout outbreak (F4546), B) E. coli O157:H7 from apple juice outbreak Massachusetts (MDD3 38), C) E. coli from apple cider outbreak New York O111 (MDD339). ( adapted (n=6).
63 Figure 4 3. Linear regression lines of STEC strains at 60C to estimate D value. A) E. coli O157:H7 from human feces sprout outbreak (F4546) B) E. coli O157:H7 from apple juice outbreak Massachusetts (MDD338), C) E. coli from apple cider outbreak New York O111 (MDD339). ( adapted (n=6).
64 Figure 4 4. Linear regression lines of Salmonella serotypes at 55C to est imate D value. A) S. Typhimurium from orange juice outbreak (ATCC 14028), B) S. Gaminara from orange juice outbreak (CDC H0662), C) S. Muenchen from orange juice outbreak (MDD30). ( adapted (n=6).
65 Figure 4 5. Linear regress ion lines of Salmonella serotypes at 58C to estimate D value. A) S. Typhimurium from orange juice outbreak (ATCC 14028), B) S. Gaminara from orange juice outbreak (CDC H0662), C) S. Muenchen from orange juice outbreak (MDD30). ( ad apted (n=6).
66 Figure 4 6. Linear regression lines of Salmonella serotypes at 60C to estimate D value. A) S. Typhimurium from orange juice outbreak (ATCC 14028), B) S. Gaminara from orange juice outbreak (CDC H0662), C) S. Muenchen from orange juic e outbreak (MDD30). ( adapted (n=6).
67 Figure 4 7. Linear regression lines of L. monocytogenes strains at 56C to estimate D value. A) L. monocytogenes from raw cabbage outbreak ( LCDC 81 861 ), B) L. monocytogenes from human milk outbreak ( Scott A ), C) L. monocytogenes from milk associated outbreak (v7). ( adapted (n=6).
68 Figure 4 8. Linear regression lines of L. monocytogenes strains at 58C to estimate D value. A) L. monocytogenes from raw cabbage outbreak ( LCDC 81 861 ), B) L. monocytogenes from human milk outbreak ( Scott A ), C) L. monocytogenes from milk associated outbreak (v7). ( adapted (n=6).
69 Figure 4 9. Linear regression lines of L. monocytogenes strains at 60C to estimate D v alue. A) L. monocytogenes from raw cabbage outbreak ( LCDC 81 861 ), B) L. monocytogenes from human milk outbreak ( Scott A ), C) L. monocytogenes from milk associated outbreak (v7). ( adapted (n=6).
70 Figure 4 10. Linear regress ion lines of STEC strains to estimate z value. A) E. coli O157:H7 from human feces sprout outbreak (F4546), B) E. coli O157:H7 from apple juice outbreak Massachusetts (MDD338), C) E. coli from apple cider outbreak New York O111 (MDD339). ( ) Acid adapted, adapted.
71 Figure 4 11. Linear regression lines of Salmonella serotypes to estimate z value. A) S. Typhimurium from orange juice outbreak (ATCC 14028), B) S. Gaminara from orange juice outbreak (CDC H0662), C) S. Muenchen from orange juice o utbreak (MDD30). ( adapted.
72 Figure 4 12. Linear regression lines of L. monocytogenes strains estimate z value. A) L. monocytogenes from raw cabbage outbreak (LCDC 81 861), B) L. monocytogenes from human milk outbreak (Scott A), C) L. monocytogenes from milk associated outbreak (v7). ( adapted.
73 Figure 4 13. Semilogaritmic plot of the highest D values by organism versus temperature of non ( ) STEC str ains;non ) L. monocytogenes strains; and non adapted ( ) Salmonella serotypes. The highest D values obtained for each pathogen were plotted to a linear curve to calculate a minimum process at 71.1C. A thick solid line was drawn above all the pathogen regression lines to calculate an overall process for all strains. The equation of thick line y = 164,697,794.0085e 0.3122x (R = 1.00).
74 CHAPTER 5 DISCUSSION The microbial safety of fruit ju ices can be ensured by pasteurization treatment with proper time and temperature parameters to destroy sufficient levels of pathogens. Current juice microorganism of public health significance that is likely to occur in the juice (FDA, 2001). Fruit juice processors target 5 log reduction of pertinent microorganisms during pasteurization, as described in the Juice HACCP rule (FDA, 2001). In identification of the pertinent mic roorganism, outbreaks of pathogen associated with different types of juice have been considered. FDA (2001) states that Salmonella species are implicated with outbreaks related to ce, while both Escherichia coli O157:H7 and Cryptosporidium apple juice. Recently, when orange juice moves between facilities, and is required under juice HACCP to be repasteurized, Listeria monocytogenes has be en suggested as a reasonable pertinent microorganism due to its ubiquitous nature. Both E. coli and L. monocytogenes survive for extended periods of time in orange juice to be able to cause outbreaks (Piotrowski, 2003; Parish and Higgins, 1989; Oyarzabal e t al., 2006). Although not present natural flora of fresh juice, it is believed that E. coli can be introduced to juice as a consequence of fecal contamination from reservoirs including cattle, wild birds, deer rodents, goat, sheep, cats, dogs. (Nielsen et al., 2004). The ubiquitous nature of L. monocytogenes makes it a potential risk for all juice products. To set conservative pasteurization parameters, thermal death of Salmonella E. coli and L. monocytogenes should be considered. Acid adaptation is gener ally believed to enhance the thermal tolerance of pathogens in fruit juices, and has been demonstrated for E. coli O157:H7, Salmonella and L. monocytogenes in
75 apple, orange and white grape juices (Mazotta, 2001), and E. coli O157:H7 at 52C in orange juic e and apple cider (Ryu and Beuchat, 1998). Similar to previous studies on strain cocktails (Mazotta, 2001; Ryu and Be uchat, 1998 ), acid adaptation significantly increased the heat resistance of STEC strains MDD338 and MDD339 at all temperatures tested. Ho wever, the thermal resistance of STEC strain F4546 was not significantly increased by acid adaptation at 56 and 58C, indicating that there is strain to strain variability in the protective effects of acid adaptation and that acid adaptation may not increa se thermal tolerance of all STEC strains. In this study, only small, insignificant ( P > 0.05), increases in thermal tolerance of Salmonella resulted from acid adaptation, unlike those previously reported in the literature with the exception of S. Typhimuri um at 55C (Mazotta, 2001). In a recent study where the authors used individual Salmonella serotypes, non adapted S Newport and S Saintpaul had higher thermal tolerance than acid adapted cells at 56C in mango and pineapple juice (Yang et al., 2012). Me thodology differences, serotype variability and use of cocktails are likely reasons for different thermal tolerance results among the studies. L. monocytogenes strains had very different h eat resistance trend following acid adaptation when compared to STEC strains and Salmonella serotypes. Acid adaptation decreased, but not statistically significantly ( P > 0.05), the heat resistance of L. monocytogenes strains in this study at all temperatures tested, while Mazotta (2001) reports that acid adaptation led to higher L. monocytogenes heat resistance in orange juice. Only, acid adapted L. monocytogenes strain LCDC 81 861 has higher D values than non adapted cells at 58C. Similar to this study, Sharma et al. (2005) reported that acid adapted E. coli strains and Salmonella serotypes are more heat resistant, but did not see the same for L. monocytogenes in watermelon and cantaloupe juice. The differences between the acid adaptation responses of L. monocytogenes among the studies may be explained by the use of diffe rent
76 strains and procedure differences for adaptation. Overall, the most resistant form of bacteria should be considered as reference in determination of pasteurization parameters. The D values reported here are lower than those reported in Mazotta (2001) at all temperatures, for all species, with the exception of L. monocytogenes at 60C. Salmonella were the most heat sensitive pathogens at all temperatures tested similar to reported in previous studies (Mak et al., 2001, Mazotta, 2001, Sharma et al., 2005 ). Here, E. coli O111 was the most heat resistant pathogen at 56 and 58C, while L. monocytogenes str ain from human milk outbreak had the highest D value at 60C. Mazotta (2001) also finds that L. monocytogenes is more heat resistant than E. coli at higher temperatures, particularly after 65 C. Ena che et al. (2011) report D values varied among different E. coli serogroups O26, O45, O103, O111, O121, O145, and O157 in apple juice, and that E. coli O157:H7 wa s the most heat resistant. The D values reported he re for E. coli O157:H7 we re also lo wer than those reported by Enac he et al., (2011), however D values for strain O111 we re similar for both non adapted and acid adapted cells. Unlike apple juice, our results indicate that E. coli O111 wa s more heat resista nt than E. coli O157:H7 in orange juice at all temperatures tested. We also observed that D values, for all s trains in a single species, varied significantly at lower temperatures, while in creasing the temperature reduced the D value variability among the strains. Amongst the three strains/sero type s of STEC Salmonella and L. monocytogenes we have tested in orange juice STEC was the most heat resistant pathogen. However, non adapted L. monocytogenes had a higher D value at 60C, and the highest z value amo ng all pathogens evaluated, indicating that L. monocytogenes have higher thermal tolerance as temperature increases. This result confirms work by Mazotta (2001) where L. monocytogenes has higher thermal tolerance than E. coli O157:H7 at typical juice proce ssing temperatures. Lower D value
77 differences for L. monocytogenes strains at tested temperatures resulted in high z values compare to z Also, higher z values determined in this study indicated that larger changes in temperature were required to reduce the treatment time. As L. monocytogenes can survive at 4C up to 61 days (Piotrowski, 2003), is ubiquitous, and more heat resistance than E. coli O157:H7 at higher processing temperatures (Mazotta, 2001), processors should consider L. monocytogenes in orange juice pasteurization. Semi logarit h mic plot of the highest D values versus temperature for each pathogen was plotted as described by Mazotta (2001) to determine orange juice pasteurization parameters. The highest D values obtained for both non adapted and acid adapted cells of each pathogen were plotted to obtain a linear curve to calculate a minimum process at 71.1C. To determine process parameters that would provide the thermal inactivation of all three pathogen s, a regression line covering all regression lines for all three pathogens was drawn above all regression lines for strains and serotypes with the highest D values (Figure 4 13 ). The equation of this line (log D = 8.2 0.14 T (C)) was used to calculate a general process for orange juice at 71.1C, indicating that achievement of 5 log reductions of all three pathogens in orange juice requires 5.29 seconds at 71.1C with, a z value of 7.1C. Mazotta (2001) indicate s that process of 3 s at 71.1C is sufficie nt for fruit juice pasteurization, 2.3 seconds less than our calculated value. In New York, the recommended pasteurization parameters of apple cider are already 6 s at 71.1C for most varieties of apples (NYSDM, 1998; Mak et al., 2001). The use of cocktai l of strains in thermal destruction experiments might be an explanation for the differences between Mazotta (2001) and results reported here; as i s our use of the O111 serogroup of E. coli A validation study of apple cider pasteurization where the author s observed that higher log reductions were observed in two E. coli O157:H7 strains compare to the other three strains they used in their cocktail demonstrate
78 the masking effect strain cocktails may have while determining pasteurization parameters (Mak et a l., 2001). In this case the authors note that use of the less heat tolerant strains would not be appropriate for successful pasteurization of apple cider. Differences in pasteurization parameters defined here and those produced in previous studies may be d ue to a number of reasons. Mazotta (2001) only used E. coli O157:H7 cocktail, and in this case, acid adapted E. coli O157:H7 was the most heat resistant pathogen in calculation of the pasteurization parameter. Here, acid adapted E. coli O111 was the most h eat resistant pathogen, increasing the time to achieve a 5 log reduction at 71.1C by 2.3 seconds. As only individual strains were used to determine D values of pathogens in our studies, the possible masking affect among the strains and serotypes was elimi nated. Finally, significant methodology differences might have affected the results, which may explain the lower D values we report. Although our individual D values we re lower than those reported in literature, the calculated juice pasteurization time at the same temperature is higher. This indicates that proportions of the D values at different temperatures within experiments may play more of a role than calculated D value numbers in determination of pasteurization parameters. No outbreaks associated wit h pasteurized orange juice have occurred since FDA required 5 log reduct as a mandatory step in fruit juice process. Current fruit juice pasteurization parameters recommended by FDA are likely sufficient to produce safe frui t juices, due to the absence of outbreaks associated with juices being produced under functioning HACCP (Vojdani et al., 2008). However, we should consider other factors that directly affect the success of fruit juice pasteurization. Salmonella can survive long enough on orange peel surface to be a contamination source; Salmonella population on the orange peel surface declined between 1.5 and 3.0 log CFU/orange after 30 days in any set of post harvest
79 treatment conditions fruit tested (Da nyluk et al. 2010). Pao and Davis (2001) indicate that microbial levels detected in fresh juice are 90 99% lower than microorganism presence on fruit surface indicating that most contamination on fruit can be eliminated by commercial citrus juice extraction. It is unlikely p ossible that fruit juices will be contaminated with more than 5 logs of foodborne pathogen under good manufacturing practices (GMPs) and standard sanitation operating procedures (SSOPs). However, we cannot ignore the possibility of worst case scenario. Val ero et al. (2010) states that high initial microbial population level (10 5 to 10 6 CFU/m L ) in citrus juice may occur as a consequence of insufficient sanitation, poor hygiene practices, deteriorated fruits, and poor equipment sanitation to increase the pote ntial risks of level of foodborne pathogen in juice.
80 CHAPTER 6 CONCLUSIONS AND FUTU RE WORKS FDA (2001) recommends the heat treatment of fruit juices for 3 s at 71.1C to achieve 5 hese parameters are sufficient to achieve 5 log reductions of pertinent bacterial pathogens in different types of fruit juices. In this study, we determined that these parameters may not be adequate to kill 99.999% of the viable pertinent bacterial pathoge ns in orange juices. Our findings indicate the achievement of successful 5 log pasteurization under the requirement of FDA can be completed with heat treatment for 5.29 s at 71.1C. This implies that if the population of viable pathogens in orange juice s om ehow reaches over 100,000 CFU/mL an outbreak associated with orange juice may result under curr ent pasteurization parameters. In practice, orange juice processors pasteurize their products over recommended pasteurization parameters to avoid any violatio n of critical limits and to inactivate pectin methyl esterase; this study is important in validation of pasteurization parameters for orange juice processors. D values reported in this study are lower than those previously reported in literature at all tem peratures (Mazotta, 2001; Sharma et al., 2005; Enache et al., 2011). This may be a result of methodology differences and the effect of serotype variability in cocktails used by previous researchers. Although D values reported in here are lower than those p reviously reported, the calculated overall process parameters to achieve a 5 log reduction are higher. We believe that proportions between the D values at temperatures tested are more important than calculated D values when the purpose of the study is to estimate pasteurization parameters to achieve 5 log reductions. The use of individual strains and serotypes provides us a better understanding of thermal inactivation response differences amongst strains and serotypes. Different strains and serotypes
81 respo nded to heat treatment similarly, but development of acid adaptation varied strain to strain even within the same species. We suggest that more strain s of E. coli and L. monocytogenes strains as well as Salmonella serotypes should be studied in orange and other juices types.
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94 BIOGRAPHICAL SKETCH Zeynal Topalcengiz is a master graduate research assistant seeking for master degree at Citrus Research and Education Center, off campus experiment station in University of Food Science and Human Nutrition department. His main project consists of the estimation of thermal death parameters ( D value and z value) of acid adapted and non adapted stationary phase Shiga toxin producing Escherichia coli (STEC), Salmonella spp., and Listeria monocytogenes in fruit juices. Zeynal was raised in Mersin, Turkey. After he graduated with a bachelor degree in food engineering from the Department of Food Engineering of Inonu University in Turkey, he worked in two main sectors of fo od industry; dairy, catering. He has been honored with several scholarships, including his current scholarship for master degree by the Republic of Turkey the Ministry of National Education. In the near future, Zeynal will pursue his PhD. in food microbiol ogy, particularl y, in the field of food safety