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Visual Quantification of Non-Homogeneous Colors in Foods

Permanent Link: http://ufdc.ufl.edu/UFE0021786/00001

Material Information

Title: Visual Quantification of Non-Homogeneous Colors in Foods
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Aparicio, Jose A
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: analysis, color, machine, measurement, sensory, vision
Food Science and Human Nutrition -- Dissertations, Academic -- UF
Genre: Food Science and Human Nutrition thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Color is an important quality attribute for nearly every agricultural product. Consumers may perceive color as an indicator of freshness and wholesomeness, and color may affect their final decision to accept/reject food. A better understanding of human perception of colors in food would be beneficial to increase the consistency and quality of food products. The quantification of color is becoming more important due to an emphasis on international trade and implementation of Hazard Analysis Critical Control Points (HACCP) requiring record keeping. Thus, it is important to provide the agricultural industry with methods to quantify and correlate sensory and instrumental evaluations of foods. Machine vision imitates human visual perception by using a camera and a computer with software capable to generate precise, consistent, and cost-effective color measurement. The comparison and correlation of instrumental and visual color analysis has been performed in many uniformly colored agricultural products such as meat, bakery and seafood. Generally, there is a close relationship between sensory and instrumental color analysis of homogenous foods. However, comparison and correlation of non-homogeneous color measurements in foods is more challenging and has not been thoroughly studied. Machine vision was used to quantify the degree of color uniformity of mangos and nectarines using the number of color blocks and color primitives. The use of color primitives provided a more accurate method to measure color uniformity of mangos and nectarines. Three reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen images captured by machine vision and fruits placed in trays. Panelists attempted to quantify color by selecting (2, 4 or 6 colors) from the reference color bars and compare the colors in the reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days using different panelists. Sensory and machine vision evaluations were compared using the absolute delta E value. delta E measures total color change by accounting for combined changes in L*a*b values. The concept of the best possible delta E or best performance under given circumstances was also evaluated. It was apparent that the number of reference colors and color selections had an impact on the error made by panelists. More color selections reduced the delta E values of the visual evaluations. Statistical analysis described significant differences between the number of reference colors, the number of selections, presentation, and the interaction between the reference colors and the selections. The 8 and 16 reference colors bar provided less error compared to the 12 reference colors bar, quantified by both delta E for both mangos and nectarines. The 12 reference colors bar gave the most error. Two color selections provided the highest mean values. The screen images in general had lower mean values than the fruit trays. This study provided a better understanding of the way panelists perceive non-uniform colors. It also suggested a new formulation of consumer panel studies involving non-uniform visual attributes of foods.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jose A Aparicio.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Balaban, Murat O.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021786:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021786/00001

Material Information

Title: Visual Quantification of Non-Homogeneous Colors in Foods
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Aparicio, Jose A
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: analysis, color, machine, measurement, sensory, vision
Food Science and Human Nutrition -- Dissertations, Academic -- UF
Genre: Food Science and Human Nutrition thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Color is an important quality attribute for nearly every agricultural product. Consumers may perceive color as an indicator of freshness and wholesomeness, and color may affect their final decision to accept/reject food. A better understanding of human perception of colors in food would be beneficial to increase the consistency and quality of food products. The quantification of color is becoming more important due to an emphasis on international trade and implementation of Hazard Analysis Critical Control Points (HACCP) requiring record keeping. Thus, it is important to provide the agricultural industry with methods to quantify and correlate sensory and instrumental evaluations of foods. Machine vision imitates human visual perception by using a camera and a computer with software capable to generate precise, consistent, and cost-effective color measurement. The comparison and correlation of instrumental and visual color analysis has been performed in many uniformly colored agricultural products such as meat, bakery and seafood. Generally, there is a close relationship between sensory and instrumental color analysis of homogenous foods. However, comparison and correlation of non-homogeneous color measurements in foods is more challenging and has not been thoroughly studied. Machine vision was used to quantify the degree of color uniformity of mangos and nectarines using the number of color blocks and color primitives. The use of color primitives provided a more accurate method to measure color uniformity of mangos and nectarines. Three reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen images captured by machine vision and fruits placed in trays. Panelists attempted to quantify color by selecting (2, 4 or 6 colors) from the reference color bars and compare the colors in the reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days using different panelists. Sensory and machine vision evaluations were compared using the absolute delta E value. delta E measures total color change by accounting for combined changes in L*a*b values. The concept of the best possible delta E or best performance under given circumstances was also evaluated. It was apparent that the number of reference colors and color selections had an impact on the error made by panelists. More color selections reduced the delta E values of the visual evaluations. Statistical analysis described significant differences between the number of reference colors, the number of selections, presentation, and the interaction between the reference colors and the selections. The 8 and 16 reference colors bar provided less error compared to the 12 reference colors bar, quantified by both delta E for both mangos and nectarines. The 12 reference colors bar gave the most error. Two color selections provided the highest mean values. The screen images in general had lower mean values than the fruit trays. This study provided a better understanding of the way panelists perceive non-uniform colors. It also suggested a new formulation of consumer panel studies involving non-uniform visual attributes of foods.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jose A Aparicio.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Balaban, Murat O.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021786:00001


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VISUAL QUANTIFICATION OF NON-HOMOGENEOUS COLORS IN FOODS


By

JOSE ALEJANDRO APARICIO













A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2007

































2007 Jose Alejandro Aparicio
































To Caroline Elizabeth Fisher for your never ending support and encouragement throughout this
journey, and who made this milestone possible









ACKNOWLEDGMENTS

I am very grateful to my major advisor, Dr. Murat O. Balaban, for his guidance and

support. My appreciation also to the members of my supervisory committee, Dr. Charles Sims

and Dr. Allen Wysocki, for their mentoring, all participants in my surveys for their input and

open participation, and my lab mates for their support. I thank my family for their loyal

encouragement, which always gave me strength to complete my study.









TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ..............................................................................................................4

L IS T O F T A B L E S ................................................................................. 7

LIST OF FIGU RE S ................................................................. 9

ABSTRACT ........................................... .. ......... ........... 13

CHAPTER

1 INTRODUCTION ............... ..................................................... ..... 15

2 L ITE R A TU R E R E V IE W ......................................................................... ........................ 17

Color of Foods and A agricultural M aterials............................................................................17
Instrumental Color Measurement in Agricultural Food Products .......................................17
Computer Vision or M machine Vision System ...................................................................... 18
B akery P rodu cts ...................................... ......................................................20
Red M eat and Seafood ................................................... ............... .... ...... 20
V eg etab le s ................... ...................2...................3........
F ru its ............................................................................................ ............2 3
P prepared C onsum er F oods .................................................................. ..... .........................24
F ood C ontainer Inspection ........................................................... .....................................24
G rain s ...... ......... ................ ................................................................ 2 5
O th er A p p licatio n s ......... ............ ................ ........................................................2 5
Visual Texture Analysis .................... ....................................26
Visual Texture Applications in Agriculture ........................................ ....................... 27
Correlation between Image and Visual Color Analysis ............................... ................27
Preliminary Experim ents ......................... ........... .. .. ......... ......... 28
Objectives of the Study ........... ................... .................................. 30

3 M A TER IA L S A N D M ETH O D S ........................................ .............................................31

M angos and Nectarines ............... ................. .......... .......... .............3.. 31
Im ag e A c q u isitio n ............................................................................................................. 3 2
Im a g e A n a ly sis ................................................................................................................. 3 2
Experim mental D design .......................... ...................33
Method of Selection of the Reference Color Bars .......................................34
Sensory Evaluations.......................................35
D eterm nation of Color Uniform ity of Fruit ................................................................... 36
A v erag e C o lo r: .........................................................................................3 6
C o lo r B lo ck s .........................................................................................3 7
C o lo r P rim itiv es ................................................................................ 3 7
Calculation of Best Possible AE ......................... ........... ... .............. .............. 39










Statistical Analysis............. ..... .... ....................... ...........39

4 R E SU L T S & D ISC U SSIO N ......................................................................... ...................4 1

M V C olor R results of F ruits ........... ....................................................... ............................4 1
Non-Uniformity Analysis of Fruits ....... .............................................................42
B est Possible AE ............. .. ............. .................................................... 44
Sensory Panel R results .......... .... ...... ...... .... .... .... .................. .. ..... 48
Statistical A analysis .............................................50
Mangos ....................................... 51
N ectarin es ........... ... ...... ......... .................................... ...........................56
AE vs. CCI ..........................................61

5 C O N C L U SIO N S ................................................................62

APPENDIX

A COLOR ANALYSIS FOR ALL TRAYS ....................................................... 64

B PANELISTS PERFORMANCE FOR MANGOS AND NECTARINES ...............................75

C AE VS CCI FOR ALL COMBINATIONS ............................................ 79

D DELTA E VALUES FOR DIFFERENT CASES ..................................... ...............85

E SOURCE CODES FOR SA S PROGRAM S ........................................................................ 91

L IST O F R E F E R E N C E S ......................................................................................................... 98

B IO G R A PH IC A L SK ETCH ..........................................................................105























6









LIST OF TABLES


Table page

3-1 N ikon D 200 settings .......................... ...... ....................... .... ........ ........ 32

3-2 Factorial-Level com binations ................................................. ............................... 34

4-1 M V color analysis for m angos............................................................................ ...... 41

4-2 M V color analysis for nectarines ....................................................................... 41

4-3 Best possible selections and minimum AE value possible for 8 references and 2
selections for m angos ......... .. .... .......... .................. .... .. ........ .. ........ .... 45

4-4 Best possible selections and minimum AE value possible for 8 references and 2
selections for nectarines .......... .... .... .... ........... ... ..................... .... 45

4-5 Best possible selections and minimum AE value possible for 12 references and 2
selections for m angos ......... .. .... .......... .................. .... .. ........ .. ........ .... 45

4-6 Best possible selections and minimum AE value possible for 12 references and 2
selections for nectarines .......... .... .... .... ........... ... ..................... .... 46

4-7 Best possible selections and minimum AE value possible for 16 references and 2
selections for m angos ......... .. .... ............................ .... .. ........ .. ............ 46

4-8 Best possible selections and minimum AE value possible for 16 references and 2
selections for nectarines .......... .... .... .... ........... ... ..................... .... 46

4-9 Best possible selections and minimum AE value possible for 8 references and 4
selections for m angos ......... .. .... .......... .................. .... .. ........ .. ........ .... 47

4-10 Best possible selections and minimum AE value possible for 8 references and 4
selections for nectarines .......... .... .... .... ........... ... ..................... .... 47

4-11 Best possible selections and minimum AE value possible for 12 references and 4
selections for m angos ......... .. .... .......... .................. .... .. ........ .. ........ .... 47

4-12 Best possible selections and minimum AE value possible for 12 references and 4
selections for nectarines .......... .... .... .... ........... ... ..................... .... 48

4-13 Summary performance for panelists evaluating mangos for booth 1 .............................49

4-14 Summary performance for panelists evaluating nectarines for booth 1 ..........................50

4-15 ANOVA summary absolute AE for mangos................................................................... 51










4-16 ANOVA summary difference AE for mangos............. .............................................51

4-17 ANOVA summary absolute AE for nectarines.............................................................56

4-18 ANOVA summary difference in AE for nectarines .....................................................57

A-1 L*a*b values for reference color bar with 8 color.................................. ..................70

A-2 L*a*b values for reference color bar with 12 colors ....................................................71

A-3 L*a*b values for reference color bar with 16 colors ....................................................71

A -4 M ango color prim itives...................................................................... ..........................73

A -5 N ectarine color prim itives.................................................................... ........................74

B-l Summary performance for panelists evaluating both fruits for booth 1 ...........................75

B-2 Summary performance for panelists evaluating both fruits for booth 2 ............................76

B-3 Summary performance for panelists evaluating both fruits for booth 3 ............................76

B-4 Summary performance for panelists evaluating both fruits for booth 4 ............................76

B-5 Summary performance for panelists evaluating both fruits for booth 5 ............................77

B-6 Summary performance for panelists evaluating both fruits for booth 6 ............................77

B-7 Summary performance for panelists evaluating both fruits for booth 7 ............................77

B-8 Summary performance for panelists evaluating both fruits for booth 8 ............................78

B-9 Summary performance for panelists evaluating both fruits for booth 9 ............................78

B-10 Summary performance for panelists evaluating both fruits for booth 10 ..........................78

E-l Mixed mode summary absolute AE for mangos............................... ... ............91

E-2 Mixed mode summary difference AE for mangos .................................. ..................92

E-3 Mixed Mode summary absolute AE for nectarines..........................................................92

E-4 Mixed Mode summary difference in AE for nectarines................................ ..............92









LIST OF FIGURES


Figure p e

3-1 Example of mango and nectarine on aluminum tray .....................................................31

3-2 Example of reference color bar with 8 colors added to fruit images presented to the
p an elists.......... .............................. ................................................ 3 5

4-1 Correlation between number of primitives and color change index (CCI)......................43

4-2 Correlation between number of neighbors and color change index (CCI)........................43

4-3 Correlation between number of neighbors and number of primitives ..............................44

4-4 Comparison of AE values for 8, 12, and 16 reference colors, 2 selections.....................49

4-4 Absolute AE means difference of selections of colors using mangos ............................52

4-5 Difference in AE means difference of selections of colors using mangos.......................52

4-6 Absolute AE means for reference colors for mangos.............................. ...............53

4-7 Difference in AE means for reference colors for mangos............... .... ............... 53

4-8 Absolute AE means for interaction between the number of reference colors and the
num ber of selections ........................................ .. .. ..... .......... .... 54

4-9 Difference in AE means for interaction between the number of reference colors and
the num ber of selections ................................................................. ........ 55

4-10 Absolute AE means for presentation for mangos.................................. ..................55

4-11 Difference AE means for presentation for mangos....................................56

4-12 Absolute AE means for reference colors for nectarines............................ .............57

4-13 Difference AE means for reference colors for nectarines...............................................58

4-14 Absolute AE means for selection of colors for nectarine........................................58

4-15 Difference AE means for selections of colors for nectarines...................................59

4-16 Difference AE means for selections of colors for nectarines ....................................59

4-17 Difference AE means for selections of colors for nectarines ....................................60

4-18 Absolute AE means for presentation for nectarines ................................................60









4-19 Difference AE means for presentation for nectarines ................................ ...............61

A-i Fruit tray booth 1 for image acquisition and sensory panel..................... ............64

A-2 Fruit tray booth 2 for image acquisition and sensory panel..................... ............64

A-3 Fruit tray booth 3 for image acquisition and sensory panel..................... ............65

A-4 Fruit tray booth 4 for image acquisition and sensory panel............... ....... ............65

A-5 Fruit tray booth for image acquisition and sensory panel..............................................66

A-6 Fruit tray booth 6 for image acquisition and sensory panel..................... ............66

A-7 Fruit tray booth 7 for image acquisition and sensory panel................ ....... ............67

A-8 Fruit tray booth 8 for image acquisition and sensory panel................ ....... ............67

A-9 Fruit tray booth 9 for image acquisition and sensory panel................ ....... ............68

A-10 Fruit tray booth 10 for image acquisition and sensory panel....... ...... ......... ..........68

A M machine V vision set-up ...................... .... ............................................ ........................... 69

A -12 L eight box specifications................................................................................ ............ 69

A-13 Reference scales presented to panelists. ........................................ ........................ 70

A-14 Example ballot for screen image evaluation........................... ...................................72

A-15 Example ballot for fruit evaluation .................................. ............... ............... 73

A-16 Representation of color primitives and equivalent circles for mangos (left) and
nectarines (right) w ith a M V system .......................................... ............................ 74

C-l Absolute A E for nectarine for screen image and 8 references........................................79

C-2 Absolute A E for nectarine for screen image and 12 references.............. ... .............79

C-3 Absolute A E for nectarine for screen image and 16 references.............. .. ................80

C-4 Absolute A E for nectarine for tray and 8 references ......................................................80

C-5 Absolute A E for nectarine for tray and 12 references ..................................................81

C-6 Absolute A E for nectarine for tray and 16 references ..................................................81

C-7 Absolute A E for mango for screen image and 8 references............................................82









C-8 Absolute A E for mango for screen image and 12 references................ .............. ....82

C-9 Absolute A E for mango for screen image and 16 references................ .............. ....83

C-10 Absolute A E for mango for tray 8 references ....................................... ............... 83

C-11 Absolute A E for mango for tray and 12 references............................................. 84

C-12 Absolute A E for mango for tray and 16 references.............................................84

D-1 Absolute A E for nectarine for screen image and 8 references.......................................85

D-2 Absolute A E for nectarine for screen image and 12 references.............. .. ................85

D-3 Absolute A E for nectarine for screen image and 16 references.............. ... .............86

D-4 Absolute A E for nectarine for tray and 8 references .....................................................86

D-5 Absolute A E for nectarine for tray and 12 references ..................................................87

D-6 Absolute A E for nectarine for tray and 16 references.....................................................87

D-7 Absolute A E for mango for screen image and 8 references............................................88

D-8 Absolute A E for mango for screen image and 12 references................ .............. ....88

D-9 Absolute A E for mango for screen image and 16 references................ .............. ....89

D-10 Absolute A E for mango for tray 8 references ....................................... ............... 89

D-11 Absolute A E for mango for tray and 12 references.............................. ...............90

D-12 Absolute A E for mango for tray and 16 references.............................. ...............90

E-1 Absolute AE means for selection of color for mangos ............................................... 93

E-2 Difference AE Means for selection of color for mangos ....................................... 93

E-3 Absolute AE means for reference colors for mangos.............................. ...............94

E-4 Difference AE means for reference colors for mangos .............. ...................................94

E-5 Absolute AE means for presentation for mangos..................................... ....................94

E-6 Difference AE means for presentation for mangos........................ ..................95

E-7 Absolute AE means for selections of colors for nectarines....................................95









E-8 Difference AE means for selections of colors for nectarines...........................................96

E-9 Absolute AE means for reference colors for nectarines................. ............................96

E-11 Absolute AE means for presentation for nectarines .......................................................97

E-12 Difference AE means for presentation for nectarines ................................................97









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

VISUAL QUANTIFICATION OF NON-HOMOGENEOUS COLORS IN FOODS

By

Jose Alejandro Aparicio

December 2007

Chair: Murat Balaban
Major: Food Science and Human Nutrition

Color is an important quality attribute for nearly every agricultural product. Consumers

may perceive color as an indicator of freshness and wholesomeness, and color may affect their

final decision to accept/reject food. A better understanding of human perception of colors in

food would be beneficial to increase the consistency and quality of food products. The

quantification of color is becoming more important due to an emphasis on international trade and

implementation of Hazard Analysis Critical Control Points (HACCP) requiring record keeping.

Thus, it is important to provide the agricultural industry with methods to quantify and correlate

sensory and instrumental evaluations of foods.

Machine vision imitates human visual perception by using a camera and a computer with

software capable to generate precise, consistent, and cost-effective color measurement. The

comparison and correlation of instrumental and visual color analysis has been performed in

many uniformly colored agricultural products such as meat, bakery and seafood. Generally,

there is a close relationship between sensory and instrumental color analysis of homogenous

foods. However, comparison and correlation of non-homogeneous color measurements in foods

is more challenging and has not been thoroughly studied.









Machine vision was used to quantify the degree of color uniformity of mangos and

nectarines using the number of color blocks and color primitives. The use of color primitives

provided a more accurate method to measure color uniformity of mangos and nectarines. Three

reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A

sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen

images captured by machine vision and fruits placed in trays. Panelists attempted to quantify

color by selecting (2, 4 or 6 colors) from the reference color bars and compare the colors in the

reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days

using different panelists.

Sensory and machine vision evaluations were compared using the absolute AE value. AE

measures total color change by accounting for combined changes in L*a*b values. The concept

of the best possible AE or best performance under given circumstances was also evaluated. It

was apparent that the number of reference colors and color selections had an impact on the error

made by panelists. More color selections reduced the AE values of the visual evaluations.

Statistical analysis described significant differences between the number of reference colors, the

number of selections, presentation, and the interaction between the reference colors and the

selections. The 8 and 16 reference colors bar provided less error compared to the 12 reference

colors bar, quantified by both AE for both mangos and nectarines. The 12 reference colors bar

gave the most error. Two color selections provided the highest mean values. The screen images

in general had lower mean values than the fruit trays.

This study provided a better understanding of the way panelists perceive non-uniform

colors. It also suggested a new formulation of consumer panel studies involving non-uniform

visual attributes of foods.









CHAPTER 1
INTRODUCTION

Today's consumers have increased expectations for the quality of food they purchase. In

this competitive market there is no second chance to make a first impression. An important first

impression is the color and appearance of food. How do consumers perceive color? Humans

have difficulty in quantifying color, but are good at comparing it with a reference color.

Therefore, reference colors are used in many instances, e.g. color of a potato chip, salmon color,

egg yolk color, etc. In all these examples, the color of the food is relatively uniform. There are a

limited number of studies that correlate the uniform color of foods measured by instruments, and

by sensory panels. However, many foods have non-uniform colors, e.g. mangos, nectarines, etc.

How can we accurately measure the color in this case? Many instruments measure the average

color, but this causes loss of color information in the case of non-uniform foods. Machine vision

technology eliminates this problem by measuring all the colors at the surface of a non-uniform

food. Another difficulty is how to measure the non-uniformity of color. In this study, methods

were developed and used to quantify the non-uniformity of color with the use of machine vision

technology.

Once the non-uniformity of color is determined, how will this affect how consumers

perceive the color of non-uniform foods? Intuitively, we expect that the more non-uniform the

color, the more difficult it will be for consumers to describe or quantify it. In a preliminary

study, we found that for rabbit meat, the more non-uniform the color, the more error consumers

made in correctly quantifying it (Balaban and others, 2007).

In this study, we asked the following questions:

* Can the image of a food material, taken with a good digital camera, and under controlled
conditions, be substituted for the real food, for the purposes of evaluating visual and color
attributes? If this is possible, then geographical and temporal restrictions in evaluating
visual attributes will be eliminated. The image of a food can then be sent anywhere in the









world to be evaluated. Food images from different times can be compared without concern
for decay. Also, the image of the food, as an accurate representation of it, can be used for
record keeping.
* If reference colors are to be used in evaluating the non-uniform color of foods, how many
reference colors should be presented to the sensory panelists? How will the number of
reference colors affect the error that the consumer makes in quantifying the color? The
answer to this question would allow optimization of the number of reference colors to use.
* From a number of reference colors, how many colors should a panelist select? Too few
color choices may not allow a good representation of the actual color. On the other hand,
too many colors may confuse the panelist, and may allow large errors in the quantification
of real colors. The answer to this question will allow the fine-tuning of the way panelists
are asked to evaluate non-uniform colors.

The quantification of color is becoming increasingly important due to an emphasis on

international trade, and implementation of Hazard Analysis Critical Control Points (HACCP)

requiring record keeping. Thus, it is important to provide the agricultural industry with methods

to quantify and correlate sensory and instrumental evaluations of foods.

The overall impact of this study will be a better understanding of the way panelists

perceive non-uniform colors. This will result in a better formulation of consumer panel studies

involving visual attributes of foods.









CHAPTER 2
LITERATURE REVIEW

Color of Foods and Agricultural Materials

Color is an important quality attribute for almost every agricultural product (Delwiche,

1987). Consumers may perceive color as an indicator of freshness and wholesomeness, and

color may affect their final decision to accept/reject food. For the meat industry, muscle color is

the primary characteristic consumers consider when evaluating the quality and acceptability of

meats (Cornforth, 1994). The discoloration of retail beef accounts for $1 billion in price

discounts annually (Mancini and Hunt, 2005). Color determines the degree of ripeness of many

vegetables and fruits (Polder and others, 2000). Different grains and their varieties are

commonly characterized according to kernel color and quality defects such as grass-green, bin-

burnt, and fungal-damaged (Lou and others, 1999).

Color measurement of food and agricultural materials can be performed subjectively by

sensory panels (Chizzolini and others, 1993). Color can also be measured by instrumental

methods (Balaban and Odabasi, 2006). The quantification of color is becoming increasingly

important due to an emphasis on international trade, and implementation of Hazard Analysis

Critical Control Points (HACCP) requiring record keeping.

Instrumental Color Measurement in Agricultural Food Products

The agricultural industry uses mostly high cost, labor intensive methods to assure control

of color quality parameters. One possibility to reduce cost is to use instrumental methods to

measure color to emulate human visual perception (Zhu and Brewer, 1999). Instruments are

cost-effective, repeatable and objective in measuring color. Instruments such as colorimeters are

commonly used to measure color in the agricultural industry. Colorimeters provide users with

fast and simple "averaged" color measurements.









The accuracy of the instrument is assured by calibrating with standard color tiles before

measurement. The color reading is obtained by providing a controlled illuminant or standard

light source. Common standard light sources are: A=tungsten lamp, B= near sunlight, C= near

daylight, D= daylight. Colorimeters have illuminants C or D65 with color temperatures of 67740

K or 65040 K (Oliveira and Balaban, 2006). However, it is known that other methods provide

more precise color measurements (Coles and others, 1993). Colorimeters may not measure the

observed color if the product has non-uniform colors, because all colors in their view area are

averaged. If the agricultural product is too small, or too big, or has non-uniform surfaces, then

sampling location for color measurement becomes critical. Also, careful consideration is

necessary if data are compared between industrial plants, since variations between instruments

may occur (Brewer and others, 2001).

Spectrophotometers are also used in agriculture to measure color. The working method of

these instruments is based on the generation of a spectral curve representing the transmittance or

reflectance of light from the surface of the product. This is immediately compared with the

reflectance of a reference standard. The values may be converted to different color space values.

The agricultural industry requires a better method of color measurement. In the 1960s the

use of a camera with a computer and software capable of image processing became an option for

color measurement (Brosnan and Sun 2004). The system was called computer vision or machine

vision. The capabilities of this instrument were precise, accurate and fast color measurement of

agricultural products.

Computer Vision or Machine Vision System

The computer vision or machine vision (MV) systems started in the early 1960s. Since

then, the use of machine vision in the agricultural industry has grown. Machine vision is used

for its generation of precise data, consistency, and cost effective color measurement.









This instrument aims to emulate human visual perception by using a camera and a

computer with software capable of performing predefined visual tasks (Brosnan and Sun 2004).

Images are captured in digital form by a charge coupled device (CCD) camera. CCD cameras

can convert light into electrical charges and create high-quality, low-noise images with pixels.

They have excellent light sensitivity; they are free of geometric distortion and highly linear in

their response to light (Du and Sun 2004). The computer software then performs image

processing, which is the study of representation and manipulation of pictorial information

(Martin and Tosunoglu 2000). The pictorial information is converted to three-dimensional color

space of red (R) green (G) and blue (B) values. Further analysis provides color results.

Search for cost-reduction and increased efficiency in quality inspection has made the

agricultural industry look for techniques and instruments that provide more complex and

accurate as well as fast and objective determination of quality parameters in online inspection.

Machine vision has shown to be a useful method in this area (Blasco and others, 2003; Lee and

others 2004).

Machine vision has several other advantages over other color measurement instruments:

* Images are composed of the entire view area making the analysis more representative

* The data provided from images can be converted to different color measurement systems
(O'Sullivan and others 2003) and processed beyond the capabilities of colorimeters

* Non-uniform surfaces and colors can be handled easily

The agricultural industry uses image processing and MV to classify, sort and grade

agricultural produce in diverse areas such as bakery, meats and fish, vegetables, fruits, grains,

prepared consumer foods and even food container inspection. The food industry ranks among

the top ten industries to use image processing techniques (Gunasekaran, 1996).









Bakery Products

Bakery products are influenced by their external as well as internal appearance.

Consumer's judgment on their appearance dictates purchasing decision and marketability, and it

is essential to meet and exceed their expectations of quality of bakery products. At the same

time it is essential to reduce cost. A MV system was used to classify defective bread loaves by

height and top slope (Scott, 1994). Cookies were studied to estimate the fraction of top surface

area covered with chocolate chip, and other physical features such as size, shape and color of

baked dough (Davidson and others, 2001). MV was capable of providing automated inspection

and could separate light from dark muffin samples (Abdullah and others, 2000).

Red Meat and Seafood

In 2006, the retail value of U.S. beef industry was $71 billion (USDA, 2006 a). More than

12 billion kilograms of beef were consumed in the U.S. in the same year, and, the beef industry

represented 4.4% of U.S. total production exports. In the U.S. nearly 15% of retail beef is

discounted due to surface discoloration, which corresponds to annual revenue losses of $1 billion

(Mancini and Hunt, 2005). The USDA beef carcass grading system consists of two parts: quality

grade and yield grade. Quality grade is evaluated by trained individuals. MV has been

recognized as an objective alternative to assessment of meat quality from fresh-meat

characteristics (Tan, 2004). Recent studies indicate MV has great capability for classification

and grading of beef muscle type, breed, age and tenderness (Basset and others, 2000; Hatem and

others, 2003; Li and others, 1997).

The purpose of grading meats is to standardize the characteristics valuable to the consumer and

those that facilitate marketing and merchandising (Hatem and others, 2003). Beef rib eye steaks

were effectively graded for quality attributes such as color and marbling scores determined by









USDA using image processing (Gerrard and others, 1996). The results reported that MV

predicted color with an accuracy level of R2=0.86 and marbling with R2=0.84.

The pork industry has also applied MV to its processes. Pork loins were graded according

to color. Researchers used image processing with statistical and neural network models to

predict color scores of 44 pork loins (Lu and others, 2000). The scores were then compared with

trained sensory panel scores. The scores were based on visual perception ranging from 1 to 5.

Prediction error was the difference between instrumental and sensory scores. An error of 0.6 or

lower was considered not significant. Image processing and neural network models were able to

predict 93.2% of the samples with error lower than 0.6. Statistical regressions were able to

predict 84.1% of the samples with error lower than 0.6. Another study reported 90% agreement

between a MV color score and a sensory panel using 200 pork loin chops (Tan and others, 2000).

Tedious human inspection and costs are part of the grading practices in the poultry

industry. MV was used to separate defective (tumors, bruises, and torn skin and torn meat)

poultry carcasses from normal carcasses (Park and others, 1996).

In 2006, freshwater and marine fishing produced 60 million tons for human consumption

(FAO 2006). Americans consumed an average of 2.2 billion kg of seafood in 2006 (NOAA

2007). Fish represent one of the main sources of protein used in developing countries (Louka

and others, 2004).

Seafood inspection involves costly human involvement. MV was used to capture, identify

and differentiate images of three different varieties of fish: carp (Cyprinus carpio), St. Peter's

fish (Oreochromis sp.) and grey mullet (Mugil cephalus) (Zion and others, 1999). This study

also concluded that fish mass, an important quality parameter in marketing, could be predicted

from image area with the use of image processing. Other parameters important to market these









three types of fish were acquired. Fish species have also been sorted according to shape, length

and orientation in a processing line (Strachan, 1993). Image analysis was used to differentiate

between stocks of Haddock (Melanogrammus aeglefinus) (Strachan and Kell, 1995). Dimension

reduction derived from principal component analysis and canonical correlations was used. The

reports showed 71.7% correct sorting accuracy for shape and 90.9% and 95.6% for both stocks in

color differences. Flesh quality is important for successful development of fish farming and fish

processing (Marty-Mahe and others, 2004). Objective criteria to predict flesh redness from the

spawning coloration of fall chum salmon has been performed with image processing (Hatano and

others, 1989). Skin color development is an important quality parameter for live goldfish

(Carassius auratus), an ornamental fish of high commercial value (Chapman and others, 1997).

Objective measurement and quantification of the color of live goldfish (Carassius auratus)

raised in well water was acquired by a machine vision system (Wallat and others, 2002). The

color of dried cod fillets may go from yellow to orange, depending on the drying method used.

Image processing has been used to compare drying methods in cod fillets (Louka and others,

2004). The fillets were subject to three drying methods: hot air drying, vacuum drying, and

freeze drying. Image processing compared the three techniques to controlled instantaneous

discharge (DIC) and dehydration by successive discharge (DDS), two new techniques of drying

cod fillets. The highest whiteness value found was quantified in freeze-drying and the lowest in

air drying. Analysis of variance was used to find differences between procedures. Vacuum

drying and DIC did not have significant differences.

Catfish ranks as the fourth most popular seafood consumed in the U.S. Fresh farm-raised

catfish (Ictaluruspunctatus) quality relies primarily on human inspection. MV was used to

evaluate color changes over storage time for fresh farm-raised catfish (Korel and others, 2001).









Vegetables

Vegetables are greatly affected by quality factors such as size, shape, color, blemishes, and

diseases. Image processing resulted in more precise color measurement for potato crisp color

(Coles and others, 1993). The potato industry used a MV system and online inspection to grade

potatoes by shape (Tao and others, 1995). This study reported 89% agreement between the

instrument and human perception. The accuracy in grading potatoes was 90% by using hue,

saturation and intensity color system.

Discoloration of the mushroom cap reduced product quality, with less market value

(Brosnan and Sun, 2004). In order to maximize quality parameters, a MV system was used to

inspect and grade mushrooms based on color, stem cut, shape and cap veil opening (Hienemann

and others, 1994). MV resulted in a 20% classification error compared to two human inspectors.

The surface color of tomatoes was analyzed using a MV system classifying differences in

ripeness stages (Polder and others, 2000). Image processing from MV was used to recognize and

estimate cabbage size for a selective harvester (Hayashi and others, 1998). Surface defects,

curvature and brakes of carrots are quality parameters that influence the product's value. MV

was used to classify standard and defective carrots (Howarth and others, 1992).

Fruits

In 2005, the U.S. fruit consumption averaged 128 kg per person (fresh-weight equivalent)

(USDA, 2006b), with bananas being the most consumed fresh fruit. Apples were the second

favorite fresh fruit. A MV system was used to evaluate the color to determine the ability of

oxalic acid to inhibit browning in banana and apple slices (Yoruk and others 2004). Golden

delicious apples were evaluated for quality parameters such as bruises, scabs, fungi or wounds

with the use of a MV system (Leemans and others 1998). The results suggested that image









processing with different algorithms were able to detect bruises, scabs, fungi and wounds in

golden delicious apples.

In 2002, the U.S. was the world's largest importer of mangos (Perez and Pollack, 2002).

In this same year, Mexico shipped over 90% of its exports to the U.S. The increased

consumption of mangos is related to the increased population of Latino and Asian groups.

Consumers seek mangos without external damage, with stable weight, color and consistency, at a

reasonable price (Zufiiga-Arias and Ruben 2007). However, grading mangos for export involves

hand labor and subjectivity. A MV system equipped with cameras to obtain single and multiple

view image angles was used to evaluate physical parameters like: projected area, length, width,

thickness, volume, and surface area with 96.47% accuracy (Chalidabhongse and others, 2006;

Yimyam and others, 2005).

Prepared Consumer Foods

The evaluation of cheese functional properties such as different cooking conditions, size of

samples and shred dimensions are important aspects for the marketability of pizza. Topping

types, percentage and distributions influence the appearance and the different varieties of pizza.

Pizza image acquisition is very complex due to the non-homogenous colors, shapes, overlapping,

shadows, and light reflection. Methods have been developed to quantify the color distribution

and topping exposure in pizza (Sun and Du, 2004).

Food Container Inspection

MV and image processing are used to determine shape, and check for foreign matter,

threads of bottles, sidewalls and base defects, fill levels, correct closure and label position of

food containers. MV has also been used to check for wrinkles, dents and other damages to

aluminum cans that cause leakage of contents (Seida and Frenke, 1995).









Grains

Nigeria is the world's leading importer of wheat (USDA 2006c). Ninety percent of the

imported wheat is supplied by the U.S. Competitive prices and product quality has lead the U.S.

increase the wheat market in Nigeria. The variety, environmental effects and class make the

classification of wheat a very complex practice even for experienced inspectors. MV systems

and image processing have been used widely in wheat (Uthu, 2000; Majumdar and Jayas, 2000;

b; Nair and others, 1997). A MV system and crush force features were used to differentiate hard

and soft wheat varieties (Zayas and others, 1996). The correct differentiation rate was 94% for

the varieties tested. Corn kernels were analyzed with MV for whiteness, mechanical and mold

damage (Liu and Paulsen, 1997). Rice has also been studied using MV and image processing.

The appearance characteristics of brown rice such as kernel shape, color, and defects were

determined using a MV system (Wan and others, 2000). An online automatic inspection system

was able to recognize cracked, chalky, broken, immature, and damaged brown rice kernels.

Other Applications

A MV system was used for online inspection of dry sugar granules and powders to

determine particle size for process control and quality improvement (Strickland, 2000). Image

processing and MV were used to detect dirt on brown eggs with stains, dark feces, white uric

acid stains, blood stains and stains caused by egg yolk (Mertens and others, 2005). The results

reported 91% overall accuracy of image processing to detect dirty eggs.

Research efforts were made to provide efficient image-based techniques to monitor

distribution and migration of fish (Nery and others, 2005). Image processing was used to

classify nine species offish based on adipose fin, anal fin, caudal fin, head and body shape, size

and length/depth ratio of body (Lee and others, 2003). This method provided an alternative to

subjective monitoring of numbers, size and species at specific fish passages during migration.









Visual Texture Analysis

Visual texture is defined as how varied or patchy the color of a surface looks (Balaban,

2007). MV systems have been used in determining color, size and shape of agricultural produce.

Texture analysis with MV has great potential due to the powerful discriminating ability and

pattern recognition of this technique.

Texture information may be used to enhance the accuracy of color measurements

(Maenpaa, 2003). Texture is characterized by the relationship of the intensities of neighboring

pixels (Palm, 2004). Visual texture discriminates different patterns of images by extracting the

dependency of intensity between pixels and their neighboring pixels (Kartikeyan and Sarkar,

1991). In other words, texture is the repetition of a basic pattern. The patterns can be the result

of physical surface properties such as roughness or oriented strands, even the reflectance

differences given by a color on a surface (Tuceryan and Jain, 1998).

Visual texture analysis is divided into four main areas: statistical texture, structural texture,

model-based texture, and transform-based texture. Statistical texture describes mainly regions in

an image through high-order moments of their grayscale histograms (Bharati and others, 2004).

Structural texture is described as a composition of elements regulated by rules in images.

Model-based texture generates an empirical model of each pixel in the image based on a

weighted average of the pixel intensities in its neighborhood. Transform-order texture converts

the image into a new form using spatial frequency properties of the pixel under consideration of

its intensity variations.

Image analysis literature describes many ways to quantify texture (Bertrand and others,

1992; Mao and Jain, 1992; Reed and Du Buf, 1993; Tuceryan and Jain, 1998). A new method

used to quantify non-uniform colors is that of color primitives and color change index (Balaban,









2007). The methodology used to quantify color non-uniformity should be independent of

rotation, variation in size and shape (Zheng, 2006).

Visual Texture Applications in Agriculture

Image texture analysis has been used in grading and inspection for quality and safety of

agricultural products. A MV system was used with image processing and texture analysis to

quantify changes in color, shape and image texture of apple slices (Fernandez and others, 2005).

A method for texture analysis was developed to quantify non-homogeneity of color of mangos,

apples and rabbit meat using color primitives and color change index, were a color primitive was

defined as a continuous area of an image with similar light intensity (Balaban and others, 2007).

Texture analysis was used to identify the changes in textural appearance in experimental

breads caused by variations of surfactants added to flour (Bertrand and others, 1992). Iyokan

orange fruits (Miyauchi lyokan) were used to predict sugar content of oranges (Kondo and others,

2000). Image processing and texture analysis were entered to a neural network. MV system

along with neural networks recognized relatively sweet fruit from reddish color, low height,

medium size and glossy surface. Several studies on meat tenderness characteristics

(Chandraratne and others, 2006) and classification of genotypic origins of bovine meat (Basset

and others, 2000) have been successful. Texture analysis evaluated the microstructure of food

surfaces such as potatoes, bananas, pumpkins, carrots, bread crust, potato chips and chocolates

(Quevedo and others, 2002). Texture features have demonstrated to be effective discriminating

models for classifying wholesome and unwholesome chicken carcasses (Park and others, 2002).



Correlation between Image and Visual Color Analysis

The majority of studies regarding color comparison between sensory and instrumental

measures in foods have been developed in the meat area. Research was performed to compare









and correlate homogeneous color measurements of pork, beef, and chicken using instrumental

and visual color analysis (Denoyelle and Berny, 1999; Lu and others, 2000; Sandusky and Heath,

1998; Zhu and Brewer, 1999). Meat and poultry were used to correlate instrumental and visual

color evaluation. A range of meat redness was studied by mixing ground poultry breast and

ground beef. High correlations between visual redness and instrumental redness were found

(Zhu and Brewer, 1999).

The comparison and correlation of instrumental and visual color analysis has also been

studied in bakery, seafood, and in medical fields. Research using cookies for color analysis

showed a strong correlation between sensory and instrumental methods (Kane and others, 2003).

The relationship between sensory and instrumental correlations using raw, baked and smoked

flesh of rainbow trout (Onchoyhychus mykiss) was studied. Close relationship between color

evaluation by sensory analysis and instrumental methods was observed (Skrede and others,

1989). A study on colorimetric assessment of small color differences on translucent dental

porcelain revealed strong correlation between instrumental and visual color analysis (Seghi and

others, 1989). However, comparison and correlation of non-homogeneous color measurements

in foods is more challenging and has not been thoroughly studied.

Preliminary Experiments

A method was developed to quantify the perception of non-homogeneous colors of foods

by sensory taste panels. The average colors of mangos, apples, and rabbit meat were measured

using MV. Differences between the average (real) colors (MV system) and those from the

sensory panel were reported as AE values (Balaban, 2007).

A sensory panel composed of 20 panelists performed visual evaluations of rabbit meat captured

images and 60 panelists for that of real fruit and captured sample images. The degree of non-









uniformity of sample colors was determined using two methods: color blocks and color

primitives.

A color reference bar was developed for the panelists to select colors that represented those of

the samples. Panelists selected 3 colors from these reference colors, and estimated their

percentages. The "red mango" had more color blocks, and visually represented more non-

uniform colors. In the case of rabbit meat samples, there was no apparent advantage of using the

color block scheme. Clearly, a different method to quantify non-uniformity needed to be

developed for these samples. The rabbit samples had colors ranging from white to red, with

many shades in between. The lack of any other hue value may have contributed to the inability

of the color block scheme to quantify non-uniformity.

The more non-uniform samples were more difficult to evaluate, thus, the AE error was

higher. The non-uniformity of the samples caused more difficulty in the panelists' matching

ability with the reference color scale, and caused higher errors.

Males (33) and females (27) were compared regarding AE values. The mean AE for males

and females was 10.58 and 10.18, respectively, with a p-value= 0.52. In this study gender did

not significantly affect AE. A higher number of panelists may or may not affect this outcome.

This preliminary research suggested a criteria and parameters to quantify the error panelists

made when subject to visual appraisal of non-homogenous colors in foods (Balaban, 2007;

Balaban and others, 2007). However, the number of colors that panelists selected from a

reference color bar was limited to 3 choices. More studies are needed to study the effect of the

number of colors in the reference scale, and the number of colors to choose.

The food industry could benefit from a better understanding of precise, repeatable and

accurate color measurements of foods with non-uniform surfaces and/or colors. The quantitative









measurement of color attributes of agricultural materials is important in quantifying quality,

maturity, defects, and various other color-dependent properties. Global market expansion and

implementations of Hazard Analysis Critical Control Points (HACCP) require record keeping.

The difference of screen captured image and real sample and its effect on human perception of

sensory evaluations has not been studied thoroughly. A properly taken image of a food sample

can be a good representation of the food itself. This may provide a usable and more flexible tool

in the analysis of visual attributes.

Objectives of the Study

The objectives of this study were:

i. To measure differences in color evaluation between sensory panel and MV system, for
non-uniformly colored fruits and their images.

ii. To develop a quantitative measure of the degree of non-uniformity of color, and to
evaluate the effect of degree of non-uniformity of sample color on the difference in color
evaluation.

iii. To evaluate the effect of the number of reference colors, and number of allowed color
selections on the error in color evaluation









CHAPTER 3
MATERIALS AND METHODS

Mangos and Nectarines

The fruits used in this study were artificial fruits to avoid color degradation due to

maturation and decay of real fruits. Mangos and nectarines generally have non-uniform colors

and surfaces. The fruits used in this study consisted of red mangos and nectarines with non-

uniform surface colors. The mangos were purchased from Amazing Produce (4470 W. Sunset

Boulevard Suite 106 Los Angeles, CA 90027) and the nectarines made of compressed polyfoam

from Zimmerman Market (254 E Main St Leola, PA 17540) (Figure 3-1). The fruits were placed

on aluminum trays. Adhesive tape was used to keep fruits from moving while images were

captured. There were a total of 10 trays with one mango and one nectarine in each. The mangos

and nectarines shown in Figures A-i to A10 were first wrapped in grey paper (R= 128, G = 128,

B = 128) to obtain a color neutral background.

1 b 4-'-Milo


Figure 3-1. Example of mango and nectarine on aluminum tray.









Image Acquisition

The artificial fruits were placed inside a light box built of white acrylic sheets as shown

in Figure A- 1. The light box had top and bottom lighting with 2 fluorescent lights each to

simulate illumination by noonday summer sun (D65 illumination). The door remained closed

while images were captured to assure uniformity of light inside and to minimize the effect of

outside light. Images were captured using a camera (Nikon D200 Digital Camera, Nikon Corp.,

Japan) located inside the chamber mounted to face the bottom of the light box as shown in

Figure A-11. The image acquisition set up is shown in Figure A-12. The Nikon D200 Settings

used are described in Table 3-1. After the images were captured, trays were labeled for booth

and tray numbers for identification purposes.

Table 3-1. Nikon D200 settings.
Setting Specification
Device Nikon D200
Lens VR 18-200 mm F 3.5-5.6 G
Focal length 36 mm
Sensitivity ISO 100
Optimize image Custom
High ISO NR Off
Exposure mode Manual
Metering mode Multi-pattern
Shutter speed and aperture 1/3s -F/11
Exposure compensation (in camera) 0 EV
Focus mode AF-S
Long exposure NR Off
Exposure compensation (by capture NX) 0 EV
Sharpening Auto
Tone compensation Auto
Color mode Model
Saturation Normal
Hue adjustment 0
White balance Direct sunlight


Image Analysis

Each captured image included a "red" color standard with known L*, a*, and b* values

(Certified Reflectance Standard, Labsphere, ID# SCS-RD-020). Captured images of the fruits









were analyzed for average color, color blocks, and color-texture profiles using MV software.

The values obtained were compared with the measured L*, a*, and b* values of the red standard.

The difference of the L*, a*, and b* values was used as the correction factor for the whole

image. The images were "cleaned" using an image editing software. Each acquired pixel had

(R), (G) and (B) color intensities. The calibrated images were then used to determine the

average L*, a*, and b* values using every pixel of the fruits with Lens Eye color evaluation

software.

For color block analysis, the program read RGB values from every pixel in the captured

image, and counted that pixel a specified color block. Each pixel's RGB values were converted

first to tristimulus values XYZ, and then to L*, a*, and b* values.

The color data generated by the software was presented in histogram form. This feature

allowed all colors present on the surface area to be seen more easily. Because all colors present

were too numerous to be considered for the color scale formation, a method was developed to

represent the most significant surface colors.

Experimental Design

For this study, a completely randomized design was used. Because the effects of two or

more factors may affect the outcome, whether or not interaction exists, a factorial experimental

design was implemented.

The independent variables considered were number of reference colors, number of colors

to choose from the reference colors, and the sensory evaluation of screen image or real fruit. The

dependent variable for this study was the AE values. The AE value is the color differences

between sensory and MV measured colors of each sample for each panelist. AE measures total

color change by accounting for combined changes in L*a*b values.










AE = V( *, -L *' + (a -a *J2 + (b *, -b *,)2


The subscript 0 refers to the MV read values, and s refers to the panelists' average

The sensory panel combinations are shown in Table 3-2, and each session was performed

at different days using different panelists.

Table 3-2. Factorial-Level combinations.
Number of references Two Selections Four Selections Six Selections
8 Session 1 Session 2 Session 3
12 Session 4 Session 5 Session 6
16 Session 7 Session 8 Session 9


Method of Selection of the Reference Color Bars

Reference color bars were added to each image to be presented to the panelists (Figure A-

13). Using all 10 fruit tray images (both mangos and nectarines), sixteen global reference colors

were selected from all the color blocks with more than 1% of the surface area of a sample. The

reference color bars consisted of 8, 12 and 16 reference colors (Figure 3-2). Each color in the

reference color bars had known L*a*b values (Tables A-i, A-2, A-3).

The different color scales and number of colors were designed to test and quantify the

effect of these variables on the ability of panelists to match fruit colors. From our preliminary

study, we expected that it would be harder for panelists to correctly select several colors. On the

other hand, more color selection may enhance the ability to predict closer to the real color.

The sample numbers for tray and fruit images were the same. Also, the tray assigned to

each booth was maintained throughout the nine sensory sessions. However, the presentation of

the images or fruits was randomized.


(3-1)































Figure 3-2. Example of reference color bar with 8 colors added to fruit images presented to the
panelists.

Sensory Evaluations

The sensory panel was composed of college age students from the University of Florida.

Each session consisted of n=80 panelists. There were a total of 9 sessions (1 combination per

session) at different days using different panelists. Panelists evaluated fruits from two sources:

screen image and fruit tray. The presentation order was randomized.

The questionnaires consisted of two separate paper sheets handed to participants at the

stages of the sensory evaluation. Each paper sheet included: evaluation stage (image or tray

sample) date, age and gender of the panelist, booth number, and five instruction steps explaining

how to fill out the questionnaire. Also, each sheet included a table with two columns (Figure A-

14 and A-15). The two columns in each table included spaces to select the sample number of

colors to choose, and percentage of total area per color.









Sensory room staff explained to the panelists the reference color bars, and how to match

each color to the surface area of the fruits on the screen or in the tray. Once panelists finished

evaluating, e.g. the screen images, they handed in the questionnaire to staff, who made sure it

was properly filled out. In the second part of the session, using e.g. real fruit trays, staff would

pass the questionnaires and the fruit tray to panelists. They would also explain again the

directions to properly fill the questionnaires. Before the participants could leave the room our

sensory panel staff checked the above and made sure that the panelists followed instructions

properly, filled out evaluation sheets, one for screen image and one for fruit tray. It was critical

for our study that panelists selected the correct number of colors and that percentages added to

100. The questionnaires were checked one more time for selection numbers and percentages and

the data were entered to a spreadsheet to prepare for statistical analysis.

Determination of Color Uniformity of Fruit

Degree of non-uniformity of color or color-texture is a relatively new area in the computer

vision field. The degree of non-uniformity in this study was quantified using two methods: the

number of color blocks, and color primitives (Balaban, 2007).

Average Color:

Individual L*, a*, and b* values of each pixel in an object are read and averaged. For

uniform colored materials this method is satisfactory, but when the colors are non-homogeneous,

the averaging may result in unrealistic colors. For most agricultural materials colors vary

throughout the surface. Therefore an average color is of little use. Also, frequently defects or

ripening stages are detected because they are of different colors.









Color Blocks

The machine vision system used in this study captured images having 24 bits of color.

Each acquired pixel had three-dimensional color space RGB color intensities represented by 8

bits in the computer, resulting in 256 possible values for each. The total number of distinct

colors represented by this system (256)3 is too large to apply in reality and a known method was

used to reduce the number of colors in the color space. In this study each color axis was divided

into 16 (16 x 16 x 16 = 4096 color blocks). Any color within a color block was represented by

the center color of that block. The machine vision system then counted the number of pixels that

fell within a color block, and calculated the percentage of that color based on the total view area

of the object. Some color blocks were ignored because their percentage in the total area was too

small. The acceptance threshold for the color blocks was set to 1% of the total area. The

assumption was that the higher the number of color blocks, the more non-homogeneous the color

of the object.

Color Primitives

A color primitive is defined as a continuous area of an image where the "intensity" of any

pixel compared to an "anchor pixel" is within a given threshold value (Balaban, 2007). The

intensity difference is defined as A I.

AI = (R- R)2 (G G)2(B- B)2 (3-2)

Once all the pixels that belong to a primitive with AI values less than a given threshold are

found, and no other pixels can be added, then the anchor pixel is changed to an available,

neighboring pixel and the process continues until all the pixels of an object are processed.

The subscript o defines the "base" color, and the subscript s defines a pixel that is tested.









The center of gravity of the pixels belonging to a primitive was calculated (Balaban,

2007). Also, an equivalent circle with the same area as that of the primitive was found. The

radius of this circle was defined as:


radius =primitive area (3-3


This circle was drawn with its center at the center of gravity of the primitive. The

advantage of the color primitives is that there may be many primitives with the same color, but

they will be counted separately. The more color primitives in an image, the more non-

homogeneous the color of that object. The AI values of neighboring color primitives, and the

distance between their equivalent circles can be used to quantitatively calculate the degree of

non-homogeneity of color.


rate of change of color = (3-4)
Dis tan ce between two equivalent circles

The more color primitives there are, the higher the value of the cumulative "color change

index". Also, the bigger the area of an object the more color primitives, everything else being

equal. Therefore, a "color change index (CCI)" was proposed (Balaban, 2007):

I Al for all neighboring primitives number of neighbors 100 (3
dis tan ces between equivalent circles object area

MV results reported L*, a*, and b* values. Lens Eye color evaluation software required all

reference bar L*a*b values to be entered. It also required the input of all MV determined L*a*b

values from all 10 trays. The final data input to the program was that of the panelists and their

choices of colors from the reference color bars.

The output of the program reported panelists L*a*b values as well as MV determined L*a*b

values. It finally calculated AE values for all nine sensory sessions. Each reference color's L*,









a*, and b* values were weighted by the percentage, and averaged, providing the estimated

average color of a sample. The data was grouped in order to prepare for statistical analysis.


EstimatedAverageColorL* = -L *-, ,, (3-6)
1100 re


EstimatedAverageColora* = ----a* (3-7)
1100 ref,


EstimatedAverageColorb* = b *, 1 (3-8)
-1, 100

The variable n refers to the number of selections (2, 4, and 6). Pi refers to the percentage of

color i. L*ref,I is equal to the L* value of ith reference color. a*ref,I is equal to the a* value of ith

reference colors, b*ref,I is equal to the b* value of ith reference colors.

Calculation of Best Possible AE

There is an inherent error in trying to correctly quantify the non-uniform color of a material

using a finite number of reference colors, and a finite number of selections from these colors.

The best possible values given the different selection of reference colors and choices a panelist

could provide are described as "the best possible AE". A computer program was developed to

take each combination of possible choices from a given number of reference colors, and then try

the percentages of these selected colors from 1 to 100, accept the cases with the sum of all

percentages adding to 100, and find the combination with the minimum AE. This value can then

be subtracted from the AE values of panelists (absolute AE) to form a more accurate error term.

Statistical Analysis

The AE for sensory and MV measured colors of each sample for each panelist were

calculated. The results were evaluated for the ten booths and nine sessions. The AE values were

analyzed using SAS 9.0 for statistical analysis.









ANOVA procedures (Duncan's multiple range tests) and Mixed Models (Restricted

Maximum Likelihood REML LS means) were used to find significant differences on the effect

of reference color bar, number of selections, and presentation source screen image or fruit tray.

The mixed model provided the flexibility of modeling not only the means of data (as in the

standard linear model) but their variances and co variances and fixed effects as well. The need

for covariance parameters arouse because repeated measurements were taken on the same

experimental unit, and these repeated measurements are correlated or exhibit variability that

changes. The ANOVA can provide incorrect results depending on the design because if analysis

is driven by accounting for degrees of freedoms and tests, p-values, contrasts, least square means

etc. may be taken for granted. The ANOVA used in this experiment computed means of the

dependent variables for the effects mentioned earlier based on Ordinary Least Squares. All main

effects were tested using means for those effects.

In each method, AE absolute or AE difference were used as model statements or dependent

variables. The program codes and outputs are shown in Tables E-1 and 2.










CHAPTER 4
RESULTS & DISCUSSION

MV Color Results of Fruits

MV measured colors of each fruit in each booth are summarized in Tables 4-1 and 4-2.

For both mangos and nectarines it can be seen that L*a*b values and number of color blocks are

similar for all 10 booths. It is also seen that the Color Change Index (CCI), the number of

primitives and the number of neighbors are slightly different for each booth.


Table 4-1. MV
Booth L*
1 51.79
2 52.92
3 49.62
4 50.42
5 48.41
6 56.39
7 48.48
8 46.88
9 46.64
10 52.22


color analysis for mangos.
a* b* # Color Blocks
31.91 42.39 31
27.18 45.61 31
37.21 44.4 33
25.31 42.91 33
28.24 42.62 22
21.93 46.84 30
39.19 42.4 32
38.92 40.34 20
36.3 40.59 33
31.26 43.85 32


Table 4-2. MV
Booth L*
1 52.57
2 53.3
3 53.16
4 54.61
5 62.78
6 60
7 65.25
8 53.45
9 56.51
10 57.57


color analysis for
a* b*
36.94 43.81
35.55 45.31
34.67 48.11
35.67 41.26
26.47 50.46
28.71 49.91
24.48 45.09
38.59 44.06
30.86 43.92
29.13 42.24


nectarines.
# Color Blocks
31
31
32
27
28
31
25
30
31
30


CCI
17.516
13.721
13.135
13.202
11.326
14.231
17.759
15.194
11.490
14.768


# Primitives
915
693
581
794
626
729
856
756
739
773


# Neighbors
1760
1366
1196
1521
1215
1491
1648
1488
1414
1566


CCI
6.423
9.218
5.588
4.904
6.968
7.164
6.762
4.338
4.589
11.294


# Primitives
616
613
513
548
593
615
609
538
536
738


# Neighbors
1190
1239
982
1012
1127
1190
1196
964
1016
1511


I


I









The tables A-4, A-5 show a summary of the number of primitives, number of neighbors

and color change index for mangos and nectarines used in this study. The color primitives

analysis, e.g. mango and nectarine for booth 1, is shown in Figure A-16.

Non-Uniformity Analysis of Fruits

The degree of non-uniformity was determined using color blocks and number of primitives

(Tables 4-1, 4-2). The numbers of color blocks considered were those colors greater than 1% of

the sample surface area. The average L*a*b values in all booth for mangos and nectarines were

similar to each other. Because of these similarities between booths, the number of color blocks

were also similar between booths. The number of color blocks for the two fruits had a range of

values from 20-33, with higher values being predominant. However, in some instances, e.g.

booth 8 for mangos and booth 7 for nectarines, the number of color blocks was 20 and 25,

respectively (Tables 4-1 and 4-2). For nectarines, the range of color blocks was from 32 to 25,

representing a change of 22%. The CCI values ranged from 17.7 to 11.3, representing a change

of 36%. This suggests that the number of color blocks for mangos and nectarines is not a better

parameter to quantify the non-uniformity of these samples, compared to CCI. This was expected,

since from previous results it was considered that the number of color blocks does not represent

an accurate method to quantify colors of non-homogenous foods with a wide distribution of

hues.

Although the number of color blocks in most booths were close, it can be seen that the

number of primitives, number of neighbors, and CCI change for each booth were different. For

example, the CCI for mango in booth 1 was 6.4 and that for booth 2 was 9.2. For both fruits, the

number of color blocks was 31. The same pattern is seen for booth 1 and booth 6 of the

nectarines as shown in Table 4-2.










The correlation between CCI, number of primitives and number of neighbors for both

mangos and nectarines is shown in Figures 4-1, 4-2 and 4-3.


1000
Mango Nectarine R-square = 0.7895
900


800
(U r
E 700
"r
Q_

600 U U


500


400-


0 5 10 15 20
CCI

Figure 4-1. Correlation between number of primitives and color change index (CCI).


1800
1700- Mango Nectarine
R-square = C

1600

1500- .

n 1400

D 1300
z
1200- m

1100

1000- .

900-
0 5 10 15 20
CCI

Figure 4-2. Correlation between number of neighbors and color change index (CCI).


1.8471










1800

1700 Mango Nectarine R-square = 0.9685

1600

1500 -. .

1400
-C
0)
'D 1300
z
1200

1100

1000-

900----------
400 500 600 700 800 900 1000
#primitives


Figure 4-3. Correlation between number of neighbors and number of primitives.

From these figures it can be seen that these three variables are strongly correlated, and

therefore can be used interchangeably. The number of primitives, CCI and the number of

neighbors effectively quantified the non-uniformity of mangos and nectarines in all 10 booths.

In general, nectarines were more non-uniform than mangos. It would not be possible to make

this conclusions using the L*a*b average values or the number of color blocks.

Best Possible AE

Tables 4-3 to 4-10 show the best possible AE for various combinations of color

references and selections. The cases with 6 selections of colors are not shown since all best AE

values were less than 1. It can be seen that especially for 2 or 4 selections, the best AE values

can be significantly high. This means that there is an inherent error associated with selection of

2 or 4 reference colors regardless of the number of available reference colors. Therefore, the

"real" error that a panelist makes in estimating the color of a sample is the difference between










the "absolute AE" and the "best possible AE". Large "best AE" values are not restricted to 2

selections only. This is shown in Table 4-8 with 4 selections out of 8 reference colors in booth 7.

Table 4-3. Best possible selections and minimum AE value possible for 8 references and 2
selections for mangos.


Booth Color 1 1% Color 2 2% Min AE
1 3 56 5 44 2.923
2 3 46 5 54 1.895
3 3 67 8 33 2.407
4 3 14 4 86 2.588
5 1 42 3 58 1.931
6 4 87 7 13 2.669
7 3 71 8 29 2.191
8 3 70 5 30 0.827
9 3 46 4 54 0.612
10 3 54 5 46 2.800

Table 4-4. Best possible selections and minimum AE value pos,
selections for nectarines.
Booth Color 1 1% Color 2 2% Min AE
1 3 65 8 35 1.337
2 3 62 8 38 0.907
3 3 59 8 41 1.893
4 3 63 8 37 4.659
5 3 40 8 60 2.647
6 3 45 8 55 1.267
7 3 40 8 60 8.666
8 3 59 7 41 1.247
9 3 55 8 45 4.646
10 3 53 8 47 7.079


sible for 8 references and 2


Table 4-5. Best possible selections and minimum AE value possible for 12 references and 2
selections for mangos.
Booth Color 1 1% Color 2 2% Min AE
1 3 56 5 44 2.923
2 3 46 5 54 1.895
3 3 67 8 33 2.407
4 3 14 4 86 2.589
5 1 42 3 58 1.932
6 4 87 7 13 2.669
7 3 71 8 29 2.192
8 3 70 5 30 0.827
9 3 46 4 54 0.613
10 3 54 5 46 2.801










Table 4-6. Best possible selections and minimum AE value possible for 12 references and 2
selections for nectarines.
Booth Color 1 1% Color 2 2% Min AE
1 3 65 8 35 1.338
2 3 62 8 38 0.908
3 3 59 8 41 1.893
4 3 63 8 37 4.660
5 3 40 8 60 2.647
6 3 45 8 55 1.267
7 3 40 8 60 8.666
8 3 59 7 41 1.247
9 3 55 8 45 4.646
10 3 53 8 47 7.079


Table 4-7. Best possible selections and minimum AE valu
selections for mangos.
Booth Color 1 1% Color 2 2% Min AE
1 3 56 5 44 2.923
2 3 46 5 54 1.895
3 3 67 8 33 2.407
4 3 14 4 86 2.589
5 1 42 3 58 1.932
6 4 87 7 13 2.669
7 3 71 8 29 2.192
8 3 70 5 30 0.827
9 3 46 4 54 0.613
10 3 54 5 46 2.801


Table 4-8. Best possible selections and minimum AE valu
selections for nectarines.
Booth Color 1 1% Color 2 2% Min AE
1 3 65 8 35 1.338
2 3 62 8 38 0.908
3 3 59 8 41 1.893
4 3 63 8 37 4.660
5 3 40 8 60 2.647
6 3 45 8 55 1.267
7 3 40 8 60 8.666
8 3 59 7 41 1.247
9 3 55 8 45 4.646
10 3 53 8 47 7.079


e possible for 16 references and 2

















e possible for 16 references and 2










Table 4-9. Best possible selections and minimum AE value possible for 8 references and 4
selections for mangos.
Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min AE
1 3 44 4 1 6 43 7 12 2.798
2 3 29 4 1 6 52 7 18 1.377
3 3 15 4 1 6 32 7 52 0.177
4 1 1 3 1 4 17 5 81 2.576
5 1 25 3 1 4 53 5 21 1.792
6 3 19 5 1 6 62 7 18 2.990
7 3 30 4 3 6 28 7 39 0.664
8 3 64 4 1 6 29 7 6 0.686
9 3 3 4 54 5 42 6 1 0.587
10 3 30 4 1 6 44 7 25 2.182



Table 4-10. Best possible selections and minimum AE value possible for 8 references and 4
selections for nectarines.
Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min AE
1 3 1 4 1 6 33 7 65 2.828
2 3 1 5 1 6 36 7 62 2.564
3 5 1 6 41 7 40 8 18 2.125
4 3 17 4 1 6 36 7 46 5.930
5 5 1 6 59 7 36 8 4 8.814
6 5 1 6 54 7 36 8 9 6.851
7 4 1 5 1 6 60 7 38 11.595
8 4 1 5 1 6 30 7 68 3.688
9 3 1 4 1 6 45 7 53 5.452
10 3 18 4 1 6 49 7 32 7.221

Table 4-11. Best possible selections and minimum AE value possible for 12 references and 4
selections for mangos.
Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min AE
1 4 32 5 47 7 2 9 19 0.0211
2 5 75 7 12 8 2 9 11 0.0225
3 4 8 5 43 7 43 10 6 0.0165
4 1 37 3 6 4 47 9 10 2.3482
5 1 34 3 1 4 62 9 3 1.7248
6 5 72 6 5 9 19 10 4 0.0220
7 1 11 3 58 4 11 10 20 0.0288
8 3 54 5 39 9 5 11 2 0.0352
9 1 18 4 75 5 2 9 5 0.4383
10 3 36 6 37 7 13 9 14 0.0314










Table 4-12. Best possible selections and minimum AE value possible for 12 references and 4
selections for nectarines.
Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min AE
1 4 59 7 8 9 10 12 23 0.0396
2 5 43 8 30 9 26 12 1 0.0192
3 1 14 3 38 8 15 10 33 0.0252
4 3 18 5 26 7 18 9 38 0.0527
5 6 9 7 32 9 34 11 25 0.0318
6 2 23 4 11 9 39 11 27 0.0241
7 5 20 8 1 9 70 11 9 0.0453
8 4 34 7 32 9 20 11 14 0.0327
9 2 22 4 8 6 20 9 50 0.0313
10 1 17 3 6 4 27 9 50 0.0616


Sensory Panel Results

Images and trays of fruits were used in visual sensory evaluations. The reference color bars

and color selections provided to panelists had the following combinations:

1. Treatment 1 = 8 reference color and 2 color selections

2. Treatment 2 = 8 reference colors and 4 color selections

3. Treatment 3 = 8 reference colors and 6 color selections

4. Treatment 4 = 12 reference color and 2 color selections

5. Treatment 5 = 12 reference colors and 4 color selections

6. Treatment 6 = 12 reference colors and 6 color selections

7. Treatment 7 = 16 reference colors and 2 color selections

8. Treatment 8 = 16 reference colors and 4 color selections

9. Treatment 9 = 16 reference colors and 6 color selections

The summary performance of panelists evaluating mangos and nectarines in booth 1 are shown

in Tables 4-13 and 4-14, respectively. The summary performance for the rest of the booths is

shown in Tables B-1 to B-10.










Table 4-13. Summary performance for panelists evaluating mangos for booth 1.
Case Best AE Screen AE Stdev Tray AE Stdev
1* 2.920 11.389 9.556 15.752 6.046
2* 2.790 10.701 5.158 10.615 2.298
3* 0.200 9.488 4.044 10.519 5.414
4* 2.920 21.152 7.643 18.036 8.864
5* 0.200 13.533 7.794 12.844 3.014
6* 0.200 13.872 8.261 12.729 4.741
7* 2.920 15.198 10.286 14.933 4.890
8* 0.200 13.972 9.509 11.335 1.861
9* 0.200 13.907 5.911 6.404 6.404


The number of reference colors and color selections has an impact on the error made by

panelists as reflected in absolute AE values (screen AE or tray AE). In Table 4-13 the best AE

that panelists could obtain for treatment 1 (2 selections) was 2.92. The actual average values

provided by panelists evaluating the screen image was 11.34 with a standard deviation of 9.56,

and that for fruit tray was higher at 17.75 and smaller standard deviation of 6.05. More color

selections reduce the AE values of the visual evaluation of mangos. This is shown for AE values

in case 2 (4 selections) and case 3 (6 selections) in Table 4-13.


Mango, abs.DE, real fruit
40
8 ref, 2
12 ref, 2
30- 16 ref, 2


L20


10


0-
0 1 2 3 4 5 6 7 8 9 10 11
trays



Figure 4-4. Comparison of AE values for 8, 12, and 16 reference colors, 2 selections.









For mangos, it was apparent that panelists had more difficulty with 12 reference colors

and its combinations. Treatment 4 provided panelists with the most challenge of all

combinations reflecting a high AE value of 21.15 for the screen image and 18.036 for fruit tray.

The AE values for the combinations with 12 references are higher than the rest of the

combinations (Figure 4-4).

Table 4-14. Summary performance for panelists evaluating nectarines for booth 1.
Case Best AE Screen AE Stdev Tray AE Stdev
1* 1.34 9.148 4.788 12.970 8.376
2* 2.82 7.410 2.789 11.314 3.515
3* 0.2 7.610 2.724 10.089 2.376
4* 1.34 25.728 14.359 20.020 14.592
5* 0.2 13.545 9.165 11.070 1.256
6* 0.2 8.770 1.822 10.615 4.044
7* 1.34 13.271 11.203 12.658 1.423
8* 0.2 9.950 6.329 12.572 3.761
9* 0.2 14.134 10.592 10.566 4.915


In Table 4-14 the best AE that panelists could obtain for treatment 1 was 1.34. The actual

average value provided by panelists for the screen image of a nectarine was 9.15 with a standard

deviation of 4.79, that for fruit tray was 12.97 and smaller standard deviation of 8.38. In general,

the AE values of nectarines were higher than those of mangos.

Statistical Analysis

The data for the sensory panel was analyzed for sources of variation such as number of

reference colors, selection of colors and its interactions, presentation: screen image or fruit tray,

and its interactions with references and selections, using two different models: Mixed model

results, and ANOVA analysis of variance. For the statistical analysis the difference in AE was

also used as our dependent variable because this provided a more realistic number due to the best

possible outcome by panelists given the combinations of reference colors and selection of colors.









The difference in AE was the value obtained from subtracting best AE from absolute AE. Both

models provided similar data. The data was separately analyzed for mangos and nectarines.

Mangos

The analysis of variance showed significant differences between the number of reference

colors, the number of selections, presentation and the interaction between the reference colors

and the number of selections, both for the AE absolute and difference in AE (p-value=0.0001) as

shown in Tables 4-15 and 4-16. The mixed mode analysis also reported these same significant

differences as shown in Tables E-1 and E-2. The rest of the interactions did not result in

significant differences for reference color and presentation, selections and presentation and

reference colors, selections of colors and presentation.

Table 4-15. ANOVA summary absolute AE for mangos.
Source DF* ANOVA SS* MEAN Square F Value Pr >F
Ref. colors 2 2100.72 1050.36 22.47 < .0001
Selections 2 4410.37 2205.18 47.18 < .0001
Presentation 1 10.55.36 1055.36 22.58 < .0001
Ref. colors selections 4 1948.59 487.15 10.42 < .0001
Ref. colors* presentation 2 189.06 94.53 2.02 0.133
Selections*presentation 2 136.41 68.21 1.46 0.233
Ref. 4 65.66 16.41 0.35 0.843
colors*selections*presentation
Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.


Table 4-16. ANOVA summary difference AE for mangos.
Source DF ANOVA SS MEAN Square F Value Pr >F
Ref. colors 2 2602.76 1301.38 27.73 <.0001
Selections 2 1356.82 678.41 14.46 <.0001
Presentation 1 1055.28 1055.28 22.49 <.0001
Ref. colors selections 4 1769.15 442.29 9.42 <.0001
Ref. colors* presentation 2 189.43 94.53 2.01 0.134
Selections*presentation 2 136.43 68.21 1.45 0.234
Ref. colors*selections*presentation 4 65.66 16.41 0.35 0.844
Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.












It is apparent that the error means for color selections, 2 color choices had the highest mean


and was significantly different than the rest as shown in Figure 4-4 and 4-5. Panelists tend to


make more errors when selecting only 2 colors. The error decreases and panelists become more


efficient with more color choices.

The ANOVA Procedure
Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.74127
Number of Means 2 3
Critical Range .8657 .9115
Means with the same letter are not significantly different.
Duncan Grouping Mean N Selections

A 15.2401 480 2

B 11.9144 480 4

B 11.2349 480 6

Figure 4-4. Absolute AE means difference of selections of colors using mangos.


The ANOVA Procedure
Duncan's Multiple Range Test for DiffAE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.92962

Number of Means 2 3
Critical Range .8674 .9133

Means with the same letter are not significantly different.

Duncan Grouping Mean N Selections

A 13.1554 480 2

B 11.1637 480 4

B 11.0349 480 6

Figure 4-5. Difference in AE means difference of selections of colors using mangos.


Because of this, the rest of the color selections, 4 and 6 choices had mean values showing no


significant difference between each other for both AE absolute and difference in AE.


This same pattern was seen using the mixed mode for statistical analysis and is shown in Figures


E-1 and E-2.











It was also apparent that the 8 reference colors provided less error both for the AE absolute


and difference in AE as shown in Figures 4-6 and 4-7. The highest error made by panelists was


with 12 reference colors compared to 8 and 16. However, nectarines reported slightly higher


error values than mangos. This may be due to the higher non-uniformity of nectarines making the


evaluations harder for panelists. The same results were obtained using the mixed mode as shown


in Figures E-3 and E-4.

The ANOVA Procedure
Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.74127

Number of Means 2 3
Critical Range .8657 .9115

Means with the same letter are not significantly different.

Duncan Grouping Mean N Reference Colors
A 14.3974 480 12
B 12.5116 480 16
C 11.4803 480 8
Figure 4-6. Absolute AE means for reference colors for mangos.

The ANOVA Procedure
Duncan's Multiple Range Test for DiffAE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.92962

Number of Means 2 3
Critical Range .8674 .9133

Means with the same letter are not significantly different.
Reference
Duncan Grouping Mean N Colors

A 13.4795 480 12
B 11.6834 480 16
C 10.1910 480 8


Figure 4-7. Difference in AE Means for reference colors for mangos.

The interaction between the number of reference colors and the number of color selections


also reported significant differences. The highest error made by panelists was when evaluating


treatment 4 or 12 reference colors and 2 color choices as shown in Figures 4-8 and 4-9 both for


absolute AE and difference in AE.











The ANOVA Procedure
Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1431
Error Mean Square 47.45812
Number of Means 2 3 4 5 6 7 8 9
Critical Range 1.511 1.591 1.644 1.684 1.715 1.740 1.761 1.779
Means with the same letter are not significantly different.
Duncan Grouping Mean N TRT
A 19.1266 160 4
B 13.9341 160 7
C B 12.6597 160 1
C B 12.4915 160 5
C D 11.8113 160 9
C D 11.7896 160 8
C D 11.5741 160 6
C D 11.4620 160 2
D 10.3192 160 3

Figure 4-8. Absolute AE means for interaction between the number of reference colors and the
number of selections.


The lowest error made by panelists was with treatment 3 or 8 reference colors and 6


selections both for absolute AE and difference in AE. The rest of the treatments were slightly


different however, providing significant differences. It is clear that the more color selections, the


less error made by the panelists. It is possible that up to certain level of reference colors panelists


would perform more efficiently, and above that level it would too complicated for the panelists


to refer to color selections and reference colors. There may be an optimum number of reference


colors.


















Number of Means
Critical Range


Duncan's
Alpha
Error
Error
2 3
1.514 1.594


The ANOVA Procedure
Multiple Range Test for DiffAE
0.05
Degrees of Freedom 1431
Mean Square 47.64525


4
1.647


5
1.687


b
1.718


Means with the same letter are not significantly different.


Duncan Grouping


Mean
17.0419
12.0225
11.8493
11.6113
11.5896
11.3741
10.5750
10.1192
9.8789


Figure 4-9. Difference in AE means for interaction between the number of reference colors and
the number of selections.


The presentation (screen image vs. fruit tray) was also significantly different (p-


value=0.0001). The fruit tray had mean values higher than the screen image both for absolute


AE and difference in AE as shown in Figures 4-10 and 4-11. These same results were obtained


using the mixed mode as shown in Figures E-5 and E-6.


The ANOVA Procedure
Duncan's Multiple Range Test for AE


Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.74127

Number of Means 2
Critical Range .7068


Means with the same letter are not significantly different.


Duncan Grouping


Mean N Source


A 13.6525 720
B 11.9404 720


Figure 4-10. Absolute AE means for presentation for mangos.


7
1.743


8
1.765


.783
1.783










The ANOVA Procedure
Duncan's Multiple Range Test for DiffAE

Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 46.92962

Number of Means 2
Critical Range .7083

Means with the same letter are not significantly different.

Duncan Grouping Mean N Source
A 12.6407 720 F
B 10.9286 720 S

Figure 4-11. Difference AE means for presentation for mangos.

Nectarines

The analysis of variance resulted in significant differences between reference colors, the

number of selections, presentation and the interaction between the reference colors and the

selection of colors for both the AE absolute and the difference in AE (p-value = 0.0001) as shown

in Tables 4-17 and 4-18.

Table 4-17. ANOVA summary absolute AE for nectarines.
Source DF ANOVA SS MEAN Square F Value Pr >F
Ref. colors 2 2827.38 1413.69 30.00 <.0001
Selections 2 7657.60 3828.80 81.25 <.0001
Presentation 1 761.75 761.74 16.16 < .0001
Ref. colors selections 4 3850.96 962.74 20.43 <.0001
Ref. colors* presentation 2 154.12 77.06 1.64 0.195
Selections*presentation 2 122.78 61.39 1.30 0.272
Ref. 4 238.36 59.59 1.26 0.282
colors*selections*presentation
Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.

Similar to mangos, the interactions for reference color and presentation, selections and

presentation and reference colors, selections of colors and presentation did not result in

significant differences. These same results were obtained using the mixed mode as shown in

Tables E-3 and E-4.











The rest of the interactions did not result in significant differences for reference color and

presentation, selections and presentation and reference colors, selections of colors and

presentation.


Table 4-18. ANOVA summary difference in AE for nectarines.
Source DF ANOVA SS MEAN Square F Value Pr >F
Ref. colors 2 6968.87 3484.44 75.76 <.0001
Selections 2 1903.92 951.96 20.70 <.0001
Presentation 1 761.82 761.82 16.56 <.0001
Ref. colors selections 4 4565.35 1141.34 24.82 <.0001
Ref. colors* presentation 2 154.10 77.05 1.68 0.188
Selections*presentation 2 122.79 61.39 1.33 0.264
Ref. colors*selections*presentation 4 238.38 59.59 1.30 0.269
Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.



The same pattern seen previously with mangos were 8 reference colors reported the lowest

value and 12 reference colors the highest value as shown in Figures 4-12 and 4-13 both for the


absolute AE and the difference in AE. Similar results were obtained using the mixed mode as


shown in Figures E-9 and E-10.




The ANOVA Procedure
Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 47.12485
Number of Means 2 3
Critical Range .8692 .9152


Means with the same letter are not significantly different.
Reference
Duncan Grouping Mean N Colors


A 14.9077
B 13.0530
C 11.4792


Figure 4-12. Absolute AE means for reference colors for nectarines.











The ANOVA Procedure

Duncan's Multiple Range Test for DiffAE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 45.99134

Number of Means 2 3
Critical Range .8587 .9041
Means with the same letter are not significantly different.
Reference
Duncan Grouping Mean N Colors

A 13.6839 480 12
B 11.7746 480 16
C 8.3653 480 8

Figure 4-13. Difference AE means for reference colors for nectarines.


The number of selections was also significantly different with (p-value= 0.0001). However,


when looking at the means for color selections, 2 color choices had the highest mean of the rest


of the color selections, 4 and 6 as shown in Figure 4-14 and there were no significant difference


between 4 and 6 color selections of Difference in AE as shown in Figure 4-15, the same case as


the mangos. Similar results were obtained with the mixed mode procedures as seen in Figures


E-7 and E-8.





The ANOVA Procedure
Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 47.12485

Number of Means 2 3
Critical Range .8692 .9152

Means with the same letter are not significantly different.
Duncan Grouping Mean N Selections

A 16.3154 480 2
B 12.2299 480 4
C 10.8946 480 6

Figure 4-14. Absolute AE means for selection of colors for nectarine.











The ANOVA Procedure

Duncan's Multiple Range Test for DiffAE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 45.99134
Number of Means 2 3
Critical Range .8587 .9041
Means with the same letter are not significantly different.
Duncan Grouping Mean N Selections
A 12.8803 480 2
B 10.6946 480 6
B 10.2490 480 4


Figure 4-15. Difference AE means for selections of colors for nectarines.


The interaction between reference colors and number of selection of colors also resulted in


significant differences with (p-value = 0.0001).


The ANOVA Procedure

Duncan's Multiple Range Test for AE
Alpha 0.05
Error Degrees of Freedom 1431
Error Mean Square 47.72086
Number of Means 2 3 4 5 6 7 8 9
Critical Range 1.515 1.595 1.649 1.688 1.719 1.745 1.766 1.784
Means with the same letter are not significantly different.
Duncan Grouping Mean N TRT
A 21.2744 160 4
B 14.9965 160 7
C 12.8476 160 5
C 12.6753 160 1
C 12.4578 160 8
D C 11.7046 160 9
D C 11.3842 160 2
D 10.6010 160 6
D 10.3782 160 3

Figure 4-16. Difference AE means for selections of colors for nectarines.


For both the absolute AE and difference in AE treatment 4 or 12 reference colors and 2 color


choices had the highest value as shown in Figures 4-16 and 4-17. Similar to mangos, panelists


had the most difficulty in matching 12 reference colors and 2 color selections. Panelist also had


difficulty in evaluating treatment 7 or 16 reference colors and 2 color selections reported as the


second highest mean error for absolute AE. Panelists performed best and reported the lowest


error value for difference in AE with treatment 2 or 8 reference colors and 4 color selections as


shown in Figure 4-16.












The ANOVA Procedure


Multiple Range Test for DiffAE
0.05
Degrees of Freedom 1431
Mean Square 46.59453


Number of Means
Critical Range


2
1.497


3
1.576


4
1.629


5
1.668


6
1.699


7 8
1.724 1.745


Means with the same letter are not significantly different.
Duncan Grouping Mean N TRT


A 17.8393
B 12.8114
B 12.2578
C B 11.5613
C B 11.5046
C D 10.4010
C D 10.1782
D 9.2402
E 5.6777


Figure 4-17. Difference AE means for selections of colors for nectarines.


The presentation (screen image vs. fruit tray) was also significantly different (p-


value=0.0001). The fruit tray had mean values higher than the screen image as shown in Figures


4-18 and 4-19 for both AE and Difference in AE. Similar results were obtained using the mixed


mode as shown in Figures E-11 and E-12.


The ANOVA Procedure
Duncan's Multiple Range Test for Delta_E
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 47.12485
Number of Means 2
Critical Range .7097
Means with the same letter are not significantly different.
Duncan Grouping Mean N Source
A 13.8739 720 F
B 12.4193 720 S
Figure 4-18. Absolute AE means for presentation for nectarines.


Duncan's
Alpha
Error
Error


9
1.763










The ANOVA Procedure
Duncan's Multiple Range Test for DiffDE
Alpha 0.05
Error Degrees of Freedom 1422
Error Mean Square 45.99134
Number of Means 2
Critical Range .7011

Means with the same letter are not significantly different.

Duncan Grouping Mean N Source

A 12.0020 720 F
B 10.5473 720 S

Figure 4-19. Difference AE means for presentation for nectarines.

AE vs. CCI

The number of primitives, number of neighbors, and CCI measure the degree of non-

uniformity of color. AE gave the difference between MV and panelists color appraisal. The


correlation between AE and non-uniformity (CCI) is shown in Figures C-l to C-12. The AE for

nectarines obtained from screen images and combinations of references of colors and color

selection did not correlate well with the CCI values as shown in Figures C-l to C-3. The same

results were reported for AE obtained from fruit trays as shown in Figures C-4 to C-6. This was

the same pattern for mangos in Figures C-7 to C-12.

The number of reference color or the number of selections of colors did not provide any

information in regard to a correlation between AE and CCI. The color change index or measure

of non-uniformity did not have a relationship with the panelist's performance and error made

when visually evaluating mangos and nectarines. It is possible that above a certain degree of

non-uniformity of colors panelists performance is reduced and becomes too complicated to refer

to color bars.









CHAPTER 5
CONCLUSIONS

Colors reflect important quality parameters such as maturity, defects and other color-

dependent attributes. Most agricultural materials e.g. fruits, vegetables, grains, meat, and

seafood have non-uniform shapes, surfaces and colors. It is important to quantify the color

attributes of these materials in order to measure their quality and to help with the record keeping

associated with the new globalization requirements.

The number of color blocks did not provide a clear measure of the degree of non-

uniformity in mangoes and nectarines. However, the number of color primitives associated with

color change index and number of neighbors does provide a better measure of their degree of

non-uniformity.

Quantitative color data can be correlated with human perception. The method developed

in this study can be used to quantify the perceptions of untrained panelists regarding non-

uniformly colored foods, with objective error measurements to optimize the method parameters.

It was observed that there may be an "optimum" number of reference colors for a given food. In

our study, 12 reference colors performed poorly compared to either 8 or 16 reference colors.

Since all of the 8 reference colors were present in the set of 12 reference colors, one may argue

that increasing the number from 8 to 12 "diluted" their effect. This needs to be tested in future

studies. It is more difficult to explain why 16 reference colors performed better than 12

references. Future studies may explore this dilemma.

It was clear that the more color selections, the less the error made by the panelists, given

the reference colors provided in this study. There was also statistically significant interaction

between the number of reference colors and the number of selections. It is possible that up to

certain level of reference colors panelists would perform more efficiently, and above that level it









would be too complicated for the panelists to refer to color selections and reference colors. This

issue needs to be elucidated in future studies.

In this study there was no correlation between the error performance of panelists and the

degree of non-uniformity provided by the number of primitives. The concept of the minimum

possible performance level was introduced, the best possible AE. This provided a more realistic

way to calculate the error made by sensory panels, given a number of reference colors and color

selections.

Panelists also evaluated the color of the same sample either by looking at its image, or at a

real fruit. This study found small but statistically significant differences in the error made by

panelists between these sources. It is interesting, but expected that the error made when looking

at the image was less, since the reference colors were developed from the images. Specific

studies in the future need to clarify if images can be substituted for the real food, for visual

evaluation purposes.

It is essential to keep identifying criteria to measure the visual evaluations of panelists and

their correlations with instrumental methods of color measurements, to provide a better

understanding to the human perception of non-uniform colors. The search for better methods to

quantify and correlate instrumental and human perception data in this area should continue.









APPENDIX A
COLOR ANALYSIS FOR ALL TRAYS


49i :. ^ij


Figure A-1. Fruit Tray booth 1 for image acquisition and sensory panel.
mlira.i ~ '*ILI 3


Figure A-2. Fruit Tray booth 2 for image acquisition and sensory panel.






















'I


Figure A-3. Fruit Tray booth 3 for image acquisition and sensory panel.


Figure A-4. Fruit Tray booth 4 for image acquisition and sensory panel.
































Figure A-5. Fruit Tray booth for image acquisition and sensory panel.


Figure A-6. Fruit Tray booth 6 for image acquisition and sensory panel.








-: -^ ^


Figure A-7. Fruit Tray booth 7 for image acquisition and sensory panel.













317






Figure A-8. Fruit Tray booth 8 for image acquisition and sensory panel.



























Figure A-9. Fruit Tray booth 9 for image acquisition and sensory panel.




















Figure A-10. Fruit Tray booth 10 for image acquisition and sensory panel.
E: l
"." ~ ~ ~ ~ r :" ji 1'
.: '. "ii
::::::9

;.!:., '0 .'r"t Tryboh1 o mg custo n esr a.:."































Figure A-11. Machine Vision set-up.


Figure A-12. Light box specifications.





























Figure A-13. Reference scales presented to panelists.

The main reference color bar was that with 16 colors. From that color bar, color blocks

with close/similar L*a*b or RGB values were merged to reduce the number of colors to choose

and create a color bar with 12 reference colors. The same procedure was used to create the color

reference scale with 8 references colors.


Table A-1. L*a*b values for reference color bar with 8 color.
Reference color L* a* b*
1 59.14 -5.61 58.53
2 32.97 56.6 43.38
3 39.89 53.77 34.18
4 52.32 21.89 47.08
5 60.95 2.83 55.43
6 41.77 62.53 46.01
7 71.07 17.04 60.64
8 75.28 7.85 64.73


01 02 03 04 05 06 07 08

M 11 M
8 References
01 02 03 04 05 06 07 08 09 10


11 12

M M 12 References
01 02 03 04 05 06 07 08 09 10


11 12 13 14 15 16 16 References

MEME










Table A-2. L*a*b values for reference color bar with 12 colors.
Reference color L* a* b*
1 59.14 -5.61 58.53
2 32.97 56.6 43.38
3 39.89 53.77 34.18
4 42.15 47.55 36.62
5 52.32 21.89 47.08
6 60.95 2.83 55.43
7 45.33 53.39 40.92
8 41.77 62.53 46.01
9 67.41 28.11 40.77
10 71.07 17.04 60.64
11 79.56 -1.8 74.03
12 75.28 7.85 64.73

Table. A-3 L*a*b values for reference color bar with 16 colors.
Reference color L* a* b*
1 50.01 13.55 50.72
2 59.14 -5.61 58.53
3 32.97 56.6 43.38
4 39.89 53.77 34.18
5 42.15 47.55 36.62
6 52.32 21.89 47.08
7 56.51 12.35 51.18
8 60.95 2.83 55.43
9 45.33 53.39 40.92
10 47.94 46.34 43.57
11 41.77 62.53 46.01
12 67.41 28.11 40.77
13 71.07 17.04 60.64
14 79.56 -1.8 74.03
15 75.28 7.85 64.73
16 90.35 -9.87 67.25









Figure A-14. Example ballot for screen image evaluation.
(Fruit Image Evaluation Form)

Sensory color evaluation form


Date


Panelist Age:


Male 0 Female 0


Booth number

Instructions:
1. Do not re-orient the samples, or modify their wrapping.
2. Evaluate the samples using the order given here.
3. From the screen, select only 2 colors that best represent the colors of the sample.
4. Estimate the percentage of these colors for the surface of the sample shown.
5. The sum of the 2 percentages must add to 100%


Sample number (541)


Color number Percent of
(1 to 8) total area


Sum=100%

Sample number (397)

Color number Percent of
(1 to 8) total area


Sum=100%









Figure A-15. Example ballot for fruit evaluation.


(Fruit Tray Evaluation Form)


Sensory color evaluation form


Date


Panelist Age:


Male 0 Female 0


Booth number

Instructions:
1. Do not re-orient the samples, or modify their wrapping.
2. Evaluate the samples using the order given here.
3. From the screen, select only 2 colors that best represent the colors of the sample.
4. Estimate the percentage of these colors for the surface of the sample shown.
5. The sum of the 2 percentages must add to 100%


Sample number (397)


Color number Percent of
(1 to 8) total area


Sum=100%

Sample number (541)

Color number Percent of
(1 to 8) total area


Sum=100%


Table A-4. Mango color primitives.
Booth CCI # Primitives # Neighbors
1 6.42 616 1190
2 9.21 613 1239
3 5.58 513 982
4 4.90 548 1012
5 6.96 593 1127
6 7.16 615 1190
7 6.76 609 1196
8 4.33 538 964
9 4.58 536 1016
10 11.29 738 1511










Table A-5. Nectarine color primitives.
Booth CCI # Primitives # Neighbors
1 17.51 915 1760
2 13.72 693 1366
3 13.13 581 1196
4 13.20 794 1521
5 11.32 626 1215
6 14.23 729 1491
7 17.75 856 1648
8 15.19 756 1488
9 11.49 739 1414
10 14.76 773 1566


























Figure A-16. Representation of color primitives and equivalent circles for mangos (left) and
nectarines (right) with a MV system.









APPENDIX B
PANELISTS PERFORMANCE FOR MANGOS AND NECTARINES

10. Treatment 1 = 8 reference color and 2 color selections

11. Treatment 2 = 8 reference colors and 4 color selections

12. Treatment 3 = 8 reference colors and 6 color selections

13. Treatment = 12 reference color and 2 color selections

14. Treatment 5 = 12 reference colors and 4 color selections

15. Treatment 6 = 12 reference colors and 6 color selections

16. Treatment 7 = 16 reference colors and 2 color selections

17. Treatment 8 = 16 reference colors and 4 color selections

18. Treatment 9 = 16 reference colors and 6 color selections


Table B-1. Summary performance for panelists evaluating both fruits for booth 1.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 1.34 9.15 4.79 12.97 8.38 2.92 11.39 9.56 15.75 6.05
2* 2.82 7.41 2.79 11.31 3.52 2.79 10.70 5.16 10.62 2.30
3* 0.20 7.61 2.72 10.09 2.38 0.20 9.49 4.04 10.52 5.41
4* 1.34 25.73 14.36 20.02 14.59 2.92 21.15 7.64 18.04 8.86
5* 0.20 13.54 9.16 11.07 1.26 0.20 13.53 7.79 12.84 3.01
6* 0.20 8.77 1.82 10.61 4.04 0.20 13.87 8.26 12.73 4.74
7* 1.34 13.27 11.20 12.66 1.42 2.92 15.20 10.29 14.93 4.89
8* 0.20 9.95 6.33 12.57 3.76 0.20 13.97 9.51 11.34 1.86
9* 0.20 14.13 10.59 10.57 4.92 0.20 13.91 5.91 6.40 6.40









Table B-2. Summary performance for panelists evaluating both fruits for booth 2.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 0.90 11.91 5.20 9.46 3.55 1.90 11.04 7.00 15.36 5.87
2* 2.56 6.36 3.27 10.02 1.88 1.38 9.15 5.23 10.02 4.12
3* 0.20 10.44 9.88 6.10 1.35 0.20 10.51 5.77 11.13 3.95
4* 0.91 14.93 7.46 21.29 16.93 1.90 15.75 6.93 20.84 7.87
5* 0.20 8.73 2.85 11.01 2.34 0.20 9.53 3.63 10.97 4.69
6* 0.19 6.09 3.49 10.23 4.23 1.90 8.78 5.48 11.73 5.00
7* 1.34 13.36 4.59 13.09 4.79 2.92 15.50 8.52 15.15 7.15
8* 0.20 9.19 1.31 11.63 4.17 0.20 8.60 8.60 13.50 6.62
9* 0.20 12.49 7.31 10.77 2.24 0.20 8.60 3.01 12.10 12.10

Table B-3. Summary performance for panelists evaluating both fruits for booth 3.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 1.90 9.15 4.72 7.69 3.14 2.40 10.72 6.26 10.85 1.46
2* 2.13 7.94 4.46 8.67 3.54 0.18 9.15 5.23 10.02 4.12
3* 0.20 5.39 2.81 5.73 2.10 0.20 10.59 5.09 8.34 4.48
4* 1.89 20.09 14.33 16.52 12.38 2.41 16.55 11.69 9.90 3.52
5* 0.20 14.67 8.71 10.68 8.71 0.20 12.31 7.82 12.16 4.68
6* 0.20 7.60 2.82 7.35 4.61 0.20 7.87 4.08 7.54 3.42
7* 1.90 9.79 5.82 8.58 3.40 2.41 11.48 6.38 15.47 7.19
8* 0.20 12.74 8.25 12.43 8.25 0.20 7.82 5.86 10.28 2.63
9* 0.20 7.79 3.97 7.38 3.66 0.20 9.46 8.39 9.69 9.69


Table B-4. Summary performance for panelists evaluating both fruits for booth 4.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 4.66 11.91 7.34 14.87 4.70 2.59 7.19 4.71 15.59 5.49
2* 5.93 14.46 6.65 13.98 4.70 2.58 11.44 3.71 13.31 3.15
3* 0.20 14.16 9.21 12.78 2.07 0.20 12.05 7.14 15.14 3.94
4* 4.66 28.17 11.16 14.58 10.72 2.59 19.67 8.85 23.81 9.87
5* 0.20 14.08 7.71 14.04 3.64 2.35 10.76 5.43 18.19 6.04
6* 0.20 10.45 5.21 13.18 1.82 0.20 11.58 5.65 11.03 6.09
7* 4.66 14.66 7.97 17.08 7.40 2.59 11.06 5.31 18.05 9.57
8* 0.20 13.44 6.36 16.89 6.36 0.20 12.74 7.01 18.41 2.38
9* 0.20 9.14 3.68 17.39 7.27 0.20 11.33 4.93 15.09 15.09









Table B-5. Summary performance for panelists evaluating both fruits for booth 5.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 2.65 10.23 5.71 9.92 2.64 1.93 11.98 6.53 19.10 5.68
2* 8.81 11.77 10.50 11.75 7.95 1.79 12.94 5.21 11.84 5.07
3* 0.20 6.44 2.54 11.61 6.57 0.20 9.26 4.48 12.30 4.49
4* 2.65 15.11 3.07 13.43 5.00 1.93 22.01 9.40 21.45 7.65
5* 0.20 11.93 5.84 17.36 8.56 1.72 12.30 6.52 15.76 5.79
6* 0.20 6.76 2.52 13.84 6.81 0.20 10.32 4.37 15.52 4.24
7* 2.65 15.25 9.75 18.36 7.43 1.93 12.07 2.42 17.39 7.39
8* 0.20 14.04 8.55 13.15 8.55 0.20 15.73 10.68 14.64 3.05
9* 0.20 9.71 4.51 14.16 5.20 0.20 13.78 4.57 16.04 16.04


Table B-6. Summary performance for panelists evaluating both fruits for booth 6.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 1.27 13.24 6.92 16.02 9.33 2.67 13.15 7.69 19.44 10.38
2* 6.85 11.48 6.36 12.39 4.91 2.99 11.07 5.56 15.20 8.04
3* 0.20 13.13 6.10 11.55 3.20 0.20 8.48 2.84 10.01 3.32
4* 1.27 20.59 5.85 25.95 12.12 2.67 16.86 4.54 21.79 7.02
5* 0.20 7.17 2.76 8.59 2.03 0.20 9.50 3.97 11.22 4.53
6* 0.20 7.28 1.76 7.30 4.76 0.20 11.58 5.65 11.03 6.09
7* 1.27 12.37 8.48 10.63 2.21 2.67 12.07 2.42 17.39 7.39
8* 0.20 9.02 3.37 8.57 3.37 0.20 12.79 5.06 12.15 3.88
9* 0.20 8.29 2.40 9.63 3.65 0.20 10.84 6.06 10.17 10.17


Table B-7. Summary performance for panelists evaluating both fruits for booth 7.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 8.67 13.75 8.90 17.46 9.89 2.19 12.66 9.72 11.80 2.94
2* 11.59 15.28 5.80 15.94 9.54 0.66 13.27 6.68 9.00 2.91
3* 0.20 12.70 3.43 13.13 2.18 0.20 5.19 1.97 8.21 0.92
4* 8.67 23.78 7.06 22.47 8.77 2.19 17.21 17.44 15.84 16.89
5* 0.20 16.34 8.74 18.53 5.54 0.20 13.03 11.79 12.28 10.80
6* 0.20 14.43 7.37 18.62 7.86 0.20 11.98 9.70 11.88 6.16
7* 8.67 13.29 5.03 16.91 7.01 2.19 11.54 5.54 11.78 2.80
8* 0.20 18.12 8.37 16.39 8.37 0.20 8.02 4.94 11.22 5.19
9* 0.20 11.93 5.78 16.85 4.13 0.20 10.48 12.83 11.30 11.30









Table B-8. Summary performance for panelists evaluating both fruits for booth 8.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 1.25 11.28 9.21 15.52 5.38 0.83 13.25 11.45 13.42 4.07
2* 3.69 7.14 4.47 9.59 2.15 0.69 9.47 4.16 12.05 2.69
3* 0.20 7.68 3.08 9.73 3.76 0.20 10.39 3.14 10.90 3.80
4* 1.25 21.03 13.05 24.14 16.85 0.83 23.58 15.44 11.13 12.01
5* 0.20 9.49 5.06 11.96 2.62 0.20 14.69 10.29 12.15 3.50
6* 0.20 11.31 7.19 8.95 3.14 0.20 12.44 7.46 10.87 3.56
7* 1.25 16.38 6.68 20.19 7.65 0.83 9.87 4.58 13.43 7.18
8* 0.20 4.86 2.30 13.65 2.30 0.20 11.92 5.69 13.10 3.36
9* 0.20 8.69 3.74 14.58 4.33 0.20 11.44 8.81 13.76 13.76


Table B-9. Summary performance for panelists evaluating both fruits for booth 9.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 4.65 8.95 6.12 12.86 3.66 0.61 8.77 7.13 12.83 3.58
2* 5.45 10.38 4.01 14.28 7.35 0.59 10.69 4.37 13.70 4.18
3* 0.20 8.17 2.08 11.40 3.30 0.20 11.47 3.66 10.53 2.95
4* 4.65 24.19 10.96 19.32 13.82 0.61 18.88 11.63 35.88 15.71
5* 0.20 13.35 4.51 15.40 4.91 0.20 13.01 4.73 13.59 4.26
6* 0.20 12.02 3.60 11.06 2.69 0.20 13.01 4.73 13.59 4.26
7* 4.65 13.07 5.34 21.78 8.71 0.61 10.87 7.18 11.36 4.55
8* 0.20 12.68 6.82 13.33 6.82 0.20 11.08 7.83 11.88 4.45
9* 0.20 11.07 3.57 14.15 4.07 0.20 10.49 5.53 16.38 16.38


Table B-10. Summary performance for panelists evaluating both fruits for booth 10.
Nectarine Mango
Best Screen Tray Best Screen Tray
Case AE AE Stdev AE Stdev AE AE Stdev AE Stdev
1* 7.08 9.23 6.05 17.59 4.57 2.80 7.27 4.89 11.59 3.74
2* 7.22 13.13 5.15 15.19 4.93 2.18 10.13 9.04 12.65 5.37
3* 0.20 12.72 3.53 17.77 5.69 0.20 9.00 4.78 12.68 6.86
4* 7.10 27.25 10.32 26.93 10.90 2.80 14.39 8.22 17.80 13.21
5* 0.20 12.79 3.99 15.93 2.94 0.20 15.16 6.04 11.19 3.60
6* 0.20 9.58 2.65 15.96 5.15 0.20 9.93 4.79 10.14 3.01
7* 7.08 18.04 5.65 21.21 7.77 2.80 12.83 7.72 16.43 5.28
8* 0.20 10.86 4.36 14.97 4.36 0.20 5.99 1.54 10.66 2.71
9* 0.20 9.92 5.22 15.42 3.01 0.20 7.18 2.59 10.91 10.91









APPENDIX C
AE VS CCI FOR ALL COMBINATIONS


Nectarine, abs.DE, screen
40
8 ref, 2 8 ref, 4 8 ref, 6

30

LU
S20


10


0
11 12 13 14 15 16 17 18 19
CCI
Figure C-1. Absolute A E for nectarine for screen image and 8 references.


Nectarine, abs.DE, screen

40
12 ref, 2 12 ref, 4 12 ref 6

30


20
*




10


0
11 12 13 14 15 16 17 18 19
CCI

Figure C-2. Absolute A E for nectarine for screen image and 12 references.











Nectarine, abs.DE, screen


4U
S16ref, 2 16 ref, 4 16 ref, 6

30

LU
(o



10



20
11 12 13 14 15 16 17 18 1
CCI

Figure C-3. Absolute A E for nectarine for screen image and 16 references.




Nectarine, abs.DE, fruit


8ref, 2 8ref, 4 A 8ref, 6






t,
A A


A.


Figure C-4. Absolute A E for nectarine for tray and 8 references.


11 12 13 14 15 16 17 18 19
CCI


* *










Nectarine, abs.DE, fruit

40
12ef,2 12ref, 4 12 ref, 6

30


20-


10-
0


11 12 13 14 15 16 17 18 19
CCI
Figure C-5. Absolute A E for nectarine for tray and 12 references.


Nectarine, abs.DE, fruit

40
16 ref, 2 16 ref, 4 16 ref, 6

30

LU
S20


10


0------------------
11 12 13 14 15 16 17 18 19
CCI
Figure C-6. Absolute A E for nectarine for tray and 16 references.











Mango, abs.DE, screen


40



30


10



0


S8ref, 2 8ref, 4 A 8ref, 6












3 4 5 6 7 8 9 10 11 1

3 4 5 6 7 8 9 10 11 1


CCI

Figure C-7. Absolute A E for mango for screen image and 8 references.


Mango, abs.DE, screen


40


30

LU
-
20-


10


0


* 12ef,2 12 ref, 4 A 12 ref, 6


3 4 5 6 7 8 9 10 11


CCI

Figure C-8. Absolute A E for mango for screen image and 12 references.


r










Mango, abs.DE, screen


10 -


0
3 4 5 6 7 8 9 10 11 1
CCI
Figure C-9. Absolute A E for mango for screen image and 16 references.


Mango, abs.DE, fruit


40


30


* 8ref, 2 8 ref, 4


S8 ref, 6


I A



3 4 5 6 7 8 9 10 11 1
CCI


Figure C-10. Absolute A E for mango for tray 8 references.


* 16 ref, 4 16 ref, 6


* 16 ref, 2











Mango, abs.DE, fruit


S12ef, 2 12ref,4 12 ref, 6

30







0


3 4 5 6 7 8 9 10 11 1
CCI

Figure C-11. Absolute A E for mango for tray and 12 references.


Mango, abs.DE, fruit


40


S16ref, 2 16 ref, 4 16 ref, 6






----------r-------- ^
2 S.^


u I I I I
3 4 5 6 7 8 9 10 11
CCI

Figure C-12. Absolute A E for mango for tray and 16 references.









APPENDIX D
DELTA E VALUES FOR DIFFERENT CASES


Nectrarine, abs.DE, screen
40
8 ref, 2
8 ref, 4
30
30 -8 ref, 6

LU
= 20
(D

10-


0
0--------------------


0 1 2 3 4 5 6 7 8 9 1011
trays
Figure D-1. Absolute A E for nectarine for screen image and 8 references.


Nectarine, Abs.DE, screen
40
S12 ref, 2 12 ref, 4 12 ref 6

30


20



0 \/ v A
10


0
0 1 2 3 4 5 6 7 8 9 10 11
Trays



Figure D-2. Absolute A E for nectarine for screen image and 12 references.











Nectarine, abs.DE, screen


16 ref, 2 -16 ref, 4 -16 ref, 6


0 1 2 3 4 5 6 7 8 9 10 11
Trays

Figure D-3. Absolute A E for nectarine for screen image and 16 references.


Nectarine, abs.DE, real fruit

40
8 ref, 2 8 ref, 4 8 ref, 6

30

LU

S20
10-


0 1 2 3 4 5 6 7 8
trays


Figure D-4. Absolute A E for nectarine for tray and 8 references.


9 10 11










Nectarine, abs.DE, real fruit


-12 ref, 2 -12 ref, 4 -12 ref, 6


0 1 2 3 4 5 6 7 8 9 10 11
trays
Figure D-5. Absolute A E for nectarine for tray and 12 references.


Nectarine, abs.DE, real fruit


-16 ref, 2 16 ref, 4 16 ref, 6


0 1 2 3 4 5 6 7 8 9 10 11
trays
Figure D-6. Absolute A E for nectarine for tray and 16 references.











Mango, abs.DE, screen

40
8 ref, 2 8 ref, 4 8 ref, 6

30

LU
= 20


10


0
0 1 2 3 4 5 6 7 8 9 10 11
trays

Figure D-7. Absolute A E for mango for screen image and 8 references.



Mango, abs.DE, screen

40
S12 ref, 2 -12 ref, 4 -12 ref, 6

30


I 20


10


0
0 1 2 3 4 5 6 7 8 9 10 11
trays

Figure D-8. Absolute A E for mango for screen image and 12 references.













40


30

-
- 20-

1
10


Mango, abs.DE, screen


-16 ref, 2 -16 ref, 4 -16 ref, 6


0 1 2 3 4 5 6 7 8 9 10 1
trays

Figure D-9. Absolute A E for mango for screen image and 16 references.


Mango, abs.DE, real fruit


8 ref, 2 8 ref, 4 8 ref, 6


0 1 2 3 4 5 6 7 8 9 10 11
trays

Figure D-10. Absolute A E for mango for tray 8 references.










Mango, abs.DE, real fruit


-12 ref, 2 -12 ref, 4 -12 ref, 6 A


0 1 2 3 4 5 6 7 8 9 10 11
trays
Figure D-11. Absolute A E for mango for tray and 12 references.


Mango, abs.DE, real fruit


16 ref, 2 16 ref, 4 16 ref, 6


0 1 2 3 4 5 6 7 8 9 10 11
trays
Figure D-12. Absolute A E for mango for tray and 16 references.










APPENDIX E
SOURCE CODES FOR SAS PROGRAMS

Method 1.

PROC PRINT DATA=FILE;
RUN;
proc sort data= File; by Object;
proc anova data=file; by Object;
class reference colors selections source panelist booth;
model Delta E = reference colorslselectionslsource;
means reference colors selections source
reference colorslselectionslsource/duncan;
run;



Method 2.


proc sort data= File; by object;

proc mixed DATA=File; by object;
class reference colors selections source booth panelist;
model DiffDE = reference colorslselectionslsource;
random panelist booth;
lsmeans selectionslreference colors source /pdiff;
run;





*** The model statement was interchangeable to Diff AE or AE to statistically analyze both
dependent variables.

Table E-1. Mixed mode summary absolute AE for mangos.
Source Num. Den. F Value Pr >F
DF* DF*
Reference colors 2 1343 23.21 <.0001
Selections 2 1343 48.73 <.0001
Presentation 1 1343 23.32 <.0001
Reference colors selections 4 1343 10.76 < .0001
Reference colors* presentation 2 1343 2.09 0.124
Selections*presentation 2 1343 1.51 0.222
Reference 4 1343 0.36 0.835
colors*selections*presentation
Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of
freedom.












Table E-2. Mixed mode summary difference AE for mangos.
Source Num. Den. F Value Pr >F
DF* DF*
Reference colors 2 1413 28.57 <.0001
Selections 2 1413 14.90 < .0001
Presentation 1 1413 23.17 < .0001
Reference colors selections 4 1413 9.71 < .0001
Reference colors* presentation 2 1413 2.08 0.126
Selections*presentation 2 1413 1.50 0.224
Reference 4 1413 0.36 0.837
colors*selections*presentation
Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of
freedom.

Table E-3. Mixed Mode summary absolute AE for nectarines.
Source Num. Den. F Value Pr >F
DF* DF*
Reference colors 2 1343 32.84 <.0001
Selections 2 1343 88.95 <.0001
Presentation 1 1343 17.70 < .0001
Reference colors selections 4 1343 22.37 < .0001
Reference colors* presentation 2 1343 1.79 0.167
Selections*presentation 2 1343 1.43 0.241
Reference 4 1343 1.38 0.237
colors*selections*presentation
Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of
freedom.

Table E-4. Mixed Mode summary difference in AE for nectarines.
Source Num. Den. F Value Pr >F
DF* DF*
Reference colors 2 1413 77.58 <.0001
Selections 2 1413 21.20 < .0001
Presentation 1 1413 16.96 < .0001
Reference colors selections 4 1413 25.41 < .0001
Reference colors* presentation 2 1413 1.72 0.126
Selections*presentation 2 1413 1.37 0.224
Reference 4 1413 1.33 0.837
colors*selections*presentation
Num DF refers to numerator degrees of freedom. Den DF refers to denominator degrees of
freedom.













The Mixed Procedure
Type 3 Tests of Fixed Effects


Selections*Source
Refere*Select*Source
Least
Reference
Source Colors


Num Den
DF DF

2 1343
4 1343
Squares Means


F Value Pr > F

1.51 0.2219
0.36 0.8353


Selections Estimate
2 15.2401
4 11.9144
6 11.2349


Least Squares Means
Reference
Source Colors


Differences of Least Squares Means
Standard
Selections Selections Estimate Error
2 4 3.3258 0.4342
2 6 4.0053 0.4342
4 6 0.6795 0.4342


Standard
Error
0.5072
0.5072
0.5072


Selections Pr > Itl
2 <.0001
4 <.0001
6 <.0001


t Value
7.66
9.22
1.56


Figure E-1. Absolute AE means for selection of color for mangos.


The Mixed Procedure
Type 3 Tests of Fixed Effects


Effect
Refere*Select*Sourc


Selections Es
2 1
4 1
6 1
Different


Selections Selection
2 4
2 6
4 6


Num Den
DF DF F Value
e 4 1413 0.36
Least Squares Means
Standard
timate Error DF
3.1554 0.4968 1413
1.1637 0.4968 1413
1.0349 0.4968 1413
ces of Least Squares Means
Standard
s Estimate Error
1.9917 0.4356
2.1205 0.4356
0.1288 0.4356


Pr > F
0.8369


t Value
26.48
22.47
22.21


Pr > Itl
<.0001
<.0001
<.0001


t Value
4.57
4.87
0.30


Figure E-2. Difference AE Means for selection of color for mangos.


Effect


Effect
Selections
Selections
Selections


Effect
Selections
Selections
Selections


Effect
Selections
Selections
Selections


t Value
30.05
23.49
22.15


Pr > Itl
<.0001
<.0001
0.1179


Effect
Selections
Selections
Selections


Effect
Selections
Selections
Selections


Pr > Itl
<.0001
<.0001
0.7675

















Effect

Reference Colors
Reference Colors
Reference Colors


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error


11.4803
14.3974
12.5116


0.5072
0.5072
0.5072


DF t Value Pr > Itl


22.64
28.39
24.67


<.0001
<.0001


Differences of Least Squares Means


Effect

Reference Colors
Reference Colors
Reference Colors


Selections


Estimate


2.9171
1.0314
1.8858


Standard
Error

0.4342
0.4342
0.4342


DF t Value Pr > Itl


<.0001
0.0177
<.0001


Figure E-3. Absolute AE means for reference colors for mangos.


Effect


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error


DF t Value Pr > Itl


Reference Colors
Reference Colors
Reference Colors


Effect
Reference Colors
Reference Colors
Reference Colors


8 10.1910
12 13.4795
16 11.6834
Differences of Least

Selections Estimate
8 12 -3.2885
8 16 -1.4924
12 16 1.7961


0.4968 1343
0.4968 1343
0.4968 1343
Squares Means
Standard
Error DF
0.4356 13
0.4356 13
0.4356 13


Figure E-4. Difference AE means for reference colors for mangos.


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error DF t
F 13.6525 0.4752 1343
S 11.9404 0.4752 1343
Differences of Least Squares Means
Standard
Selections Estimate Error DF
F S 1.7122 0.3546 1343


Value
28.73
25.13


Pr > Itl
<.0001
<.0001


t Value Pr > Itl
4.83 <.0001


Figure E-5. Absolute AE means for presentation for mangos.


<.0001


20.52
27.14
23.52


t Value
43 -7.55
43 -3.43
43 4.12


<.0001
<.0001
<.0001


Pr > Itl
<.0001
0.0006
<.0001


Effect
Source
Source


Effect
Source













The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error DF


t Value Pr > Itl


12.6407 0.4638 1343 27.25
10.9286 0.4638 1343 23.56

Differences of Least Squares Means


Selections

F S


Estimate

1.7121


Standard
Error


<.0001
<.0001


DF t Value Pr > Itl


0.3557 1343 4.81


<.0001


Figure E-6. Difference AE means for presentation for mangos.


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means


Reference
Effect Source Colors


Selections Estimate


2
4
6
Least Squares Means
Reference
Source Colors


Selections 2
Selections 4
Selections 6
Differences of Least Squares Means
Standard
Selections Selections Estimate Error


4.0855
5.4208
1.3353


16.3154
12.2299
10.8946


Standard
Error


DF t Value


0.7332
0.7332
0.7332


22.25
16.68
14.86


Selections Pr > Itl


<.0001
<.0001
<.0001


DF t Value Pr > Itl


0.4235
0.4235
0.4235


9.65
12.80
3.15


<.0001
<.0001
0.0016


Figure E-7. Absolute AE means for selections of colors for nectarines.


Effect

Source
Source


Effect

Source


Selections
Selections
Selections


Effect


Effect

Selections
Selections
Selections

















Effect
Selections
Selections
Selections


Selections
2
4
6
Dif


Selections Sele
2 4
2 6
4 6


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Estimate Error DF
12.8803 0.4604 1413
10.2490 0.4604 1413
10.6946 0.4604 1413
ferences of Least Squares Means
Standard
actions Estimate Error
2.6313 0.4326
2.1857 0.4326
-0.4456 0.4326


Figure E-8. Difference AE means for selections of colors for nectarines.


Effect
Reference Colors
Reference Colors
Reference Colors


Effect
Reference Colors
Reference Colors
Reference Colors


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error DF
8 11.4792 0.7332 1343
12 14.9077 0.7332 1343
16 13.0530 0.7332 1343
Differences of Least Squares Means
Standard
Selections Estimate Error DF
8 12 -3.4285 0.4235 1343
8 16 -1.5737 0.4345 1343
12 16 1.8547 0.4345 1343


Figure E-9. Absolute AE means for reference colors for nectarines.


Effect
Reference Colors
Reference Colors
Reference Colors


Effect
Reference Colors
Reference Colors
Reference Colors


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Selections Estimate Error DF
8 8.3653 0.4604 1343
12 13.6839 0.4604 1343
16 11.7746 0.4604 1343
Differences of Least Squares Means
Standard
Selections Estimate Error DF
8 12 -5.3186 0.4326 1343
8 16 -3.4092 0.4326 1343
12 16 1.9093 0.4326 1343


Figure E-10. Difference AE means for reference colors for nectarines.


Effect
Selections
Selections
Selections


Pr > Itl
<.0001
<.0001
<.0001


t Value
27.97
22.26
23.23


DF t
1413
1413
1413


Value
6.08
5.05
-1.03


Pr > Itl
<.0001
<.0001
0.3031


t Value
15.66
20.33
17.80


t Value
-8.10
-3.72
4.38


Pr > Itl
<.0001
<.0001
<.0001


Pr > Itl
<.0001
0.0002
<.0001


t Value
18.17
29.72
25.57


t Value
-12.29
-7.88
4.41


Pr > Itl
<.0001
<.0001
<.0001


Pr > Itl
<.0001
<.0001
<.0001


t












The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Estimate Error DF t Value

13.8739 0.7125 1343 19.47
12.4193 0.7125 1343 17.43

Differences of Least Squares Means

Standard
Estimate Error DF t Value

1.4546 0.3458 1343 4.21


Figure E- 1. Absolute AE means for presentation for nectarines.


The Mixed Procedure
Type 3 Tests of Fixed Effects
Least Squares Means
Standard
Estimate Error DF t Value

12.0020 0.4252 1343 28.23
10.5473 0.4252 1343 24.81

Differences of Least Squares Means

Standard
Estimate Error DF t Value

1.4547 0.3532 1343 4.12


Figure E-12. Difference AE means for presentation for nectarines.


Effect

Source
Source


Selections

F
S


Effect

Source


Selections

F S


Pr > Itl

<.0001
<.0001





Pr > Itl

<.0001


Effect

Source
Source


Selections

F
S


Effect

Source


Selections

F S


Pr > Itl

<.0001
<.0001





Pr > Itl

<.0001









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BIOGRAPHICAL SKETCH

Jose Aparicio was born in San Pedro Sula, Honduras. He started college in Honduras and

transferred to the University of Florida in 2003 where he obtained his B.S. in dairy industry. In

2005 he gained admission to the University of Florida graduate school to work on his M.S. in the

food science program under Dr. Murat Balaban's supervision. He completed his degree in Fall

2007.





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1 VISUAL QUANTIFICATION OF NON-HO MOGENEOUS COLORS IN FOODS By JOSE ALEJANDRO APARICIO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007

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2 2007 Jose Alejandro Aparicio

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3 To Caroline Elizabeth Fisher for your never ending support and encouragement throughout this journey, and who made this milestone possible

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4 ACKNOWLEDGMENTS I am very grateful to my major advisor, Dr. Murat O. Balaban, for his guidance and support. My appreciation also to the members of my supervisory committee, Dr. Charles Sims and Dr. Allen Wysocki, for their mentoring, all pa rticipants in my surv eys for their input and open participation, and my lab mates for their support. I thank my fa mily for their loyal encouragement, which always gave me strength to complete my study.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................9 ABSTRACT...................................................................................................................................13 CHAP TER 1 INTRODUCTION..................................................................................................................15 2 LITERATURE REVIEW.......................................................................................................17 Color of Foods and Agricultural Materials............................................................................. 17 Instrumental Color Measurement in Agricultural Food Products.......................................... 17 Computer Vision or Ma chine Vision System ......................................................................... 18 Bakery Products......................................................................................................................20 Red Meat and Seafood............................................................................................................20 Vegetables...............................................................................................................................23 Fruits......................................................................................................................... ..............23 Prepared Consumer Foods...................................................................................................... 24 Food Container Inspection......................................................................................................24 Grains......................................................................................................................................25 Other Applications..................................................................................................................25 Visual Texture Analysis.........................................................................................................26 Visual Texture Applicat ions in Agriculture ........................................................................... 27 Correlation between Image and Visual Color Analysis......................................................... 27 Preliminary E xperim ents........................................................................................................ 28 Objectives of the Study...........................................................................................................30 3 MATERIALS AND METHODS........................................................................................... 31 Mangos and Nectarines...........................................................................................................31 Image Acquisition.............................................................................................................. .....32 Image Analysis................................................................................................................. ......32 Experimental Design............................................................................................................ ..33 Method of Selection of the Reference Color Bars.................................................................. 34 Sensory Evaluations................................................................................................................35 Determination of Color Uniformity of Fruit........................................................................... 36 Average Color:................................................................................................................36 Color Blocks....................................................................................................................37 Color Pr imitiv es............................................................................................................... 37 Calculation of Best Possible E .............................................................................................39

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6 Statistical Analysis........................................................................................................... .......39 4 RESULTS & DISCUSSION.................................................................................................. 41 MV Color Results of Fruits.................................................................................................... 41 Non-Uniformity Analysis of Fruits........................................................................................ 42 Best Possible E .....................................................................................................................44 Sensory Panel Results.............................................................................................................48 Statistical Analysis........................................................................................................... .......50 Mangos............................................................................................................................51 Nectarines........................................................................................................................56 E vs. CCI...................................................................................................................... ........61 5 CONCLUSIONS.................................................................................................................... 62 APPENDIX A COLOR ANALYSIS FOR ALL TRAYS.................................................................................64 B PANELISTS PERFORMANCE FOR MANGOS AND NECTARINES................................. 75 C E VS CCI FOR ALL COMBINATIONS................................................................................ 79 D DELTA E VALUES FOR DIFFERENT CASES.................................................................... 85 E SOURCE CODES FOR SAS PROGRAMS.............................................................................. 91 LIST OF REFERENCES...............................................................................................................98 BIOGRAPHICAL SKETCH ....................................................................................................105

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7 LIST OF TABLES Table page 3-1 Nikon D200 settings..........................................................................................................32 3-2 Factorial-Level combinations............................................................................................ 34 4-1 MV color analysis for mangos........................................................................................... 41 4-2 MV color analysis for nectarines....................................................................................... 41 4-3 Best possible selections and minimum E value possible for 8 references and 2 selections for m angos.........................................................................................................45 4-4 Best possible selections and minimum E value possible for 8 references and 2 selections for nectarines .....................................................................................................45 4-5 Best possible selections and minimum E value possible for 12 references and 2 selections for m angos.........................................................................................................45 4-6 Best possible selections and minimum E value possible for 12 references and 2 selections for nectarines .....................................................................................................46 4-7 Best possible selections and minimum E value possible for 16 references and 2 selections for m angos.........................................................................................................46 4-8 Best possible selections and minimum E value possible for 16 references and 2 selections for nectarines .....................................................................................................46 4-9 Best possible selections and minimum E value possible for 8 references and 4 selections for m angos.........................................................................................................47 4-10 Best possible selections and minimum E value possible for 8 references and 4 selections for nectarines .....................................................................................................47 4-11 Best possible selections and minimum E value possible for 12 references and 4 selections for m angos.........................................................................................................47 4-12 Best possible selections and minimum E value possible for 12 references and 4 selections for nectarines .....................................................................................................48 4-13 Summary performance for paneli sts evaluating m angos for booth 1................................ 49 4-14 Summary performance for panelist s evaluating nectarines for booth 1 ............................ 50 4-15 ANOVA summary absolute E for m angos......................................................................51

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8 4-16 ANOVA summary difference E for m angos................................................................... 51 4-17 ANOVA summary absolute E for nectarines .................................................................. 56 4-18 ANOVA summary difference in E for nectarines ...........................................................57 A-1 L*a*b values for referen ce color bar with 8 color ............................................................. 70 A-2 L*a*b values for referen ce color bar with 12 colors ......................................................... 71 A-3 L*a*b values for referen ce color bar with 16 colors ......................................................... 71 A-4 Mango colo r prim itives ..................................................................................................... .73 A-5 Nectarine color primitives................................................................................................. .74 B-1 Summary performance for panelists evaluating both fruits for booth 1 ............................75 B-2 Summary performance for panelist s evaluating both fruits for booth 2 ............................ 76 B-3 Summary performance for panelists evaluating both fruits for booth 3 ............................76 B-4 Summary performance for panelist s evaluating both fruits for booth 4 ............................ 76 B-5 Summary performance for panelist s evaluating both fruits for booth 5 ............................ 77 B-6 Summary performance for panelist s evaluating both fruits for booth 6 ............................ 77 B-7 Summary performance for panelist s evaluating both fruits for booth 7 ............................ 77 B-8 Summary performance for panelist s evaluating both fruits for booth 8 ............................ 78 B-9 Summary performance for panelist s evaluating both fruits for booth 9 ............................ 78 B-10 Summary performance for panelist s evaluating both fruits for booth 10 .......................... 78 E-1 Mixed mode summary absolute E for m angos................................................................91 E-2 Mixed mode summary difference E for m angos............................................................. 92 E-3 Mixed Mode summary absolute E for nectarines ............................................................ 92 E-4 Mixed Mode summary difference in E for nectarines ..................................................... 92

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9 LIST OF FIGURES Figure page 3-1 Example of mango and nectarine on aluminum tray......................................................... 31 3-2 Example of reference colo r bar with 8 colors added to fruit im ages presented to the panelists..............................................................................................................................35 4-1 Correlation between number of primitives and color change index (CCI)........................ 43 4-2 Correlation between number of nei ghbors and color change index (CCI) ........................ 43 4-3 Correlation between number of neighbors and n umber of primitives............................... 44 4-4 Comparison of E values for 8, 12, and 16 reference colors, 2 selections ........................ 49 4-4 Absolute E m eans difference of selections of colors using mangos............................... 52 4-5 Difference in E m eans difference of selecti ons of colors using mangos......................... 52 4-6 Absolute E m eans for reference colors for mangos......................................................... 53 4-7 Difference in E m eans for reference colors for mangos.................................................. 53 4-8 Absolute E m eans for interaction between the nu mber of reference colors and the number of selections.......................................................................................................... 54 4-9 Difference in E m eans for interaction between th e number of reference colors and the number of selections.................................................................................................... 55 4-10 Absolute E m eans for presentation for mangos............................................................... 55 4-11 Difference E m eans for presen tation for mangos............................................................ 56 4-12 Absolute E m eans for reference colors for nectarines..................................................... 57 4-13 Difference E m eans for reference colors for nectarines.................................................. 58 4-14 Absolute E m eans for selection of colors for nectarine................................................... 58 4-15 Difference E m eans for selections of colors for nectarines............................................. 59 4-16 Difference E m eans for selections of colors for nectarines............................................. 59 4-17 Difference E m eans for selections of colors for nectarines............................................. 60 4-18 Absolute E m eans for presentation for nectarines........................................................... 60

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10 4-19 Difference E m eans for presenta tion for nectarines........................................................61 A-1 Fruit tray booth 1 for image acquisition and sensory panel............................................... 64 A-2 Fruit tray booth 2 for image acquisition and sensory panel............................................... 64 A-3 Fruit tray booth 3 for image acquisition and sensory panel............................................... 65 A-4 Fruit tray booth 4 for image acquisition and sensory panel............................................... 65 A-5 Fruit tray booth for imag e acquisition and sensory panel .................................................66 A-6 Fruit tray booth 6 for image acquisition and sensory panel............................................... 66 A-7 Fruit tray booth 7 for image acquisition and sensory panel............................................... 67 A-8 Fruit tray booth 8 for image acquisition and sensory panel............................................... 67 A-9 Fruit tray booth 9 for image acquisition and sensory panel............................................... 68 A-10 Fruit tray booth 10 for imag e acquisition and sensory panel .............................................68 A-11 Machine Vision set-up.................................................................................................... ...69 A-12 Light box specifications.....................................................................................................69 A-13 Reference scales presented to panelists............................................................................. 70 A-14 Example ballot for sc reen im age evaluation...................................................................... 72 A-15 Example ballot for fruit evaluation.................................................................................... 73 A-16 Representation of color primitives and equivalent circles for m angos (left) and nectarines (right) with a MV system.................................................................................. 74 C-1 Absolute E for nectarine for screen im age and 8 references.......................................... 79 C-2 Absolute E for nectarine for screen im age and 12 references........................................ 79 C-3 Absolute E for nectarine for screen im age and 16 references........................................ 80 C-4 Absolute E for nectarine for tray and 8 references ......................................................... 80 C-5 Absolute E for nectarine for tray and 12 references ....................................................... 81 C-6 Absolute E for nectarine for tray and 16 references ....................................................... 81 C-7 Absolute E for m ango for screen image and 8 references.............................................. 82

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11 C-8 Absolute E for m ango for screen image and 12 references............................................82 C-9 Absolute E for m ango for screen image and 16 references............................................83 C-10 Absolute E for m ango for tray 8 references...................................................................83 C-11 Absolute E for m ango for tray and 12 references...........................................................84 C-12 Absolute E for m ango for tray and 16 references...........................................................84 D-1 Absolute E for nectarine for screen im age and 8 references.......................................... 85 D-2 Absolute E for nectarine for screen im age and 12 references........................................ 85 D-3 Absolute E for nectarine for screen im age and 16 references........................................ 86 D-4 Absolute E for nectarine for tray and 8 references ......................................................... 86 D-5 Absolute E for nectarine for tray and 12 references ....................................................... 87 D-6 Absolute E for nectarine for tray and 16 references ....................................................... 87 D-7 Absolute E for m ango for screen image and 8 references.............................................. 88 D-8 Absolute E for m ango for screen image and 12 references............................................88 D-9 Absolute E for m ango for screen image and 16 references............................................89 D-10 Absolute E for m ango for tray 8 references...................................................................89 D-11 Absolute E for m ango for tray and 12 references...........................................................90 D-12 Absolute E for m ango for tray and 16 references...........................................................90 E-1 Absolute E m eans for selection of color for mangos...................................................... 93 E-2 Difference E Means for selection of color for m angos................................................... 93 E-3 Absolute E m eans for reference colors for mangos......................................................... 94 E-4 Difference E m eans for reference colors for mangos...................................................... 94 E-5 Absolute E m eans for presentation for mangos............................................................... 94 E-6 Difference E m eans for presen tation for mangos............................................................ 95 E-7 Absolute E m eans for selections of colors for nectarines................................................95

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12 E-8 Difference E m eans for selections of colors for nectarines............................................. 96 E-9 Absolute E m eans for reference colors for nectarines..................................................... 96 E-11 Absolute E m eans for presentation for nectarines........................................................... 97 E-12 Difference E m eans for presenta tion for nectarines........................................................97

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13 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science VISUAL QUANTIFICATION OF NON-HO MOGENEOUS COLORS IN FOODS By Jose Alejandro Aparicio December 2007 Chair: Murat Balaban Major: Food Science and Human Nutrition Color is an important quality attribute for nearly every agricultura l product. Consumers may perceive color as an indicator of freshness and wholesomeness, and color may affect their final decision to accept/reject food. A better und erstanding of human perception of colors in food would be beneficial to increase the consistency and quality of food products. The quantification of color is becomi ng more important due to an emphasis on international trade and implementation of Hazard Analysis Critical Cont rol Points (HACCP) requiring record keeping. Thus, it is important to provide the agricultural industry with me thods to quantify and correlate sensory and instrumental evaluations of foods. Machine vision imitates human visual percepti on by using a camera and a computer with software capable to generate precise, consiste nt, and cost-effective color measurement. The comparison and correlation of instrumental and visual color analysis has been performed in many uniformly colored agricultural products su ch as meat, bakery and seafood. Generally, there is a close relationship between sensory a nd instrumental color analysis of homogenous foods. However, comparison and correlation of non-homogeneous color measurements in foods is more challenging and has not been thoroughly studied.

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14 Machine vision was used to quantify the degree of color uniformity of mangos and nectarines using the number of color blocks and color primitives. The use of color primitives provided a more accurate method to measure colo r uniformity of mangos and nectarines. Three reference color bars (8, 12 and 16 colors) were created from color analysis of the fruits. A sensory panel (n=80) visually evaluated mangos and nectarines in two presentations: screen images captured by machine vision and fruits placed in trays. Panelists attempted to quantify color by selecting (2, 4 or 6 colors) from the refe rence color bars and compare the colors in the reference bars with those of the fruit surfaces. There were a total of 9 sessions at different days using different panelists. Sensory and machine vision evaluations were compared using the absolute E value. E measures total color change by accounting for combined changes in L*a*b values. The concept of the best possible E or best performance under given circ umstances was also evaluated. It was apparent that the number of reference colors and color selections had an impact on the error made by panelists. More co lor selections reduced the E values of the visual evaluations. Statistical analysis described si gnificant differences between the number of reference colors, the number of selections, presentation, and the interacti on between the reference colors and the selections. The 8 and 16 reference colors bar pr ovided less error compared to the 12 reference colors bar, quantified by both E for both mangos and nectarines. The 12 reference colors bar gave the most error. Two color selections provided th e highest mean values. The screen images in general had lower mean values than the fruit trays. This study provided a better understanding of the way panelists perceive non-uniform colors. It also suggested a new formulation of consumer panel studies involving non-uniform visual attributes of foods.

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15 CHAPTER 1 INTRODUCTION Todays consumers have increased expectations for the quality of food they purchase. In this competitive market there is no second chance to make a first impression. An important first impression is the color and appearance of f ood. How do consumers perceive color? Humans have difficulty in quantifying color, but are good at comparing it with a reference color. Therefore, reference colors are used in many in stances, e.g. color of a potato chip, salmon color, egg yolk color, etc. In all these examples, the co lor of the food is relatively uniform. There are a limited number of studies that corr elate the uniform color of foods measured by instruments, and by sensory panels. However, many foods have no n-uniform colors, e.g. mangos, nectarines, etc. How can we accurately measure the color in th is case? Many instruments measure the average color, but this causes loss of color information in the case of non-uniform foods. Machine vision technology eliminates this problem by measuring all the colors at the surface of a non-uniform food. Another difficulty is how to measure the non-uniformity of color. In this study, methods were developed and used to quantify the non-unifor mity of color with the use of machine vision technology. Once the non-uniformity of color is determined, how will this affect how consumers perceive the color of non-uniform foods? Intuitively, we expect that the more non-uniform the color, the more difficult it will be for consumers to describe or quantify it. In a preliminary study, we found that for rabbit meat, the more nonuniform the color, the more error consumers made in correctly quantifying it (Balaban and others, 2007). In this study, we asked the following questions: Can the image of a food material, taken with a good digital camera, and under controlled conditions, be substituted for the real food, fo r the purposes of evaluating visual and color attributes? If this is possible, then geogra phical and temporal rest rictions in evaluating visual attributes will be eliminated. The image of a food can then be sent anywhere in the

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16 world to be evaluated. Food images from diffe rent times can be compared without concern for decay. Also, the image of the food, as an accu rate representation of it, can be used for record keeping. If reference colors are to be used in ev aluating the non-uniform color of foods, how many reference colors should be presented to the sensory panelists? How will the number of reference colors affect the error that the consumer makes in quantifying the color? The answer to this question would allow optimizatio n of the number of reference colors to use. From a number of reference colors, how many colors should a panelist select? Too few color choices may not allow a good representation of the actual colo r. On the other hand, too many colors may confuse the panelist, and may allow large errors in the quantification of real colors. The answer to this question will allow the fine-tuning of the way panelists are asked to evaluate non-uniform colors. The quantification of color is becoming increasingly important due to an emphasis on international trade, and implementation of H azard Analysis Critical Control Points (HACCP) requiring record keeping. Thus, it is important to provide the agricultura l industry with methods to quantify and correlate sensory and instrumental evaluations of foods. The overall impact of this study will be a better understanding of the way panelists perceive non-uniform colors. This will result in a better formulation of consumer panel studies involving visual attributes of foods.

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17 CHAPTER 2 LITERATURE REVIEW Color of Foods and Ag ricultural Materials Color is an important quality attribute for almost every agricultural product (Delwiche, 1987). Consumers may perceive color as an in dicator of freshness and wholesomeness, and color may affect their final deci sion to accept/reject food. For the meat industry, muscle color is the primary characteristic consumers consider when evaluating the quality and acceptability of meats (Cornforth, 1994). The discoloration of retail beef accounts for $1 billion in price discounts annually (Mancini and Hunt, 2005). Color determines the degree of ripeness of many vegetables and fruits (Polder and others, 2000). Different grains a nd their varieties are commonly characterized according to kernel color and quality defects such as grass-green, binburnt, and fungal-damaged (Lou and others, 1999). Color measurement of food and agricultural materials can be performed subjectively by sensory panels (Chizzolini and others, 1993). Co lor can also be measured by instrumental methods (Balaban and Odabasi, 2006). The quantification of color is becoming increasingly important due to an emphasis on international trade, and implementation of Hazard Analysis Critical Control Points (HACCP ) requiring record keeping. Instrumental Color Measurement in Agricultural Food Products The agricultural industry uses mostly high cost labor intensive methods to assure control of color quality parameters. One possibility to reduce cost is to use instrumental methods to measure color to emulate human visual percep tion (Zhu and Brewer, 1 999). Instruments are cost-effective, repeatable and objective in measuri ng color. Instruments such as colorimeters are commonly used to measure color in the agricultural industry. Colo rimeters provide users with fast and simple averaged color measurements.

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18 The accuracy of the instrument is assured by ca librating with standard color tiles before measurement. The color reading is obtained by providing a controlled il luminant or standard light source. Common standard lig ht sources are: A=tungsten lam p, B= near sunlight, C= near daylight, D= daylight. Colorimeters have illumi nants C or D65 with color temperatures of 6774 K or 6504 K (Oliveira and Balaban, 2006). Howe ver, it is known that other methods provide more precise color measurements (Coles and ot hers, 1993). Colorimeters may not measure the observed color if the product has non-uniform colo rs, because all colors in their view area are averaged. If the agricultural product is too sma ll, or too big, or has non-uniform surfaces, then sampling location for color measurement becomes critical. Also, careful consideration is necessary if data are compared between industria l plants, since variations between instruments may occur (Brewer and others, 2001). Spectrophotometers are also used in agriculture to measure co lor. The working method of these instruments is based on the generation of a spectral curve representing the transmittance or reflectance of light from the su rface of the product. This is immediately compared with the reflectance of a reference standar d. The values may be converted to different color space values. The agricultural industry requi res a better method of color measurement. In the 1960s the use of a camera with a computer and software ca pable of image processing became an option for color measurement (Brosnan and Sun 2004). The system was called computer vision or machine vision. The capabilities of this instrument were precise, accurate and fast color measurement of agricultural products. Computer Vision or Machine Vision System The computer vision or machine vision (MV) systems started in the early 1960s. Since then, the use of machine vision in the agricultu ral industry has grown. Machine vision is used for its generation of precise data, consistenc y, and cost effective color measurement.

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19 This instrument aims to emulate human visual perception by using a camera and a computer with software capable of performing predefined visual tasks (Brosnan and Sun 2004). Images are captured in digital form by a ch arge coupled device (CCD) camera. CCD cameras can convert light into electrical charges and create high-quality, low-noise images with pixels. They have excellent light sensi tivity; they are free of geometri c distortion and highly linear in their response to light (Du and Sun 2004). Th e computer software then performs image processing, which is the study of representation and manipulati on of pictorial information (Martin and Tosunoglu 2000). The pictorial information is converted to three-dimensional color space of red (R) green (G) and bl ue (B) values. Further analysis provides color results. Search for cost-reduction and increased effi ciency in quality inspection has made the agricultural industry look for techniques and instruments that provide more complex and accurate as well as fast and obj ective determination of quality parameters in online inspection. Machine vision has shown to be a useful method in this area (Blasco an d others, 2003; Lee and others 2004). Machine vision has several other advantages over other color meas urement instruments: Images are composed of the entire view ar ea making the analysis more representative The data provided from images can be conve rted to different color measurement systems (OSullivan and others 2003) and processe d beyond the capabilities of colorimeters Non-uniform surfaces and colors can be handled easily The agricultural industry uses image proces sing and MV to classify, sort and grade agricultural produce in diverse areas such as bake ry, meats and fish, vegetables, fruits, grains, prepared consumer foods and even food contai ner inspection. The food industry ranks among the top ten industries to use image pr ocessing techniques (Gunasekaran, 1996).

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20 Bakery Products Bakery products are influenced by their ex ternal as well as internal appearance. Consumers judgment on their appearance dictates purchasing decision and marketability, and it is essential to meet and exceed their expectations of quality of bakery products. At the same time it is essential to reduce cost. A MV system was used to classify defective bread loaves by height and top slope (Scott, 1994) Cookies were studied to es timate the fraction of top surface area covered with chocolate chip, and other physical features such as size, shape and color of baked dough (Davidson and others, 2001). MV was capable of providing automated inspection and could separate light from dark muffi n samples (Abdullah and others, 2000). Red Meat and Seafood In 2006, the retail value of U.S. beef industry was $71 billion (USDA, 2006 a). More than 12 billion kilograms of beef were consumed in th e U.S. in the same year, and, the beef industry represented 4.4% of U.S. total production exports. In the U.S. nearly 15% of retail beef is discounted due to surface discoloration, which corresponds to annual revenue losses of $1 billion (Mancini and Hunt, 2005). The USDA beef carcass gr ading system consists of two parts: quality grade and yield grade. Quality grade is eval uated by trained individuals. MV has been recognized as an objective alternative to a ssessment of meat quality from fresh-meat characteristics (Tan, 2004). Recent studies indi cate MV has great capability for classification and grading of beef muscle type, breed, age and tenderness (Basset and others, 2000; Hatem and others, 2003; Li and others, 1997). The purpose of grading meats is to standardize th e characteristics valuable to the consumer and those that facilitate marketing and merchandising (Hatem and othe rs, 2003). Beef rib eye steaks were effectively graded for quality attributes su ch as color and marbling scores determined by

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21 USDA using image processing (Gerrard and ot hers, 1996). The result s reported that MV predicted color with an accuracy level of R=0.86 and marbling with R=0.84. The pork industry has also applied MV to its processes. Pork loins were graded according to color. Researchers used image processing w ith statistical and neural network models to predict color scores of 44 pork loins (Lu and others, 2000). The scores were then compared with trained sensory panel scores. The scores were based on visual perception ranging from 1 to 5. Prediction error was the difference between instrumental and sensor y scores. An error of 0.6 or lower was considered not significant. Image pro cessing and neural network models were able to predict 93.2% of the samp les with error lower than 0.6. Sta tistical regressions were able to predict 84.1% of the samples w ith error lower than 0.6. Anot her study reported 90% agreement between a MV color score and a se nsory panel using 200 pork loin ch ops (Tan and others, 2000). Tedious human inspection and costs are part of the grading practices in the poultry industry. MV was used to separate defective (t umors, bruises, and torn skin and torn meat) poultry carcasses from normal carcas ses (Park and others, 1996). In 2006, freshwater and marine fishing produ ced 60 million tons for human consumption (FAO 2006). Americans consumed an average of 2.2 billion kg of seafood in 2006 (NOAA 2007). Fish represent one of the main sources of protein used in de veloping countries (Louka and others, 2004). Seafood inspection involves cos tly human involvement. MV was used to capture, identify and differentiate images of three different varieties of fish: carp ( Cyprinus carpio ), St. Peters fish ( Oreochromis sp .) and grey mullet (Mugil cephalus ) (Zion and others, 1999). This study also concluded that fish mass, an important qua lity parameter in marketing, could be predicted from image area with the use of image processin g. Other parameters important to market these

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22 three types of fish were acquired. Fish species ha ve also been sorted according to shape, length and orientation in a processing li ne (Strachan, 1993). Image analys is was used to differentiate between stocks of Haddock ( Melanogrammus aeglefinus) (Strachan and Kell, 1995). Dimension reduction derived from principal component anal ysis and canonical correlations was used. The reports showed 71.7% correct sorting accuracy fo r shape and 90.9% and 95.6% for both stocks in color differences. Flesh quality is important for successful development of fish farming and fish processing (Marty-Mahe and others, 2004). Objectiv e criteria to predict flesh redness from the spawning coloration of fall chum salmon has been performed with image processing (Hatano and others, 1989). Skin color development is an im portant quality parameter for live goldfish ( Carassius auratus ), an ornamental fish of high commercial value (Chapman and others, 1997). Objective measurement and quantifica tion of the color of live goldfish ( Carassius auratus ) raised in well water was acquired by a machin e vision system (Wallat and others, 2002). The color of dried cod fillets may go from yellow to orange, dependi ng on the drying method used. Image processing has been used to compare dr ying methods in cod fillets (Louka and others, 2004). The fillets were subject to three drying methods: hot air drying, vacuum drying, and freeze drying. Image processing compared the three techniques to controlled instantaneous discharge (DIC) and dehydration by successive di scharge (DDS), two new techniques of drying cod fillets. The highest whiteness value found was quantified in freeze-drying and the lowest in air drying. Analysis of variance was used to find differences between procedures. Vacuum drying and DIC did not have significant differences. Catfish ranks as the fourth most popular seaf ood consumed in the U.S. Fresh farm-raised catfish ( Ictalurus punctatus) quality relies primarily on human inspection. MV was used to evaluate color changes over storage time for fr esh farm-raised catfish (Korel and others, 2001).

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23 Vegetables Vegetables are greatly affected by quality factors such as size, shape, color, blemishes, and diseases. Image processing resulted in more pr ecise color measurement for potato crisp color (Coles and others, 1993). The potato industry used a MV system and online inspection to grade potatoes by shape (Tao and others, 1995). Th is study reported 89% agreement between the instrument and human perception. The accuracy in grading potatoes was 90% by using hue, saturation and intensity color system. Discoloration of the mushroom cap reduced product quality, with less market value (Brosnan and Sun, 2004). In orde r to maximize quality parameters a MV system was used to inspect and grade mushrooms based on color, stem cut, shape and cap veil opening (Hienemann and others, 1994). MV resulted in a 20% classification error compar ed to two human inspectors. The surface color of tomatoes was analyzed us ing a MV system classifying differences in ripeness stages (Polder and others, 2000). Image processing from MV was used to recognize and estimate cabbage size for a selective harvester (Hayashi and others, 1998). Surface defects, curvature and brakes of carrots are quality para meters that influence the products value. MV was used to classify standard and def ective carrots (Howarth and others, 1992). Fruits In 2005, the U.S. fruit consumption averaged 128 kg per person (fresh-weight equivalent) (USDA, 2006b), with bananas being the most c onsumed fresh fruit. Apples were the second favorite fresh fruit. A MV system was used to evaluate the color to determine the ability of oxalic acid to inhibit browning in banana and apple slices (Yoruk and others 2004). Golden delicious apples were evaluated for quality parameters such as bruises, scabs, fungi or wounds with the use of a MV system (Leemans and others 1998). The results suggested that image

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24 processing with different algorith ms were able to detect bruises, scabs, fungi and wounds in golden delicious apples. In 2002, the U.S. was the worlds largest im porter of mangos (Perez and Pollack, 2002). In this same year, Mexico shipped over 90% of its exports to the U.S. The increased consumption of mangos is related to the in creased population of Latino and Asian groups. Consumers seek mangos without external damage, w ith stable weight, color and consistency, at a reasonable price (Zuiga-Arias and Ruben 2007). However, grading mangos for export involves hand labor and subjectivity. A MV system equippe d with cameras to obtain single and multiple view image angles was used to evaluate physical parameters like: proj ected area, length, width, thickness, volume, and surface area with 96.47% accuracy (Chalidabhongse and others, 2006; Yimyam and others, 2005). Prepared Consumer Foods The evaluation of cheese functional properties su ch as different cooking conditions, size of samples and shred dimensions are important as pects for the marketability of pizza. Topping types, percentage and distributions influence the appearance and the different varieties of pizza. Pizza image acquisition is very complex due to the non-homogenous colors, shapes, overlapping, shadows, and light reflection. Methods have been developed to quantify the color distribution and topping exposure in pizza (Sun and Du, 2004). Food Container Inspection MV and image processing are used to determine shape, and check for foreign matter, threads of bottles, sidewalls and base defects, fill levels, correct closure and label position of food containers. MV has also been used to check for wrinkles, dents and other damages to aluminum cans that cause leakage of contents (Seida and Frenke, 1995).

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25 Grains Nigeria is the worlds leadi ng importer of wheat (USDA 2006c ). Ninety percent of the imported wheat is supplied by the U.S. Competitive prices and product quality has lead the U.S. increase the wheat market in Nigeria. The va riety, environmental effects and class make the classification of wheat a very complex practice even for experienced inspectors. MV systems and image processing have been used widely in wheat (Uthu, 2000; Majumdar and Jayas, 2000; b; Nair and others, 1997). A MV system and crush force features were used to differentiate hard and soft wheat varieties (Zayas and others, 1996). The correct differen tiation rate was 94% for the varieties tested. Corn kern els were analyzed with MV for whiteness, mechanical and mold damage (Liu and Paulsen, 1997). Rice has also been studied using MV and image processing. The appearance characteristics of brown rice such as kernel shape, color, and defects were determined using a MV system (Wan and others, 2000). An online automatic inspection system was able to recognize cracked, chalky, broken, i mmature, and damaged brown rice kernels. Other Applications A MV system was used for online inspecti on of dry sugar granules and powders to determine particle size for pro cess control and quality improveme nt (Strickland, 2000). Image processing and MV were used to detect dirt on brown eggs with stains, dark feces, white uric acid stains, blood stains and stains caused by egg yolk (Merte ns and others, 2005). The results reported 91% overall accuracy of image pr ocessing to detect dirty eggs. Research efforts were made to provide e fficient image-based techniques to monitor distribution and migration of fish (Nery and others, 2005). Image processing was used to classify nine species of fish based on adipose fin, anal fin, caudal fin, head and body shape, size and length/depth ratio of body (Lee and others, 2 003). This method provided an alternative to subjective monitoring of numbers, size and specie s at specific fish passages during migration.

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26 Visual Texture Analysis Visual texture is defined as how varied or patchy the color of a surface looks (Balaban, 2007). MV systems have been used in determining color, size and shape of agricultural produce. Texture analysis with MV has great potential due to the powerful discriminating ability and pattern recognition of this technique. Texture information may be used to enha nce the accuracy of color measurements (Menp, 2003). Texture is characterized by th e relationship of the intensities of neighboring pixels (Palm, 2004). Visual text ure discriminates different patter ns of images by extracting the dependency of intensity between pixels and th eir neighboring pixels (K artikeyan and Sarkar, 1991). In other words, texture is the repetition of a basic pattern. The patterns can be the result of physical surface properties such as roughness or oriented strands, even the reflectance differences given by a color on a surface (Tuceryan and Jain, 1998). Visual texture analysis is divided into four main areas: statis tical texture, stru ctural texture, model-based texture, and transformbased texture. Statistical text ure describes mainly regions in an image through high-order moments of their gr ayscale histograms (Bharati and others, 2004). Structural texture is describe d as a composition of elements regulated by rules in images. Model-based texture generates an empirical model of each pixel in the image based on a weighted average of the pixel intensities in its neighborhood. Tr ansform-order texture converts the image into a new form using spatial frequenc y properties of the pixe l under consideration of its intensity variations. Image analysis literature describes many ways to quantify texture (Bertrand and others, 1992; Mao and Jain, 1992; Reed and Du Buf, 1993; Tuceryan and Jai n, 1998). A new method used to quantify non-uniform colors is that of color primitives and color change index (Balaban,

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27 2007). The methodology used to quantify colo r non-uniformity should be independent of rotation, variation in size and shape (Zheng, 2006). Visual Texture Applications in Agriculture Image texture analysis has been used in grading and inspection for quality and safety of agricultural products. A MV syst em was used with image proce ssing and texture analysis to quantify changes in color, shape and image texture of apple sli ces (Fernandez and others, 2005). A method for texture analysis was developed to quantify non-ho mogeneity of color of mangos, apples and rabbit meat using color primitives and color change index, were a color primitive was defined as a continuous area of an image with sim ilar light intensity (Balaban and others, 2007). Texture analysis was used to identify the cha nges in textural appearance in experimental breads caused by variations of surfactants adde d to flour (Bertrand and others, 1992). Iyokan orange fruits ( Miyauchi Iyokan) were used to predict sugar content of oranges (Kondo and others, 2000). Image processing and texture analysis we re entered to a neural network. MV system along with neural networks recognized relatively sweet fruit from reddish color, low height, medium size and glossy surface. Several studies on meat tenderness characteristics (Chandraratne and others, 2006) and classificatio n of genotypic origins of bovine meat (Basset and others, 2000) have been successful. Texture analysis evaluated the microstructure of food surfaces such as potatoes, bananas, pumpkins, ca rrots, bread crust, potato chips and chocolates (Quevedo and others, 2002). Texture features have demonstrated to be effective discriminating models for classifying wholesome and unwholes ome chicken carcasses (Park and others, 2002). Correlation between Image and Visual Color Analysis The majority of studies regarding color co mparison between sensory and instrumental measures in foods have been developed in the meat area. Research was performed to compare

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28 and correlate homogeneous color measurements of pork, beef, and chicken using instrumental and visual color analysis (Denoyelle and Berny, 1999; Lu and others, 2000; Sandusky and Heath, 1998; Zhu and Brewer, 1999) Meat and poultry were used to correlate instrumental and visual color evaluation. A range of meat redness was studied by mixing ground poultry breast and ground beef. High correlations between visual redness and instrumental redness were found (Zhu and Brewer, 1999). The comparison and correlation of instrumental and visual color analysis has also been studied in bakery, seafood, and in medical fields. Research us ing cookies for color analysis showed a strong correlation between sensory and instrumental methods (Kane and others, 2003). The relationship between sensory and instrument al correlations using raw, baked and smoked flesh of rainbow trout ( Onchoyhychus mykiss) was studied. Close relationship between color evaluation by sensory analysis and instrument al methods was observe d (Skrede and others, 1989). A study on colorimetric assessment of sm all color differences on translucent dental porcelain revealed strong correlation between inst rumental and visual colo r analysis (Seghi and others, 1989). However, comparison and correl ation of non-homogeneous color measurements in foods is more challenging and has not been thoroughly studied. Preliminary Experiments A method was developed to quantify the perception of non-homogeneous colors of foods by sensory taste panels. The average colors of mangos, apples, and rabbit meat were measured using MV. Differences between the average (real) colors (MV system) and those from the sensory panel were reported as E values (Balaban, 2007). A sensory panel composed of 20 panelists performed visual evaluations of rabbit meat captured images and 60 panelists for that of real fruit and captured sample images. The degree of non-

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29 uniformity of sample colors was determined using two methods: color blocks and color primitives. A color reference bar was developed for the panelists to select colors that represented those of the samples. Panelists selected 3 colors from these reference colors, and estimated their percentages. The red mango had more colo r blocks, and visually represented more nonuniform colors. In the case of rabbit meat sample s, there was no apparent advantage of using the color block scheme. Clearly, a different method to quantify non-uniformity needed to be developed for these samples. The rabbit sample s had colors ranging from white to red, with many shades in between. The lack of any other hue value may have contributed to the inability of the color block scheme to quantify non-uniformity. The more non-uniform samples were mo re difficult to evaluate, thus, the E error was higher. The non-uniformity of the samples cause d more difficulty in th e panelists matching ability with the reference color scale, and caused higher errors. Males (33) and females (27) were compared regarding E values. The mean E for males and females was 10.58 and 10.18, respectively, with a p-value= 0.52. In this study gender did not significantly affect E. A higher number of panelists ma y or may not affect this outcome. This preliminary research suggest ed a criteria and parameters to quantify the error panelists made when subject to visual appraisal of non-homogenous colors in foods (Balaban, 2007; Balaban and others, 2007). However, the number of colors that panelists selected from a reference color bar was limited to 3 choices. More studies are needed to study the effect of the number of colors in the reference scale, and the number of colors to choose. The food industry could benefit from a better understanding of preci se, repeatable and accurate color measurements of foods with non-uniform surfaces an d/or colors. The quantitative

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30 measurement of color attributes of agricultura l materials is important in quantifying quality, maturity, defects, and various other color-depen dent properties. Globa l market expansion and implementations of Hazard Analysis Critical Cont rol Points (HACCP) require record keeping. The difference of screen captured image and real sample and its effect on human perception of sensory evaluations has not been studied thoroug hly. A properly taken image of a food sample can be a good representation of the food itself. Th is may provide a usable and more flexible tool in the analysis of visual attributes. Objectives of the Study The objectives of this study were: i. To measure differences in color evaluati on between sensory panel and MV system, for non-uniformly colored fruits and their images. ii. To develop a quantitative measure of the degree of non-uniformity of color, and to evaluate the effect of degree of non-uniformity of sample color on the difference in color evaluation. iii. To evaluate the effect of the number of reference colors, and number of allowed color selections on the error in color evaluation

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31 CHAPTER 3 MATERIALS AND METHODS Mangos and Nectarines The fruits used in this study were artificia l fruits to avoid colo r degradation due to maturation and decay of real fruits. Mangos and nectarines generally have non-uniform colors and surfaces. The fruits used in this study c onsisted of red mangos and nectarines with nonuniform surface colors. The mangos were pur chased from Amazing Produce (4470 W. Sunset Boulevard Suite 106 Los Angeles, CA 90027) and the nectarines made of compressed polyfoam from Zimmerman Market (254 E Main St Leola, PA 17540) (Figure 3-1). The fruits were placed on aluminum trays. Adhesive tape was used to keep fruits from moving while images were captured. There were a total of 10 trays with one mango and one nectarine in each. The mangos and nectarines shown in Figures A-1 to A10 were first wrappe d in grey paper (R= 128, G = 128, B = 128) to obtain a color neutral background. Figure 3-1. Example of mango and nectarine on aluminum tray.

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32 Image Acquisition The artificial fruits were placed inside a lig ht box built of white acrylic sheets as shown in Figure A-11. The light box had top and botto m lighting with 2 fluorescent lights each to simulate illumination by noonday summer sun (D65 illumination). The door remained closed while images were captured to assure uniformity of light inside and to minimize the effect of outside light. Images were captured using a camera (Nikon D200 Digital Camera, Nikon Corp., Japan) located inside the chamber mounted to face the bottom of the light box as shown in Figure A-11. The image acquisition set up is shown in Figure A-12. The Nikon D200 Settings used are described in Table 3-1. After the im ages were captured, trays were labeled for booth and tray numbers for identification purposes. Table 3-1. Nikon D200 settings. Setting Specification Device Nikon D200 Lens VR 18-200 mm F 3.5-5.6 G Focal length 36 mm Sensitivity ISO 100 Optimize image Custom High ISO NR Off Exposure mode Manual Metering mode Multi-pattern Shutter speed and aperture 1/3s F/11 Exposure compensation (in camera) 0 EV Focus mode AF-S Long exposure NR Off Exposure compensation (by capture NX) 0 EV Sharpening Auto Tone compensation Auto Color mode Model Saturation Normal Hue adjustment 0 White balance Direct sunlight Image Analysis Each captured image included a red color st andard with known L*, a*, and b* values (Certified Reflectance Standard, Labsphere, ID# SCS-RD-020). Captured images of the fruits

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33 were analyzed for average color, color blocks, and color-texture profiles using MV software. The values obtained were compared with the meas ured L*, a*, and b* values of the red standard. The difference of the L*, a*, and b* values was used as the correction factor for the whole image. The images were cleaned using an image editing software. Each acquired pixel had (R), (G) and (B) color in tensities. The calibrated images were then used to determine the average L*, a*, and b* values using every pixel of the fruits with Lens Eye color evaluation software. For color block analysis, the program read RG B values from every pixel in the captured image, and counted that pixel a specified color block. Each pixels RGB values were converted first to tristimulus values XYZ, and then to L*, a*, and b* values. The color data generated by the software was presented in histogram form. This feature allowed all colors present on the surface area to be seen more easily. Because all colors present were too numerous to be considered for the color scale formation, a method was developed to represent the most significant surface colors. Experimental Design For this study, a completely randomized design was used. Because th e effects of two or more factors may affect the outcome, whether or not interaction exists, a factorial experimental design was implemented. The independent variables considered were num ber of reference colors, number of colors to choose from the reference colors, and the sensory evaluation of screen image or real fruit. The dependent variable for this study was the E values. The value is the color differences between sensory and MV measured colo rs of each sample for each panelist. E measures total color change by accounting for combined changes in L*a*b values.

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34 2 2 2******so so sobbaaLLE (3-1) The subscript 0 refers to the MV read valu es, and s refers to the panelists average The sensory panel combinations are shown in Table 3-2, and each session was performed at different days usi ng different panelists. Table 3-2. Factorial-Level combinations. Number of references Two Selecti ons Four SelectionsSix Selections 8 Session 1 Session 2 Session 3 12 Session 4 Session 5 Session 6 16 Session 7 Session 8 Session 9 Method of Selection of the Reference Color Bars Reference color bars were added to each image to be presented to the panelists (Figure A13). Using all 10 fruit tray images (both mangos and nectarines), sixteen global reference colors were selected from all the color blocks with mo re than 1% of the surface area of a sample. The reference color bars consisted of 8, 12 and 16 re ference colors (Figure 3-2). Each color in the reference color bars had known L*a*b values (Tables A-1, A-2, A-3). The different color scales and number of colo rs were designed to test and quantify the effect of these variables on the ab ility of panelists to match fruit colors. From our preliminary study, we expected that it would be harder for panelists to correctly select several colors. On the other hand, more color selection may enhance the abil ity to predict closer to the real color. The sample numbers for tray and fruit images were the same. Also, the tray assigned to each booth was maintained throughout the nine sens ory sessions. However, the presentation of the images or fruits was randomized.

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35 Booth 03397 541 06 06 01 01 03 03 04 04 05 05 08 08 02 02 07 07 Figure 3-2. Example of reference color bar with 8 colors added to fruit images presented to the panelists. Sensory Evaluations The sensory panel was composed of college age students from the University of Florida. Each session consisted of n=80 panelists. There were a total of 9 sessions (1 combination per session) at different days using different panelists. Panelists evaluated fruits from two sources: screen image and fruit tray. The presentation order was randomized. The questionnaires consisted of two separate paper sheets ha nded to participants at the stages of the sensory evaluation. Each paper sheet included: evaluation stage (image or tray sample) date, age and gender of the panelist, boot h number, and five inst ruction steps explaining how to fill out the questionnaire. Also, each sh eet included a table with two columns (Figure A14 and A-15). The two columns in each table incl uded spaces to select the sample number of colors to choose, and percentage of total area per color.

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36 Sensory room staff explained to the panelists the reference color bars, and how to match each color to the surface area of th e fruits on the screen or in th e tray. Once panelists finished evaluating, e.g. the screen images, they handed in the questionnaire to staf f, who made sure it was properly filled out. In the se cond part of the session, using e .g. real fruit trays, staff would pass the questionnaires and the fruit tray to pa nelists. They would also explain again the directions to properly fill the que stionnaires. Before the participants could leave the room our sensory panel staff checked the above and made su re that the panelists followed instructions properly, filled out evaluation sheets, one for screen image and one for fruit tray. It was critical for our study that panelists selected the correct number of colors and th at percentages added to 100. The questionnaires were checked one more time for selection numbers and percentages and the data were entered to a spreadsheet to prepare for statistical analysis. Determination of Color Uniformity of Fruit Degree of non-uniformity of color or color-texture is a relatively new area in the computer vision field. The degree of non-uniformity in th is study was quantified using two methods: the number of color blocks, and color primitives (Balaban, 2007). Average Color: Individual L*, a*, and b* values of each pixe l in an object are read and averaged. For uniform colored materials this method is sati sfactory, but when the colors are non-homogeneous, the averaging may result in unrealistic colors. For most agricultural materials colors vary throughout the surface. Therefore an average color is of little us e. Also, frequently defects or ripening stages are detected becaus e they are of different colors.

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37 Color Blocks The machine vision system used in this study captured images having 24 bits of color. Each acquired pixel had three-dimensional colo r space RGB color intens ities represented by 8 bits in the computer, resulting in 256 possible va lues for each. The total number of distinct colors represented by this system (256)3 is too large to apply in reality and a known method was used to reduce the number of colors in the color space. In this study each color axis was divided into 16 (16 x 16 x 16 = 4096 color blocks). Any color within a color block was represented by the center color of that block. The machine vision system then counted the number of pixels that fell within a color block, and calculated the percen tage of that color base d on the total view area of the object. Some color blocks were ignored be cause their percentage in the total area was too small. The acceptance threshold for the color bl ocks was set to 1% of the total area. The assumption was that the higher th e number of color blocks, the more non-homogeneous the color of the object. Color Primitives A color primitive is defined as a continuous area of an image where the intensity of any pixel compared to an anchor pi xel is within a gi ven threshold value (B alaban, 2007). The intensity difference is defined as I. 2 2 2so so soBBGGRRI (3-2) Once all the pixels that belong to a primitive with I values less than a given threshold are found, and no other pixels can be added, then the anchor pixel is changed to an available, neighboring pixel and the process continues until all the pixels of an object are processed. The subscript o defines the base color, and the subscript s defines a pixe l that is tested.

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38 The center of gravity of the pixels bel onging to a primitive was calculated (Balaban, 2007). Also, an equivalent circle with the same area as that of the primitive was found. The radius of this circle was defined as: area primitive radius (3-3) This circle was drawn with its center at the center of gravity of the primitive. The advantage of the color primitives is that there may be many primitives with the same color, but they will be counted separately. The more color primitives in an image, the more nonhomogeneous the color of that object. The I values of neighboring color primitives, and the distance between their equivalent circles can be used to quantitatively ca lculate the degree of non-homogeneity of color. circles equivalent two betweenceDis I colorofchangeofrate tan (3-4) The more color primitives there are, the highe r the value of the cumulative color change index. Also, the bigger the area of an object th e more color primitives, everything else being equal. Therefore, a color change inde x (CCI) was proposed (Balaban, 2007): 100 tan areaobject neighbors ofnumber circles equivalent betweencesdis primitives gneighborin allforI CCI (3-5) MV results reported L*, a*, and b* values. Lens Eye color evaluation software required all reference bar L*a*b values to be entered. It also required the i nput of all MV determined L*a*b values from all 10 trays. The fina l data input to the program was that of the panelists and their choices of colors from the reference color bars. The output of the program reported panelists L* a*b values as well as MV determined L*a*b values. It finally calculated E values for all nine sensory sess ions. Each reference colors L*,

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39 a*, and b* values were weighted by the per centage, and averaged, providing the estimated average color of a sample. The data was grouped in order to prepare fo r statistical analysis. iref n i iL P orL AverageCol Estimated, 1* 100 (3-6) iref n i ia P ra verageColo EstimatedA, 1* 100 (3-7) iref n i ib P rb verageColo EstimatedA, 1* 100 (3-8) The variable n refers to the numbe r of selections (2, 4, and 6). Pi refers to the percentage of color i. L*ref,I is equal to the L* value of ith reference color. a*ref,I is equal to the a* value of ith reference colors, b*ref,I is equal to the b* value of ith reference colors. Calculation of Best Possible E There is an inherent error in trying to correctly qu antify the non-uniform color of a material using a finite number of reference colors, and a finite number of selections from these colors. The best possible values given th e different selection of referen ce colors and choices a panelist could provide are describe d as the best possible E. A computer program was developed to take each combination of possible choices from a given number of referen ce colors, and then try the percentages of these selected colors from 1 to 100, accept the cases with the sum of all percentages adding to 100, and find the combination with the minimum E. This value can then be subtracted from the E values of panelists (absolute E) to form a more accurate error term. Statistical Analysis The E for sensory and MV measured colors of each sample for each panelist were calculated. The results were evaluated fo r the ten booths and nine sessions. The E values were analyzed using SAS 9.0 for statistical analysis.

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40 ANOVA procedures (Duncans multiple range tests) and Mixed Models (Restricted Maximum Likelihood REML LS means) were used to find significant differences on the effect of reference color bar, number of selections, and presentation source screen image or fruit tray. The mixed model provided the flexibility of mode ling not only the means of data (as in the standard linear model) but their variances and co variances and fixed effects as well. The need for covariance parameters arouse because rep eated measurements were taken on the same experimental unit, and these repeated measuremen ts are correlated or exhibit variability that changes. The ANOVA can provide incorrect results depending on the design because if analysis is driven by accounting for degrees of freedoms and tests, p-values, contrasts, least square means etc. may be taken for granted. The ANOVA used in this experiment computed means of the dependent variables for the effects mentioned earlie r based on Ordinary Least Squares. All main effects were tested using m eans for those effects. In each method, E absolute or E difference were used as m odel statements or dependent variables. The program codes and out puts are shown in Tables E-1 and 2.

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41 CHAPTER 4 RESULTS & DISCUSSION MV Color Results of Fruits MV measured colors of each fr uit in each booth are summarized in Tables 4-1 and 4-2. For both mangos and nectarines it ca n be seen that L*a*b values and number of color blocks are similar for all 10 booths. It is also seen that the Color Change Index (CCI), the number of primitives and the number of neighbors are slightly different for each booth. Table 4-1. MV color analysis for mangos. Booth L* a* b* # Color Blocks CCI # Primitives # Neighbors 1 51.79 31.91 42.39 31 6.423 616 1190 2 52.92 27.18 45.61 31 9.218 613 1239 3 49.62 37.21 44.4 33 5.588 513 982 4 50.42 25.31 42.91 33 4.904 548 1012 5 48.41 28.24 42.62 22 6.968 593 1127 6 56.39 21.93 46.84 30 7.164 615 1190 7 48.48 39.19 42.4 32 6.762 609 1196 8 46.88 38.92 40.34 20 4.338 538 964 9 46.64 36.3 40.59 33 4.589 536 1016 10 52.22 31.26 43.85 32 11.294 738 1511 Table 4-2. MV color analysis for nectarines. Booth L* a* b* # Color Blocks CCI # Primitives # Neighbors 1 52.57 36.94 43.81 31 17.516 915 1760 2 53.3 35.55 45.31 31 13.721 693 1366 3 53.16 34.67 48.11 32 13.135 581 1196 4 54.61 35.67 41.26 27 13.202 794 1521 5 62.78 26.47 50.46 28 11.326 626 1215 6 60 28.71 49.91 31 14.231 729 1491 7 65.25 24.48 45.09 25 17.759 856 1648 8 53.45 38.59 44.06 30 15.194 756 1488 9 56.51 30.86 43.92 31 11.490 739 1414 10 57.57 29.13 42.24 30 14.768 773 1566

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42 The tables A-4, A-5 show a summary of the number of primitives, number of neighbors and color change index for mangos and nectarines used in this study. The color primitives analysis, e.g. mango and nectarine for booth 1, is shown in Figure A-16. Non-Uniformity Analysis of Fruits The degree of non-uniformity was determined us ing color blocks and number of primitives (Tables 4-1, 4-2). The numbers of color blocks considered were t hose colors greater than 1% of the sample surface area. The average L*a*b values in all booth for mangos and nectarines were similar to each other. Because of these simila rities between booths, the number of color blocks were also similar between booths. The number of color blocks for the two fruits had a range of values from 20-33, with higher values being pred ominant. However, in some instances, e.g. booth 8 for mangos and booth 7 for nectarines, the number of color blocks was 20 and 25, respectively (Tables 4-1 and 4-2). For nectarines, the range of co lor blocks was from 32 to 25, representing a change of 22%. The CCI values ra nged from 17.7 to 11.3, representing a change of 36%. This suggests that the number of color blocks for mangos and nectarines is not a better parameter to quantify the non-uniformity of these samples, compared to CCI. This was expected, since from previous results it was considered th at the number of color blocks does not represent an accurate method to quantify colors of non-homogenous foods with a wide distribution of hues. Although the number of color bloc ks in most booths were clos e, it can be seen that the number of primitives, number of neighbors, and CCI change for each booth were different. For example, the CCI for mango in booth 1 was 6.4 and that for booth 2 was 9.2. For both fruits, the number of color blocks was 31. The same patte rn is seen for booth 1 and booth 6 of the nectarines as shown in Table 4-2.

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43 The correlation between CCI, number of primitives and number of neighbors for both mangos and nectarines is shown in Figures 4-1, 4-2 and 4-3. 0 5101520 CCI 400 500 600 700 800 900 1000#Primitives Mango Nectarine Figure 4-1. Correlation between number of primitives and color change index (CCI). 0 5101520 CCI 900 1000 1100 1200 1300 1400 1500 1600 1700 1800#Neighbors Mango Nectarine Figure 4-2. Correlation between number of neighbors and color change index (CCI). R-square = 0.8471 R-square = 0.7895

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44 4005006007008009001000 #primitives 900 1000 1100 1200 1300 1400 1500 1600 1700 1800#Neighbors Mango Nectarine Figure 4-3. Correlation between number of neighbors and number of primitives. From these figures it can be seen that thes e three variables are strongly correlated, and therefore can be used interchangeably. The number of primitives, CCI and the number of neighbors effectively quantified the non-uniformity of mangos and nectarines in all 10 booths. In general, nectarines were more non-uniform th an mangos. It would not be possible to make this conclusions using the L*a*b average values or the number of color blocks. Best Possible E Tables 4-3 to 4-10 show the best possible E for various combinations of color references and selections. The cases with 6 sel ections of colors are not shown since all best E values were less than 1. It can be seen th at especially for 2 or 4 selections, the best E values can be significantly high. This means that there is an inherent error associ ated with selection of 2 or 4 reference colors regardless of the number of available reference colors. Therefore, the real error that a panelist makes in estimating the color of a sample is the difference between R-square = 0.9685

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45 the absolute E and the best possible E. Large best E values are not restricted to 2 selections only. This is shown in Table 4-8 with 4 selections out of 8 reference colors in booth 7. Table 4-3. Best possibl e selections and minimum E value possible for 8 references and 2 selections for mangos. Booth Color 1 1% Color 2 2% Min E 1 3 56 5 44 2.923 2 3 46 5 54 1.895 3 3 67 8 33 2.407 4 3 14 4 86 2.588 5 1 42 3 58 1.931 6 4 87 7 13 2.669 7 3 71 8 29 2.191 8 3 70 5 30 0.827 9 3 46 4 54 0.612 10 3 54 5 46 2.800 Table 4-4. Best possibl e selections and minimum E value possible for 8 references and 2 selections for nectarines. Booth Color 1 1% Color 2 2% Min E 1 3 65 8 35 1.337 2 3 62 8 38 0.907 3 3 59 8 41 1.893 4 3 63 8 37 4.659 5 3 40 8 60 2.647 6 3 45 8 55 1.267 7 3 40 8 60 8.666 8 3 59 7 41 1.247 9 3 55 8 45 4.646 10 3 53 8 47 7.079 Table 4-5. Best possibl e selections and minimum E value possible for 12 references and 2 selections for mangos. Booth Color 1 1% Color 2 2% Min E 1 3 56 5 44 2.923 2 3 46 5 54 1.895 3 3 67 8 33 2.407 4 3 14 4 86 2.589 5 1 42 3 58 1.932 6 4 87 7 13 2.669 7 3 71 8 29 2.192 8 3 70 5 30 0.827 9 3 46 4 54 0.613 10 3 54 5 46 2.801

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46 Table 4-6. Best possibl e selections and minimum E value possible for 12 references and 2 selections for nectarines. Booth Color 1 1% Color 2 2% Min E 1 3 65 8 35 1.338 2 3 62 8 38 0.908 3 3 59 8 41 1.893 4 3 63 8 37 4.660 5 3 40 8 60 2.647 6 3 45 8 55 1.267 7 3 40 8 60 8.666 8 3 59 7 41 1.247 9 3 55 8 45 4.646 10 3 53 8 47 7.079 Table 4-7. Best possibl e selections and minimum E value possible for 16 references and 2 selections for mangos. Booth Color 1 1% Color 2 2% Min E 1 3 56 5 44 2.923 2 3 46 5 54 1.895 3 3 67 8 33 2.407 4 3 14 4 86 2.589 5 1 42 3 58 1.932 6 4 87 7 13 2.669 7 3 71 8 29 2.192 8 3 70 5 30 0.827 9 3 46 4 54 0.613 10 3 54 5 46 2.801 Table 4-8. Best possibl e selections and minimum E value possible for 16 references and 2 selections for nectarines. Booth Color 1 1% Color 2 2% Min E 1 3 65 8 35 1.338 2 3 62 8 38 0.908 3 3 59 8 41 1.893 4 3 63 8 37 4.660 5 3 40 8 60 2.647 6 3 45 8 55 1.267 7 3 40 8 60 8.666 8 3 59 7 41 1.247 9 3 55 8 45 4.646 10 3 53 8 47 7.079

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47 Table 4-9. Best possibl e selections and minimum E value possible for 8 references and 4 selections for mangos. Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min E 1 3 44 4 1 6 43 7 12 2.798 2 3 29 4 1 6 52 7 18 1.377 3 3 15 4 1 6 32 7 52 0.177 4 1 1 3 1 4 17 5 81 2.576 5 1 25 3 1 4 53 5 21 1.792 6 3 19 5 1 6 62 7 18 2.990 7 3 30 4 3 6 28 7 39 0.664 8 3 64 4 1 6 29 7 6 0.686 9 3 3 4 54 5 42 6 1 0.587 10 3 30 4 1 6 44 7 25 2.182 Table 4-10. Best possibl e selections and minimum E value possible for 8 references and 4 selections for nectarines. Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min E 1 3 1 4 1 6 33 7 65 2.828 2 3 1 5 1 6 36 7 62 2.564 3 5 1 6 41 7 40 8 18 2.125 4 3 17 4 1 6 36 7 46 5.930 5 5 1 6 59 7 36 8 4 8.814 6 5 1 6 54 7 36 8 9 6.851 7 4 1 5 1 6 60 7 38 11.595 8 4 1 5 1 6 30 7 68 3.688 9 3 1 4 1 6 45 7 53 5.452 10 3 18 4 1 6 49 7 32 7.221 Table 4-11. Best possibl e selections and minimum E value possible for 12 references and 4 selections for mangos. Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min E 1 4 32 5 47 7 2 9 19 0.0211 2 5 75 7 12 8 2 9 11 0.0225 3 4 8 5 43 7 43 10 6 0.0165 4 1 37 3 6 4 47 9 10 2.3482 5 1 34 3 1 4 62 9 3 1.7248 6 5 72 6 5 9 19 10 4 0.0220 7 1 11 3 58 4 11 10 20 0.0288 8 3 54 5 39 9 5 11 2 0.0352 9 1 18 4 75 5 2 9 5 0.4383 10 3 36 6 37 7 13 9 14 0.0314

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48 Table 4-12. Best possibl e selections and minimum E value possible for 12 references and 4 selections for nectarines. Booth Color 1 1% Color 2 2% Color 3 3% Color 4 4% Min E 1 4 59 7 8 9 10 12 23 0.0396 2 5 43 8 30 9 26 12 1 0.0192 3 1 14 3 38 8 15 10 33 0.0252 4 3 18 5 26 7 18 9 38 0.0527 5 6 9 7 32 9 34 11 25 0.0318 6 2 23 4 11 9 39 11 27 0.0241 7 5 20 8 1 9 70 11 9 0.0453 8 4 34 7 32 9 20 11 14 0.0327 9 2 22 4 8 6 20 9 50 0.0313 10 1 17 3 6 4 27 9 50 0.0616 Sensory Panel Results Images and trays of fruits were used in visual sensory evaluations. The reference color bars and color selections provided to panelists had the following combinations: 1. Treatment 1 = 8 reference co lor and 2 color selections 2. Treatment 2 = 8 reference colo rs and 4 color selections 3. Treatment 3 = 8 reference colo rs and 6 color selections 4. Treatment 4 = 12 reference color and 2 color selections 5. Treatment 5 = 12 reference colors and 4 color selections 6. Treatment 6 = 12 reference colors and 6 color selections 7. Treatment 7 = 16 reference colors and 2 color selections 8. Treatment 8 = 16 reference colors and 4 color selections 9. Treatment 9 = 16 reference colors and 6 color selections The summary performance of panelists evaluati ng mangos and nectarines in booth 1 are shown in Tables 4-13 and 4-14, respectively. The summ ary performance for the rest of the booths is shown in Tables B-1 to B-10.

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49 Table 4-13. Summary performance for panelists evaluating mangos for booth 1. Case Best E Screen E Stdev Tray E Stdev 1* 2.920 11.389 9.556 15.752 6.046 2* 2.790 10.701 5.158 10.615 2.298 3* 0.200 9.488 4.044 10.519 5.414 4* 2.920 21.152 7.643 18.036 8.864 5* 0.200 13.533 7.794 12.844 3.014 6* 0.200 13.872 8.261 12.729 4.741 7* 2.920 15.198 10.286 14.933 4.890 8* 0.200 13.972 9.509 11.335 1.861 9* 0.200 13.907 5.911 6.404 6.404 The number of reference colors and color selections has an impact on the error made by panelists as reflected in absolute E values (screen E or tray E). In Table 4-13 the best E that panelists could obtain for tr eatment 1 (2 selections) was 2.92. The actual average values provided by panelists evaluating the screen image was 11.34 with a standard deviation of 9.56, and that for fruit tray was higher at 17.75 and sm aller standard deviation of 6.05. More color selections reduce the E values of the visual evaluati on of mangos. This is shown for E values in case 2 (4 selections) and case 3 (6 selections) in Table 4-13. Figure 4-4. Comparison of E values for 8, 12, and 16 reference colors, 2 selections. 01234567891011 trays 0 10 20 30 40Delta E 8 ref, 2 12 ref, 2 16 ref, 2Mango, abs.DE, real fruit

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50 For mangos, it was apparent that panelists had more difficulty with 12 reference colors and its combinations. Treatment 4 provided panelists with the most challenge of all combinations reflecting a high E value of 21.15 for the screen image and 18.036 for fruit tray. The E values for the combinations with 12 refe rences are higher than the rest of the combinations (Figure 4-4). Table 4-14. Summary performance for pane lists evaluating nectarines for booth 1. Case Best E Screen E Stdev Tray E Stdev 1* 1.34 9.148 4.788 12.970 8.376 2* 2.82 7.410 2.789 11.314 3.515 3* 0.2 7.610 2.724 10.089 2.376 4* 1.34 25.728 14.359 20.020 14.592 5* 0.2 13.545 9.165 11.070 1.256 6* 0.2 8.770 1.822 10.615 4.044 7* 1.34 13.271 11.203 12.658 1.423 8* 0.2 9.950 6.329 12.572 3.761 9* 0.2 14.134 10.592 10.566 4.915 In Table 4-14 the best E that panelists could obtain for treatment 1 was 1.34. The actual average value provided by panelists for the screen image of a nectarine was 9.15 with a standard deviation of 4.79, that for fruit tray was 12.97 and smaller standard deviation of 8.38. In general, the E values of nectarines were higher than those of mangos. Statistical Analysis The data for the sensory panel was analyzed for sources of variation such as number of reference colors, selection of colors and its interactions, presentation: screen image or fruit tray, and its interactions with references and sele ctions, using two differe nt models: Mixed model results, and ANOVA analysis of variance. For the statistical analysis the difference in E was also used as our dependent variable because this provided a more realistic number due to the best possible outcome by panelists given the combinations of reference colors and selection of colors.

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51 The difference in E was the value obtained from subtracting best E from absolute E. Both models provided similar data. The data was separately analyzed for mangos and nectarines. Mangos The analysis of variance showed significant differences between the number of reference colors, the number of selections, presentation an d the interaction between the reference colors and the number of selections, both for the E absolute and difference in E (p-value=0.0001) as shown in Tables 4-15 and 4-16. The mixed mode analysis also reported these same significant differences as shown in Tables E-1 and E-2. The rest of the interactions did not result in significant differences for reference color and pr esentation, selections and presentation and reference colors, selections of colors and presentation. Table 4-15. ANOVA summary absolute E for mangos. Source DF* ANOVA SS* MEAN Square F Value Pr >F Ref. colors 2 2100.72 1050.36 22.47 < .0001 Selections 2 4410.37 2205.18 47.18 < .0001 Presentation 1 10.55.36 1055.36 22.58 < .0001 Ref. colors selections 4 1948.59 487.15 10.42 < .0001 Ref. colors* presentation 2 189.06 94.53 2.02 0.133 Selections*presentation 2 136.41 68.21 1.46 0.233 Ref. colors*selections*presentation 4 65.66 16.41 0.35 0.843 Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares. Table 4-16. ANOVA summary difference E for mangos. Source DF ANOVA SS MEAN Square F Value Pr >F Ref. colors 2 2602.76 1301.38 27.73 < .0001 Selections 2 1356.82 678.41 14.46 < .0001 Presentation 1 1055.28 1055.28 22.49 < .0001 Ref. colors selections 4 1769.15 442.29 9.42 < .0001 Ref. colors* presentation 2 189.43 94.53 2.01 0.134 Selections*presentation 2 136.43 68.21 1.45 0.234 Ref. colors*selections*presentation 4 65.66 16.41 0.35 0.844 Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares.

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52 It is apparent that the error means for color selections, 2 color choices had the highest mean and was significantly different than the rest as shown in Figure 4-4 and 45. Panelists tend to make more errors when selecting only 2 colors The error decreases and panelists become more efficient with more color choices. The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 3 Critical Range .8657 .9115 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 15.2401 480 2 B 11.9144 480 4 B 11.2349 480 6 Figure 4-4. Absolute E means difference of selections of colors using mangos. The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 3 Critical Range .8674 .9133 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 13.1554 480 2 B 11.1637 480 4 B 11.0349 480 6 Figure 4-5. Difference in E means difference of selecti ons of colors using mangos. Because of this, the rest of th e color selections, 4 and 6 choices had mean values showing no significant difference between each other for both E absolute and difference in E. This same pattern was seen using the mixed mode for statistical analysis and is shown in Figures E-1 and E-2.

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53 It was also apparent that the 8 reference colors provi ded less error both for the E absolute and difference in E as shown in Figures 4-6 and 4-7. The highest error made by panelists was with 12 reference colors compared to 8 and 16. However, nectarines reported slightly higher error values than mangos. This may be due to the higher non-uniformity of nectarines making the evaluations harder for panelists. The same resu lts were obtained using the mixed mode as shown in Figures E-3 and E-4. The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 3 Critical Range .8657 .9115 Means with the same letter are not significantly different. Duncan Grouping Mean N Reference Colors A 14.3974 480 12 B 12.5116 480 16 C 11.4803 480 8 Figure 4-6. Absolute E means for reference colors for mangos. The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 3 Critical Range .8674 .9133 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 13.4795 480 12 B 11.6834 480 16 C 10.1910 480 8 Figure 4-7. Difference in E Means for reference colors for mangos. The interaction between the number of referen ce colors and the number of color selections also reported significant differences. The highe st error made by panelists was when evaluating treatment 4 or 12 reference colors and 2 color c hoices as shown in Figur es 4-8 and 4-9 both for absolute E and difference in E.

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54 The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.45812 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.511 1.591 1.644 1.684 1.715 1.740 1.761 1.779 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 19.1266 160 4 B 13.9341 160 7 C B 12.6597 160 1 C B 12.4915 160 5 C D 11.8113 160 9 C D 11.7896 160 8 C D 11.5741 160 6 C D 11.4620 160 2 D 10.3192 160 3 Figure 4-8. Absolute E means for interaction between the nu mber of reference colors and the number of selections. The lowest error made by panelists was with treatment 3 or 8 reference colors and 6 selections both for absolute E and difference in E. The rest of the treatments were slightly different however, providing significa nt differences. It is clear that the more color selections, the less error made by the panelists. It is possible that up to certain le vel of reference colors panelists would perform more efficiently, and above that level it would t oo complicated for the panelists to refer to color selections and reference colors There may be an optimum number of reference colors.

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55 The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.64525 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.514 1.594 1.647 1.687 1.718 1.743 1.765 1.783 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 17.0419 160 4 B 12.0225 160 5 B 11.8493 160 7 C B 11.6113 160 9 C B 11.5896 160 8 C B D 11.3741 160 6 C B D 10.5750 160 1 C D 10.1192 160 3 D 9.8789 160 2 Figure 4-9. Difference in E means for interaction between th e number of reference colors and the number of selections. The presentation (screen image vs. fruit tr ay) was also significantly different (pvalue=0.0001). The fruit tray had mean values higher than the screen image both for absolute E and difference in E as shown in Figures 4-10 and 4-11. These same results were obtained using the mixed mode as shown in Figures E-5 and E-6. The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.74127 Number of Means 2 Critical Range .7068 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 13.6525 720 F B 11.9404 720 S Figure 4-10. Absolute E means for presentation for mangos.

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56 The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 46.92962 Number of Means 2 Critical Range .7083 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 12.6407 720 F B 10.9286 720 S Figure 4-11. Difference E means for presentation for mangos. Nectarines The analysis of variance resulted in significant differences between reference colors, the number of selections, presentation and the interaction between the reference colors and the selection of colors for both the E absolute and the difference in E (p-value = 0.0001) as shown in Tables 4-17 and 4-18. Table 4-17. ANOVA summary absolute E for nectarines. Source DF ANOVA SS MEAN Square F Value Pr >F Ref. colors 2 2827.38 1413.69 30.00 < .0001 Selections 2 7657.60 3828.80 81.25 < .0001 Presentation 1 761.75 761.74 16.16 < .0001 Ref. colors selections 4 3850.96 962.74 20.43 < .0001 Ref. colors* presentation 2 154.12 77.06 1.64 0.195 Selections*presentation 2 122.78 61.39 1.30 0.272 Ref. colors*selections*presentation 4 238.36 59.59 1.26 0.282 Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares. Similar to mangos, the interactions for refere nce color and presentation, selections and presentation and reference colors, selections of colors and presenta tion did not result in significant differences. These same results were obtained using the mixed mode as shown in Tables E-3 and E-4.

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57 The rest of the interactions did not result in significant differences for reference color and presentation, selections and pr esentation and reference colors, selections of colors and presentation. Table 4-18. ANOVA su mmary difference in E for nectarines. Source DF ANOVA SS MEAN Square F Value Pr >F Ref. colors 2 6968.87 3484.44 75.76 < .0001 Selections 2 1903.92 951.96 20.70 < .0001 Presentation 1 761.82 761.82 16.56 < .0001 Ref. colors selections 4 4565.35 1141.34 24.82 < .0001 Ref. colors* presentation 2 154.10 77.05 1.68 0.188 Selections*presentation 2 122.79 61.39 1.33 0.264 Ref. colors*selections*presenta tion 4 238.38 59.59 1.30 0.269 Ref. refers to reference. DF refers to degrees of freedom. SS refers to the sum of squares. The same pattern seen previously with mangos were 8 reference colors reported the lowest value and 12 reference colors the highest value as shown in Figures 4-12 and 4-13 both for the absolute E and the difference in E. Similar results were obtained using the mixed mode as shown in Figures E-9 and E-10. The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 3 Critical Range .8692 .9152 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 14.9077 480 12 B 13.0530 480 16 C 11.4792 480 8 Figure 4-12. Absolute E means for reference colors for nectarines.

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58 The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 3 Critical Range .8587 .9041 Means with the same letter are not significantly different. Reference_ Duncan Grouping Mean N Colors A 13.6839 480 12 B 11.7746 480 16 C 8.3653 480 8 Figure 4-13. Difference E means for reference colors for nectarines. The number of selections was also significantl y different with (p-value= 0.0001). However, when looking at the means for color selections, 2 color choices had the highest mean of the rest of the color selections, 4 and 6 as shown in Fi gure 4-14 and there were no significant difference between 4 and 6 color selections of Difference in E as shown in Figure 4-15, the same case as the mangos. Similar results were obtained with the mixe d mode procedures as seen in Figures E-7 and E-8. The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 3 Critical Range .8692 .9152 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 16.3154 480 2 B 12.2299 480 4 C 10.8946 480 6 Figure 4-14. Absolute E means for selection of colors for nectarine.

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59 The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 3 Critical Range .8587 .9041 Means with the same letter are not significantly different. Duncan Grouping Mean N Selections A 12.8803 480 2 B 10.6946 480 6 B 10.2490 480 4 Figure 4-15. Difference E means for selections of colors for nectarines. The interaction between reference colors and number of selection of colors also resulted in significant differences w ith (p-value = 0.0001). The ANOVA Procedure Duncan's Multiple Range Test for E Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 47.72086 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.515 1.595 1.649 1.688 1.719 1.745 1.766 1.784 Means with the same letter are no t significantly different. Duncan Grouping Mean N TRT A 21.2744 160 4 B 14.9965 160 7 C 12.8476 160 5 C 12.6753 160 1 C 12.4578 160 8 D C 11.7046 160 9 D C 11.3842 160 2 D 10.6010 160 6 D 10.3782 160 3 Figure 4-16. Difference E means for selections of colors for nectarines. For both the absolute E and difference in E treatment 4 or 12 reference colors and 2 color choices had the highest value as shown in Figur es 4-16 and 4-17. Similar to mangos, panelists had the most difficulty in matching 12 reference colors and 2 color selections. Panelist also had difficulty in evaluating treatment 7 or 16 reference colors and 2 co lor selections reported as the second highest mean error for absolute E. Panelists performed be st and reported the lowest error value for difference in E with treatment 2 or 8 reference colors and 4 color selections as shown in Figure 4-16.

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60 The ANOVA Procedure Duncan's Multiple Range Test for Diff E Alpha 0.05 Error Degrees of Freedom 1431 Error Mean Square 46.59453 Number of Means 2 3 4 5 6 7 8 9 Critical Range 1.497 1.576 1.629 1.668 1.699 1.724 1.745 1.763 Means with the same letter are not significantly different. Duncan Grouping Mean N TRT A 17.8393 160 4 B 12.8114 160 5 B 12.2578 160 8 C B 11.5613 160 7 C B 11.5046 160 9 C D 10.4010 160 6 C D 10.1782 160 3 D 9.2402 160 1 E 5.6777 160 2 Figure 4-17. Difference E means for selections of colors for nectarines. The presentation (screen image vs. fruit tr ay) was also significantly different (pvalue=0.0001). The fruit tray had mean values hi gher than the screen image as shown in Figures 4-18 and 4-19 for both E and Difference in E. Similar results were obtained using the mixed mode as shown in Figures E-11 and E-12. The ANOVA Procedure Duncan's Multiple Range Test for Delta_E Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 47.12485 Number of Means 2 Critical Range .7097 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 13.8739 720 F B 12.4193 720 S Figure 4-18. Absolute E means for presentation for nectarines.

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61 The ANOVA Procedure Duncan's Multiple Range Test for DiffDE Alpha 0.05 Error Degrees of Freedom 1422 Error Mean Square 45.99134 Number of Means 2 Critical Range .7011 Means with the same letter are not significantly different. Duncan Grouping Mean N Source A 12.0020 720 F B 10.5473 720 S Figure 4-19. Difference E means for presentation for nectarines. E vs. CCI The number of primitives, number of nei ghbors, and CCI measure the degree of nonuniformity of color. E gave the difference between MV and panelists color appraisal. The correlation between E and non-uniformity (CCI) is shown in Figures C-1 to C-12. The E for nectarines obtained from screen images and co mbinations of references of colors and color selection did not correlate well with the CCI values as shown in Figures C-1 to C-3. The same results were reported for E obtained from fruit trays as shown in Figures C-4 to C-6. This was the same pattern for mangos in Figures C-7 to C-12. The number of reference color or the number of selections of colors did not provide any information in regard to a correlation between E and CCI. The color change index or measure of non-uniformity did not have a relationship wi th the panelists perfor mance and error made when visually evaluating mangos and nectarines. It is possible that above a certain degree of non-uniformity of colors panelists performance is reduced and becomes too complicated to refer to color bars.

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62 CHAPTER 5 CONCLUSIONS Colors reflect important quality parameters such as maturity, defects and other colordependent attributes. Most agricultural materi als e.g. fruits, vegetables, grains, meat, and seafood have non-uniform shapes, surfaces and colo rs. It is important to quantify the color attributes of these materials in order to measure their quality and to help with the record keeping associated with the new globalization requirements. The number of color blocks did not provi de a clear measure of the degree of nonuniformity in mangoes and nectarines. However, the number of color primitives associated with color change index and number of neighbors does provide a bette r measure of their degree of non-uniformity. Quantitative color data can be correlated with human perception. The method developed in this study can be used to quantify the perceptions of unt rained panelists regarding nonuniformly colored foods, with objective error m easurements to optimize the method parameters. It was observed that there may be an optimum nu mber of reference colors for a given food. In our study, 12 reference colors performed poorly compared to either 8 or 16 reference colors. Since all of the 8 reference colors were present in the set of 12 reference colors, one may argue that increasing the number from 8 to 12 diluted their effect. This needs to be tested in future studies. It is more difficult to explain why 16 reference colo rs performed better than 12 references. Future studies may explore this dilemma. It was clear that the more color selections, th e less the error made by the panelists, given the reference colors provided in this study. Th ere was also statistically significant interaction between the number of reference colors and the nu mber of selections. It is possible that up to certain level of reference colors panelists would perform more efficiently, and above that level it

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63 would be too complicated for the pa nelists to refer to color selections and reference colors. This issue needs to be elucidated in future studies. In this study there was no correlation between the error performance of panelists and the degree of non-uniformity provided by the number of primitives. The concept of the minimum possible performance level was introduced, the best possible E. This provided a more realistic way to calculate the error made by sensory panels given a number of reference colors and color selections. Panelists also evaluated the color of the same sa mple either by looking at its image, or at a real fruit. This study found sma ll but statistically significant di fferences in the error made by panelists between these sources. It is interesting, but expected that the error made when looking at the image was less, since the reference colo rs were developed from the images. Specific studies in the future need to clarify if images can be substituted for the real food, for visual evaluation purposes. It is essential to keep identifying criteria to measure the visual evaluations of panelists and their correlations with instrumental methods of color measurements, to provide a better understanding to the human perception of non-unifo rm colors. The search for better methods to quantify and correlate instrumental and human pe rception data in this area should continue.

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64 APPENDIX A COLOR ANALYSIS FOR ALL TRAYS Figure A-1. Fruit Tray booth 1 for image acquisition and sensory panel. Figure A-2. Fruit Tray booth 2 for image acquisition and sensory panel.

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65 Figure A-3. Fruit Tray booth 3 for image acquisition and sensory panel. Figure A-4. Fruit Tray booth 4 for image acquisition and sensory panel.

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66 Figure A-5. Fruit Tray booth for im age acquisition and sensory panel. Figure A-6. Fruit Tray booth 6 for image acquisition and sensory panel.

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67 Figure A-7. Fruit Tray booth 7 for image acquisition and sensory panel. Figure A-8. Fruit Tray booth 8 for image acquisition and sensory panel.

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68 Figure A-9. Fruit Tray booth 9 for image acquisition and sensory panel. Figure A-10. Fruit Tray booth 10 for image acquisition and sensory panel.

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69 Figure A-11. Machine Vision set-up. Figure A-12. Light box specifications. 88 cm 46 cm 51 cm 50 cm

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70 08 11 01 03 04 05 06 07 12 02 09 10 06 01 03 04 05 08 02 07 01 11 14 02 04 05 06 07 08 09 10 15 16 03 12 13 8 References 12 References 16 References Figure A-13. Reference scales presented to panelists. The main reference color bar was that with 16 colors. From that color bar, color blocks with close/similar L*a*b or RGB values were me rged to reduce the number of colors to choose and create a color bar with 12 reference colors. The same procedure was used to create the color reference scale with 8 references colors. Table A-1. L*a*b values for refe rence color bar with 8 color. Reference color L* a* b* 1 59.14 -5.61 58.53 2 32.97 56.6 43.38 3 39.89 53.77 34.18 4 52.32 21.89 47.08 5 60.95 2.83 55.43 6 41.77 62.53 46.01 7 71.07 17.04 60.64 8 75.28 7.85 64.73

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71 Table A-2. L*a*b values for re ference color bar with 12 colors. Reference color L* a* b* 1 59.14 -5.61 58.53 2 32.97 56.6 43.38 3 39.89 53.77 34.18 4 42.15 47.55 36.62 5 52.32 21.89 47.08 6 60.95 2.83 55.43 7 45.33 53.39 40.92 8 41.77 62.53 46.01 9 67.41 28.11 40.77 10 71.07 17.04 60.64 11 79.56 -1.8 74.03 12 75.28 7.85 64.73 Table. A-3 L*a*b values for refe rence color bar with 16 colors. Reference color L* a* b* 1 50.01 13.55 50.72 2 59.14 -5.61 58.53 3 32.97 56.6 43.38 4 39.89 53.77 34.18 5 42.15 47.55 36.62 6 52.32 21.89 47.08 7 56.51 12.35 51.18 8 60.95 2.83 55.43 9 45.33 53.39 40.92 10 47.94 46.34 43.57 11 41.77 62.53 46.01 12 67.41 28.11 40.77 13 71.07 17.04 60.64 14 79.56 -1.8 74.03 15 75.28 7.85 64.73 16 90.35 -9.87 67.25

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72 Figure A-14. Example ballot for screen image evaluation. (Fruit Image Evaluation Form) Sensory color evaluation form Date Panelist Age: Male Female Booth number Instructions: 1. Do not re-orient the sample s, or modify their wrapping. 2. Evaluate the samples using the order given here. 3. From the screen, select only 2 colors that best represent the colors of the sample. 4. Estimate the percentage of these colo rs for the surface of the sample shown. 5. The sum of the 2 percentages must add to 100% Sample number (541) Color number (1 to 8) Percent of total area Sum=100% Sample number (397) Color number (1 to 8) Percent of total area Sum=100%

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73 Figure A-15. Example ballo t for fruit evaluation. (Fruit Tray Evaluation Form) Sensory color evaluation form Date Panelist Age: Male Female Booth number Instructions: 1. Do not re-orient the sample s, or modify their wrapping. 2. Evaluate the samples using the order given here. 3. From the screen, select only 2 colors that best represent the colors of the sample. 4. Estimate the percentage of these colo rs for the surface of the sample shown. 5. The sum of the 2 percentages must add to 100% Sample number (397) Color number (1 to 8) Percent of total area Sum=100% Sample number (541) Color number (1 to 8) Percent of total area Sum=100% Table A-4. Mango color primitives. Booth CCI # Primitives # Neighbors 1 6.42 616 1190 2 9.21 613 1239 3 5.58 513 982 4 4.90 548 1012 5 6.96 593 1127 6 7.16 615 1190 7 6.76 609 1196 8 4.33 538 964 9 4.58 536 1016 10 11.29 738 1511

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74 Table A-5. Nectarine color primitives. Booth CCI # Primitives # Neighbors 1 17.51 915 1760 2 13.72 693 1366 3 13.13 581 1196 4 13.20 794 1521 5 11.32 626 1215 6 14.23 729 1491 7 17.75 856 1648 8 15.19 756 1488 9 11.49 739 1414 10 14.76 773 1566 Figure A-16. Representation of color primitives and equivalent circles for mangos (left) and nectarines (right) wi th a MV system.

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75 APPENDIX B PANELISTS PERFORMANCE FOR MANGOS AND NECTARINES 10. Treatment 1 = 8 reference co lor and 2 color selections 11. Treatment 2 = 8 reference colo rs and 4 color selections 12. Treatment 3 = 8 reference colo rs and 6 color selections 13. Treatment = 12 reference colo r and 2 color selections 14. Treatment 5 = 12 reference colors and 4 color selections 15. Treatment 6 = 12 reference colors and 6 color selections 16. Treatment 7 = 16 reference colors and 2 color selections 17. Treatment 8 = 16 reference colors and 4 color selections 18. Treatment 9 = 16 reference colors and 6 color selections Table B-1. Summary performance for pane lists evaluating both fruits for booth 1. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 1.34 9.15 4.79 12.97 8.38 2.92 11.39 9.56 15.75 6.05 2* 2.82 7.41 2.79 11.31 3.52 2.79 10.70 5.16 10.62 2.30 3* 0.20 7.61 2.72 10.09 2.38 0.20 9.49 4.04 10.52 5.41 4* 1.34 25.73 14.36 20.02 14.59 2.92 21.15 7.64 18.04 8.86 5* 0.20 13.54 9.16 11.07 1.26 0.20 13.53 7.79 12.84 3.01 6* 0.20 8.77 1.82 10.61 4.04 0.20 13.87 8.26 12.73 4.74 7* 1.34 13.27 11.20 12.66 1.42 2.92 15.20 10.29 14.93 4.89 8* 0.20 9.95 6.33 12.57 3.76 0.20 13.97 9.51 11.34 1.86 9* 0.20 14.13 10.59 10.57 4.92 0.20 13.91 5.91 6.40 6.40

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76 Table B-2. Summary performance for pane lists evaluating both fruits for booth 2. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 0.90 11.91 5.20 9.46 3.55 1.90 11.04 7.00 15.36 5.87 2* 2.56 6.36 3.27 10.02 1.88 1.38 9.15 5.23 10.02 4.12 3* 0.20 10.44 9.88 6.10 1.35 0.20 10.51 5.77 11.13 3.95 4* 0.91 14.93 7.46 21.29 16.93 1.90 15.75 6.93 20.84 7.87 5* 0.20 8.73 2.85 11.01 2.34 0.20 9.53 3.63 10.97 4.69 6* 0.19 6.09 3.49 10.23 4.23 1.90 8.78 5.48 11.73 5.00 7* 1.34 13.36 4.59 13.09 4.79 2.92 15.50 8.52 15.15 7.15 8* 0.20 9.19 1.31 11.63 4.17 0.20 8.60 8.60 13.50 6.62 9* 0.20 12.49 7.31 10.77 2.24 0.20 8.60 3.01 12.10 12.10 Table B-3. Summary performance for pane lists evaluating both fruits for booth 3. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 1.90 9.15 4.72 7.69 3.14 2.40 10.72 6.26 10.85 1.46 2* 2.13 7.94 4.46 8.67 3.54 0.18 9.15 5.23 10.02 4.12 3* 0.20 5.39 2.81 5.73 2.10 0.20 10.59 5.09 8.34 4.48 4* 1.89 20.09 14.33 16.52 12.38 2.41 16.55 11.69 9.90 3.52 5* 0.20 14.67 8.71 10.68 8.71 0.20 12.31 7.82 12.16 4.68 6* 0.20 7.60 2.82 7.35 4.61 0.20 7.87 4.08 7.54 3.42 7* 1.90 9.79 5.82 8.58 3.40 2.41 11.48 6.38 15.47 7.19 8* 0.20 12.74 8.25 12.43 8.25 0.20 7.82 5.86 10.28 2.63 9* 0.20 7.79 3.97 7.38 3.66 0.20 9.46 8.39 9.69 9.69 Table B-4. Summary performance for pane lists evaluating both fruits for booth 4. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 4.66 11.91 7.34 14.87 4.70 2.59 7.19 4.71 15.59 5.49 2* 5.93 14.46 6.65 13.98 4.70 2.58 11.44 3.71 13.31 3.15 3* 0.20 14.16 9.21 12.78 2.07 0.20 12.05 7.14 15.14 3.94 4* 4.66 28.17 11.16 14.58 10.72 2.59 19.67 8.85 23.81 9.87 5* 0.20 14.08 7.71 14.04 3.64 2.35 10.76 5.43 18.19 6.04 6* 0.20 10.45 5.21 13.18 1.82 0.20 11.58 5.65 11.03 6.09 7* 4.66 14.66 7.97 17.08 7.40 2.59 11.06 5.31 18.05 9.57 8* 0.20 13.44 6.36 16.89 6.36 0.20 12.74 7.01 18.41 2.38 9* 0.20 9.14 3.68 17.39 7.27 0.20 11.33 4.93 15.09 15.09

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77 Table B-5. Summary performance for pane lists evaluating both fruits for booth 5. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 2.65 10.23 5.71 9.92 2.64 1.93 11.98 6.53 19.10 5.68 2* 8.81 11.77 10.50 11.75 7.95 1.79 12.94 5.21 11.84 5.07 3* 0.20 6.44 2.54 11.61 6.57 0.20 9.26 4.48 12.30 4.49 4* 2.65 15.11 3.07 13.43 5.00 1.93 22.01 9.40 21.45 7.65 5* 0.20 11.93 5.84 17.36 8.56 1.72 12.30 6.52 15.76 5.79 6* 0.20 6.76 2.52 13.84 6.81 0.20 10.32 4.37 15.52 4.24 7* 2.65 15.25 9.75 18.36 7.43 1.93 12.07 2.42 17.39 7.39 8* 0.20 14.04 8.55 13.15 8.55 0.20 15.73 10.68 14.64 3.05 9* 0.20 9.71 4.51 14.16 5.20 0.20 13.78 4.57 16.04 16.04 Table B-6. Summary performance for pane lists evaluating both fruits for booth 6. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 1.27 13.24 6.92 16.02 9.33 2.67 13.15 7.69 19.44 10.38 2* 6.85 11.48 6.36 12.39 4.91 2.99 11.07 5.56 15.20 8.04 3* 0.20 13.13 6.10 11.55 3.20 0.20 8.48 2.84 10.01 3.32 4* 1.27 20.59 5.85 25.95 12.12 2.67 16.86 4.54 21.79 7.02 5* 0.20 7.17 2.76 8.59 2.03 0.20 9.50 3.97 11.22 4.53 6* 0.20 7.28 1.76 7.30 4.76 0.20 11.58 5.65 11.03 6.09 7* 1.27 12.37 8.48 10.63 2.21 2.67 12.07 2.42 17.39 7.39 8* 0.20 9.02 3.37 8.57 3.37 0.20 12.79 5.06 12.15 3.88 9* 0.20 8.29 2.40 9.63 3.65 0.20 10.84 6.06 10.17 10.17 Table B-7. Summary performance for pane lists evaluating both fruits for booth 7. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 8.67 13.75 8.90 17.46 9.89 2.19 12.66 9.72 11.80 2.94 2* 11.59 15.28 5.80 15.94 9.54 0.66 13.27 6.68 9.00 2.91 3* 0.20 12.70 3.43 13.13 2.18 0.20 5.19 1.97 8.21 0.92 4* 8.67 23.78 7.06 22.47 8.77 2.19 17.21 17.44 15.84 16.89 5* 0.20 16.34 8.74 18.53 5.54 0.20 13.03 11.79 12.28 10.80 6* 0.20 14.43 7.37 18.62 7.86 0.20 11.98 9.70 11.88 6.16 7* 8.67 13.29 5.03 16.91 7.01 2.19 11.54 5.54 11.78 2.80 8* 0.20 18.12 8.37 16.39 8.37 0.20 8.02 4.94 11.22 5.19 9* 0.20 11.93 5.78 16.85 4.13 0.20 10.48 12.83 11.30 11.30

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78 Table B-8. Summary performance for pane lists evaluating both fruits for booth 8. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 1.25 11.28 9.21 15.52 5.38 0.83 13.25 11.45 13.42 4.07 2* 3.69 7.14 4.47 9.59 2.15 0.69 9.47 4.16 12.05 2.69 3* 0.20 7.68 3.08 9.73 3.76 0.20 10.39 3.14 10.90 3.80 4* 1.25 21.03 13.05 24.14 16.85 0.83 23.58 15.44 11.13 12.01 5* 0.20 9.49 5.06 11.96 2.62 0.20 14.69 10.29 12.15 3.50 6* 0.20 11.31 7.19 8.95 3.14 0.20 12.44 7.46 10.87 3.56 7* 1.25 16.38 6.68 20.19 7.65 0.83 9.87 4.58 13.43 7.18 8* 0.20 4.86 2.30 13.65 2.30 0.20 11.92 5.69 13.10 3.36 9* 0.20 8.69 3.74 14.58 4.33 0.20 11.44 8.81 13.76 13.76 Table B-9. Summary performance for pane lists evaluating both fruits for booth 9. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 4.65 8.95 6.12 12.86 3.66 0.61 8.77 7.13 12.83 3.58 2* 5.45 10.38 4.01 14.28 7.35 0.59 10.69 4.37 13.70 4.18 3* 0.20 8.17 2.08 11.40 3.30 0.20 11.47 3.66 10.53 2.95 4* 4.65 24.19 10.96 19.32 13.82 0.61 18.88 11.63 35.88 15.71 5* 0.20 13.35 4.51 15.40 4.91 0.20 13.01 4.73 13.59 4.26 6* 0.20 12.02 3.60 11.06 2.69 0.20 13.01 4.73 13.59 4.26 7* 4.65 13.07 5.34 21.78 8.71 0.61 10.87 7.18 11.36 4.55 8* 0.20 12.68 6.82 13.33 6.82 0.20 11.08 7.83 11.88 4.45 9* 0.20 11.07 3.57 14.15 4.07 0.20 10.49 5.53 16.38 16.38 Table B-10. Summary performance for pane lists evaluating both fruits for booth 10. Nectarine Mango Case Best E Screen E Stdev Tray E Stdev Best E Screen E Stdev Tray E Stdev 1* 7.08 9.23 6.05 17.59 4.57 2.80 7.27 4.89 11.59 3.74 2* 7.22 13.13 5.15 15.19 4.93 2.18 10.13 9.04 12.65 5.37 3* 0.20 12.72 3.53 17.77 5.69 0.20 9.00 4.78 12.68 6.86 4* 7.10 27.25 10.32 26.93 10.90 2.80 14.39 8.22 17.80 13.21 5* 0.20 12.79 3.99 15.93 2.94 0.20 15.16 6.04 11.19 3.60 6* 0.20 9.58 2.65 15.96 5.15 0.20 9.93 4.79 10.14 3.01 7* 7.08 18.04 5.65 21.21 7.77 2.80 12.83 7.72 16.43 5.28 8* 0.20 10.86 4.36 14.97 4.36 0.20 5.99 1.54 10.66 2.71 9* 0.20 9.92 5.22 15.42 3.01 0.20 7.18 2.59 10.91 10.91

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79 APPENDIX C E VS CCI FOR ALL COMBINATIONS 111213141516171819 CCI 0 10 20 30 40Delta E 8 ref, 2 8 ref, 4 8 ref, 6Nectarine, abs.DE, screen Figure C-1. Absolute E for nectarine for screen image and 8 references. 111213141516171819 CCI 0 10 20 30 40Delta E 12 ref, 2 12 ref, 4 12 ref 6Nectarine, abs.DE, screen Figure C-2. Absolute E for nectarine for screen image and 12 references.

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80 111213141516171819 CCI 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Nectarine, abs.DE, screen Figure C-3. Absolute E for nectarine for screen image and 16 references. 111213141516171819 CCI 0 10 20 30 40Delta E 8ref, 2 8 ref, 4 8 ref, 6Nectarine, abs.DE, fruit Figure C-4. Absolute E for nectarine for tray and 8 references.

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81 111213141516171819 CCI 0 10 20 30 40Delta E 12 ef, 2 12 ref, 4 12 ref, 6Nectarine, abs.DE, fruit Figure C-5. Absolute E for nectarine for tray and 12 references. 111213141516171819 CCI 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Nectarine, abs.DE, fruit Figure C-6. Absolute E for nectarine for tray and 16 references.

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82 3456789101112 CCI 0 10 20 30 40Delta E 8ref, 2 8 ref, 4 8 ref, 6Mango, abs.DE, screen Figure C-7. Absolute E for mango for screen image and 8 references. 3456789101112 CCI 0 10 20 30 40Delta E 12 ef, 2 12 ref, 4 12 ref, 6Mango, abs.DE, screen Figure C-8. Absolute E for mango for screen image and 12 references.

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83 3456789101112 CCI 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Mango, abs.DE, screen Figure C-9. Absolute E for mango for screen image and 16 references. 3456789101112 CCI 0 10 20 30 40Delta E 8ref, 2 8 ref, 4 8 ref, 6Mango, abs.DE, fruit Figure C-10. Absolute E for mango for tray 8 references.

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84 3456789101112 CCI 0 10 20 30 40Delta E 12 ef, 2 12 ref, 4 12 ref, 6Mango, abs.DE, fruit Figure C-11. Absolute E for mango for tray and 12 references. 3456789101112 CCI 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Mango, abs.DE, fruit Figure C-12. Absolute E for mango for tray and 16 references.

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85 APPENDIX D DELTA E VALUES FOR DIFFERENT CASES 01234567891011 trays 0 10 20 30 40Delta E 8 ref, 2 8 ref, 4 8 ref, 6Nectrarine, abs.DE, screen Figure D-1. Absolute E for nectarine for screen image and 8 references. 01234567891011 Trays 0 10 20 30 40Delta E 12 ref, 2 12 ref, 4 12 ref 6Nectarine, Abs.DE, screen Figure D-2. Absolute E for nectarine for screen image and 12 references.

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86 01234567891011 Trays 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Nectarine, abs.DE, screen Figure D-3. Absolute E for nectarine for screen image and 16 references. 01234567891011 trays 0 10 20 30 40Delta E 8 ref, 2 8 ref, 4 8 ref, 6Nectarine, abs.DE, real fruit Figure D-4. Absolute E for nectarine for tray and 8 references.

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87 01234567891011 trays 0 10 20 30 40Delta E 12 ref, 2 12 ref, 4 12 ref, 6Nectarine, abs.DE, real fruit Figure D-5. Absolute E for nectarine for tray and 12 references. 01234567891011 trays 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Nectarine, abs.DE, real fruit Figure D-6. Absolute E for nectarine for tray and 16 references.

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88 01234567891011 trays 0 10 20 30 40Delta E 8 ref, 2 8 ref, 4 8 ref, 6Mango, abs.DE, screen Figure D-7. Absolute E for mango for screen image and 8 references. 01234567891011 trays 0 10 20 30 40Delta E 12 ref, 2 12 ref, 4 12 ref, 6Mango, abs.DE, screen Figure D-8. Absolute E for mango for screen image and 12 references.

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89 01234567891011 trays 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Mango, abs.DE, screen Figure D-9. Absolute E for mango for screen image and 16 references. 01234567891011 trays 0 10 20 30 40Delta E 8 ref, 2 8 ref, 4 8 ref, 6Mango, abs.DE, real fruit Figure D-10. Absolute E for mango for tray 8 references.

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90 01234567891011 trays 0 10 20 30 40Delta E 12 ref, 2 12 ref, 4 12 ref, 6Mango, abs.DE, real fruit Figure D-11. Absolute E for mango for tray and 12 references. 01234567891011 trays 0 10 20 30 40Delta E 16 ref, 2 16 ref, 4 16 ref, 6Mango, abs.DE, real fruit Figure D-12. Absolute E for mango for tray and 16 references.

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91 APPENDIX E SOURCE CODES FOR SAS PROGRAMS Method 1. PROC PRINT DATA=FILE; RUN; proc sort data= File; by Object; proc anova data=file; by Object; class reference_colors selections source panelist booth; model Delta_E = reference_colors|selections|source; means reference_colors selections source reference_colors|selections|source/ duncan ; run; Method 2. proc sort data= File; by object; proc mixed DATA=File; by object; class reference_colors selections source booth panelist; model DiffDE = reference_colors|selections|source; random panelist booth; lsmeans selections|reference_colors|source / pdiff; run; *** The model statement was interchangeable to Diff E or E to statistically analyze both dependent variables. Table E-1. Mixed mode summary absolute E for mangos. Source Num. DF* Den. DF* F Value Pr >F Reference colors 2 1343 23.21 < .0001 Selections 2 1343 48.73 < .0001 Presentation 1 1343 23.32 < .0001 Reference colors selections 4 1343 10.76 < .0001 Reference colors* presentation 2 1343 2.09 0.124 Selections*presentation 2 1343 1.51 0.222 Reference colors*selections*presentation 4 1343 0.36 0.835 Num DF refers to numerator degrees of freedom Den DF refers to denominator degrees of freedom.

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92 Table E-2. Mixed mode summary difference E for mangos. Source Num. DF* Den. DF* F Value Pr >F Reference colors 2 1413 28.57 < .0001 Selections 2 1413 14.90 < .0001 Presentation 1 1413 23.17 < .0001 Reference colors selections 4 1413 9.71 < .0001 Reference colors* presentation 2 1413 2.08 0.126 Selections*presentation 2 1413 1.50 0.224 Reference colors*selections*presentation 4 1413 0.36 0.837 Num DF refers to numerator degrees of freedom Den DF refers to denominator degrees of freedom. Table E-3. Mixed Mode summary absolute E for nectarines. Source Num. DF* Den. DF* F Value Pr >F Reference colors 2 1343 32.84 < .0001 Selections 2 1343 88.95 < .0001 Presentation 1 1343 17.70 < .0001 Reference colors selections 4 1343 22.37 < .0001 Reference colors* presentation 2 1343 1.79 0.167 Selections*presentation 2 1343 1.43 0.241 Reference colors*selections*presentation 4 1343 1.38 0.237 Num DF refers to numerator degrees of freedom Den DF refers to denominator degrees of freedom. Table E-4. Mixed Mode summary difference in E for nectarines. Source Num. DF* Den. DF* F Value Pr >F Reference colors 2 1413 77.58 < .0001 Selections 2 1413 21.20 < .0001 Presentation 1 1413 16.96 < .0001 Reference colors selections 4 1413 25.41 < .0001 Reference colors* presentation 2 1413 1.72 0.126 Selections*presentation 2 1413 1.37 0.224 Reference colors*selections*presentation 4 1413 1.33 0.837 Num DF refers to numerator degrees of freedom Den DF refers to denominator degrees of freedom.

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93 The Mixed Procedure Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F Selections*Source 2 1343 1.51 0.2219 Refere*Select*Source 4 1343 0.36 0.8353 Least Squares Means Reference Standard Effect Source Colors Selections Estimate Error DF t Value Selections 2 15.2401 0.5072 1343 30.05 Selections 4 11.9144 0.5072 1343 23.49 Selections 6 11.2349 0.5072 1343 22.15 Least Squares Means Reference Effect Source Colors Selections Pr > |t| Selections 2 <.0001 Selections 4 <.0001 Selections 6 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 3.3258 0.4342 1343 7.66 <.0001 Selections 2 6 4.0053 0.4342 1343 9.22 <.0001 Selections 4 6 0.6795 0.4342 1343 1.56 0.1179 Figure E-1. Absolute E means for selection of color for mangos. The Mixed Procedure Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F Refere*Select*Source 4 1413 0.36 0.8369 Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Selections 2 13.1554 0.4968 1413 26.48 <.0001 Selections 4 11.1637 0.4968 1413 22.47 <.0001 Selections 6 11.0349 0.4968 1413 22.21 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 1.9917 0.4356 1413 4.57 <.0001 Selections 2 6 2.1205 0.4356 1413 4.87 <.0001 Selections 4 6 0.1288 0.4356 1413 0.30 0.7675 Figure E-2. Difference E Means for selection of color for mangos.

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94 The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 11.4803 0.5072 1343 22.64 <.0001 Reference_Colors 12 14.3974 0.5072 1343 28.39 <.0001 Reference_Colors 16 12.5116 0.5072 1343 24.67 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 2.9171 0.4342 1343 6.72 <.0001 Reference_Colors 8 16 1.0314 0.4342 1343 2.38 0.0177 Reference_Colors 12 16 1.8858 0.4342 1343 4.34 <.0001 Figure E-3. Absolute E means for reference colors for mangos. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 10.1910 0.4968 1343 20.52 <.0001 Reference_Colors 12 13.4795 0.4968 1343 27.14 <.0001 Reference_Colors 16 11.6834 0.4968 1343 23.52 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 3.2885 0.4356 1343 7.55 <.0001 Reference_Colors 8 16 1.4924 0.4356 1343 3.43 0.0006 Reference_Colors 12 16 1.7961 0.4356 1343 4.12 <.0001 Figure E-4. Difference E means for reference colors for mangos. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 13.6525 0.4752 1343 28.73 <.0001 Source S 11.9404 0.4752 1343 25.13 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.7122 0.3546 1343 4.83 <.0001 Figure E-5. Absolute E means for presentation for mangos.

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95 The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 12.6407 0.4638 1343 27.25 <.0001 Source S 10.9286 0.4638 1343 23.56 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.7121 0.3557 1343 4.81 <.0001 Figure E-6. Difference E means for presentation for mangos. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Reference Standard Effect Source Colors Selections Estimate Error DF t Value Selections 2 16.3154 0.7332 1343 22.25 Selections 4 12.2299 0.7332 1343 16.68 Selections 6 10.8946 0.7332 1343 14.86 Least Squares Means Reference Effect Source Colors Selections Pr > |t| Selections 2 <.0001 Selections 4 <.0001 Selections 6 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 4.0855 0.4235 1413 9.65 <.0001 Selections 2 6 5.4208 0.4235 1413 12.80 <.0001 Selections 4 6 1.3353 0.4235 1413 3.15 0.0016 Figure E-7. Absolute E means for selections of colors for nectarines.

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96 The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Selections 2 12.8803 0.4604 1413 27.97 <.0001 Selections 4 10.2490 0.4604 1413 22.26 <.0001 Selections 6 10.6946 0.4604 1413 23.23 <.0001 Differences of Least Squares Means Standard Effect Selections Selections Estimate Error DF t Value Pr > |t| Selections 2 4 2.6313 0.4326 1413 6.08 <.0001 Selections 2 6 2.1857 0.4326 1413 5.05 <.0001 Selections 4 6 0.4456 0.4326 1413 1.03 0.3031 Figure E-8. Difference E means for selections of colors for nectarines. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 11.4792 0.7332 1343 15.66 <.0001 Reference_Colors 12 14.9077 0.7332 1343 20.33 <.0001 Reference_Colors 16 13.0530 0.7332 1343 17.80 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 3.4285 0.4235 1343 8.10 <.0001 Reference_Colors 8 16 1.5737 0.4345 1343 3.72 0.0002 Reference_Colors 12 16 1.8547 0.4345 1343 4.38 <.0001 Figure E-9. Absolute E means for reference colors for nectarines. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 8.3653 0.4604 1343 18.17 <.0001 Reference_Colors 12 13.6839 0.4604 1343 29.72 <.0001 Reference_Colors 16 11.7746 0.4604 1343 25.57 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Reference_Colors 8 12 5.3186 0.4326 1343 12.29 <.0001 Reference_Colors 8 16 3.4092 0.4326 1343 7.88 <.0001 Reference_Colors 12 16 1.9093 0.4326 1343 4.41 <.0001 Figure E-10. Difference E means for reference colors for nectarines.

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97 The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 13.8739 0.7125 1343 19.47 <.0001 Source S 12.4193 0.7125 1343 17.43 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.4546 0.3458 1343 4.21 <.0001 Figure E-11. Absolute E means for presentation for nectarines. The Mixed Procedure Type 3 Tests of Fixed Effects Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F 12.0020 0.4252 1343 28.23 <.0001 Source S 10.5473 0.4252 1343 24.81 <.0001 Differences of Least Squares Means Standard Effect Selections Estimate Error DF t Value Pr > |t| Source F S 1.4547 0.3532 1343 4.12 <.0001 Figure E-12. Difference E means for presentation for nectarines.

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104 Yimyam P, Chalidabhongse T, Sirisomboon P, Boonmung S. Physical Prop erties Analysis of Mango Using Computer Vision. In: The Institute of Control A, and Systems Engineers, editor; 2005 June 2-5; Gyeonggi, Korea. p 111-5. Yoruk R, Yoruk S, Balaban MO, Marshall MR. 2004. Machine Vision Analysis of Antibrowning Potency for Oxalic Acid: A Comparative Investigation on Banana and Apple. Journal of Food Science 69(6):281-9. Zayas IY, Martin CR, Steele JL, Kartsevich A. 1996. Wheat Classification Using Image Analysis and Crush Force Parameters. Transac tions of the ASAE. 39(6), p 2199-204. Zheng C. 2006. Development of novel image segmentation and image feature extraction techniques and their applications for the ev aluation of shrinkage, mo isture content, and texture of cooled large cooked beef joints.P hD. Dissertation. Univer sity College Dublin: National University of Ireland Zhu LG, Brewer MS. 1999. Relationship Between Instrumental and Visual Color In A Raw, Fresh Beef and Chicken Model System Journal of Muscle Foods 10:131-46. Zion B, Shklyar A, Karplus I. 1999. Sorti ng Fish by Computer Vision. Computers and Electronics in Agricu lture 23(3):175-87. Zuiga-Arias, Ruben R. 2007. Variability in Qu ality and Management Practices in the Mango Supply Chain from Costa Rica. Available from: http://ageconsearch.umn.edu/ bitstream /123456789/27260/1/sp07zu01.pdf Accessed 09-16-2007

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105 BIOGRAPHICAL SKETCH Jose Aparicio was born in San Pedro Sula, Honduras. He started college in Honduras and transferred to the University of Florida in 2003 where he obtained hi s B.S. in dairy industry. In 2005 he gained admission to the University of Flor ida graduate school to work on his M.S. in the food science program under Dr. Murat Balabans supervision. He completed his degree in Fall 2007.


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