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dissertation entitled

METHODOLOGY TO ASSESS QUALITY OF FRESH-CUT
FRUIT, AS AFFECTED BY PACKAGE DESIGN, SIZE OF
FRUIT DICE AND TRANSPORTATION

presented by

KOUSHIK SAHA

has been accepted towards fulfillment
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Ph.D degree in Packaging

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METHODOLOGY TO ASSESS QUALITY OF FRESH-CUT FRUIT, AS
AFFECTED BY PACKAGE DESIGN, SIZE OF FRUIT DICE AND
TRANSPORTATION

By

Koushik Saha

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Packaging

2010

ABSTRACT

METHODOLOGY TO ASSESS QUALITY OF FRESH-CUT FRUIT, AS
AFFECTED BY PACKAGE DESIGN, SIZE OF FRUIT DICE AND
TRANSPORTATION

By

Koushik Saha

Fresh produce sales boosted from $3.3 billion in 1994 to $11 billion in
2005. The Fresh-cut Fruit (FCF) industry currently accounts for approximately a
$1 billion. Once fruits are harvested and undergo a cutting process, there is a
loss in quality. The loss in quality is primarily due to water loss, respiration,
ripening, enzymatic discoloration of cut surfaces, microbial degradation and
mechanical damage. Therefore, the success and expansion of fresh-cut fruit
quality will be dependent on continual marketing of quality products. Several
postharvest and post cutting techniques are implemented in combination to
maintain quality of FCF during storage. The most common form of packaging
used in the FCF industry currently is rigid containers made from
Polyethyleneterephthalate (PET) and Oriented Polystyrene (OPS). Rigid
containers come in different shapes and sizes, depending on the serving size
and utility (on-the-go, club store quantitY); fresh-cut fruit can be packaged
accordingly. It is known that during the cutting operation and transportation,
surface tissues get wounded making FCF highly susceptible to sensorial and
physiological degradation compared to whole fruits. Therefore, while preparing

fresh-cut fruits it is necessary to consider the dice size (cube size) and rigid

container design for packaging.This study aims to evaluate quality of fresh-cut
fruit as affected by container dimensions, size of fresh-cut fruit and
transportation.

Locally available whole cantaloupe (Cucumis melo) was used to prepare
fresh-cut fruit. Cantaloupes were cut into 2.5 and 1.5 cm cubes following proper
sanitization and post out treatment. FCF was packaged in three PET containers
designated as ‘Container A', ‘Container 8’ and ‘Container C’ of varying
dimensions. These containers were subjected to random vibration spectrum for
60 and 120 minutes described in ASTM D4169 for Assurance Level II. A 6-
member trained panel evaluated melons on a 1-15 unstructured scale for aroma,
color, sweetness, texture and overall quality at days 1, 4, 7 and 10. Total soluble
solids, headspace gas composition (02 and 002), color CIE L*, a* & b* values,
flesh firmness and olfactory response of an electronic nose, were determined at
Days 1, 4, 7 and 10. The change in color and texture properties were attributed
to vibration movement observed by the FCF. Fresh-cut fruit prepared to a cube
size of 2.5cm and packaged in “Container 8’ showed best sensory evaluated fruit
quality and minimal mechanical damage. Longer vibration test times representing
longer shipping distances produce a significantly higher release of aroma (E-
Nose) in FCF after controlled refrigerated storage. The longer shipped fruit was
therefore was rated lower by the sensory panel due to both loss of texture and
excessive release of off odors. It should be noted that a different fruit size may
have a better result in a different container shape. The way to determine this

would be by conducting a series of tests as described in this research study.

This dissertation is dedicated to

My parents Krishna Lal Saha and Chhanda Saha, and my brother
Angshuman Saha

ACKNOWLEDGEMENTS

I am very grateful to my major advisor and mentor Dr. Paul Singh for
providing me the financial assistance to let me pursue my Ph.d program. He has
been a constant motivator to achieve higher goals and a beacon of
encouragement during my tenure as a graduate student. I would like to thank him
further for giving me the opportunity to be involved in research projects, in
addition to my doctoral project. It has enabled me to nurture a better
understanding of the packaging profession.

I am very thankful to Dr. Bruce Harte and Janice Harte, they have been a
duo who provided me with all the emotional support I needed as a graduate
student. I really enjoyed the warm relationship with both Dr. Bruce Harte and
Janice Harte. They have been a valuable source of knowledge during Ph.d
program for which I will always be grateful.

Lastly but most importantly I will like to thank my parents and brother for
all the support over all these years in graduate school. It wouldn’t have been
possible to complete my Ph.d program without their love and support. Their
patience, belief and strength gave me the motivation to complete my Ph.d

program.

Table of Contents

List of Tables ................................................................................................ viii
List of Figures ............................................................................................... ix
1. Introduction ............................................................................................. 1
2. Literature Review .................................................................................... 6

2.1 . Fresh-cut fruit and quality ................................................................. 13

2.2. Respiration ....................................................................................... 15

2.3. Factors Affecting Respiration ........................................................... 15

2.3.1. Temperature ........................................................................... 15

2.3.2. Physical Stress ....................................................................... 17

2.3.3. Stage of Development ............................................................ 17

2.4. Ethylene Production ......................................................................... 19

2.5. Preservative treatments ................................................................... 20

2.6. Sensory Evaluation .......................................................................... 23

2.7. Electronic Nose Technology ............................................................. 28

2.8. Packaging ........................................................................................ 32

2.9. Distribution of Fresh Fruits ............................................................... 34

3. Materials and Methods ........................................................................... 39

3.1. Phase-1 Effect of anti-browning agent and transport vibration ......... 39

3.1.1. Fresh-cut processing .............................................................. 39

3.1.2. Sensory evaluation ................................................................. 42

3.1.3. Instrument texture analysis ..................................................... 43

3.1.4. Statistical analysis .................................................................. 43

3.2. Phase-2 ........................................................................................... 45

3.2.1. Quality of commercial FCC ..................................................... 45

3.2.2. Effect of Container Design and Dice Size on FCC ................. 46

3.2.3. Electronic Nose Methodology ................................................. 51

3.2.4. Statistical Analysis .................................................................. 52

4. Results and Discussion .......................................................................... 53

4.1 . Phase 1 ............................................................................................ 53

4.1.1. Sensory evaluation ................................................................. 53

4.1.1.1. .Appearance .................................................................... 53

4.1.1.2..Flavor .............................................................................. 54

4.1.1.3. .Texture ........................................................................... 56

4.1.1.4. .Overall liking ................................................................... 57

4.1.2. Firmness measurements ........................................................ 58

4.1.3. Key findings ............................................................................ 60

4.2. Phase 2 ............................................................................................ 61
4.2.1. Quality of Commercial Fresh-cut Cantaloupe ......................... 61

vi

4.2.2. Effect of Container Design ...................................................... 69

4.2.3. Effect of Fruit Dice Size .......................................................... 84
4.2.4. Correlation of E-Nose and sensory results ............................. 102
4.2.4.1. E-Nose analysis of commercial FCC .............................. 104
4.2.4.2. E-Nose analysis of FCC subjected to random vibration.
......................................................................................................... 105
5. Conclusion .............................................................................................. 119
Appendices ................................................................................................ 121
Appendix A Corrugated box containing nine ‘Container A’ packages .......... 122
Appendix B Corrugated box containing nine ‘Container 8’ packages .......... 123
Appendix C Corrugated box containing nine ‘Container C’ packages .......... 124
Appendix D Container A Random Vibration Test Set up in Accordance to
ASTM 4728 .................................................................................................. 125
Appendix E Container B Random Vibration Test Set up in Accordance to
ASTM 4728 .................................................................................................. 126
Appendix F Container C Random Vibration Test Set up in Accordance to
ASTM 4728 .................................................................................................. 127

Appendix G Trained panel response (n=6) on effect of container design

and transport vibration on sensory characteristics of FCC

packaged in container A, B and C, stored in 40C ........................................ 128
Appendix H Trained panel response (n=6) on effect of fruit dice size

and transport vibration on sensory characteristics of FCC packaged in
container A, B and C stored in 4°C .............................................................. 131
Appendix I Effect of container design and transport vibration on

CIE L*, a“ and b* color values of fresh-cut cantaloupe packaged in

container A, B and C, stored in 400 ............................................................. 133
Appendix J Effect of fruit dice size and transport vibration on

CIE L*,a* and b* color values of fresh-cut cantaloupe packaged in

container A, B and C, stored in 4oC ............................................................. 135
Appendix K Effect of container design and transport vibration on

firmness values of fresh-cut cantaloupe packaged in

container A, B and C, stored in 400 ............................................................. 137
Appendix L Effect of fruit dice size and transport vibration on

firmness values of fresh-cut cantaloupe packaged in

container A, B and C, stored in 400 ............................................................. 138

Bibliography ................................................................................................. 139

vii

LIST OF TABLES

Table 1 Q10 rates at different temperatures ................................................ 16
Table 2 Sensory evaluation samples provided to panelists .......................... 43
Table 3 Experimental design-1 to determine effect of container design on
quality of fresh-cut cantaloupe ..................................................................... 49
Table 4 Experimental design-2 to determine effect of fruit dice size on

quality of fresh-cut cantaloupe ..................................................................... 50
Table 5 E-nose system conditions ............................................................... 52

Table 6 Difference in Ph,TA and T83 concentration of commercial FCC 65

Table 7 Change in total soluble solid concentration of
fresh-cut cantaloupe (2.5cm) in Container A, B & C during storage ............. 74

Table 8 Change in total soluble solid concentration of
fresh-cut cantaloupe (1 .5cm) in Container A, B & C during storage ............. 74

Table 9 02 concentration of fresh-cut cantaloupe (2.5cm) in
Container A, B & C during storage .............................................................. 74

Table 10.02 concentration of fresh—cut cantaloupe (1 .5cm) in
Container A, B & C during storage ............................................................... 74

Table 11.002 concentration of fresh-cut cantaloupe (2.5cm) in
Container A, B & C during storage .............................................................. 74

Table 1200; concentration of fresh-cut cantaloupe (2.5cm) in
Container A, B & C during storage .............................................................. 74

Table 13 Trained panel response (n=6) on effect of container
design and transport vibration on sensory characteristics of fresh-cut
cantaloupe packaged in container A, B and C, stored in 4°C ....................... 128

Table 14. Trained panel response (n=6) on effect of fruit dice size
and transport vibration on sensory characteristics of fresh-cut
cantaloupe packaged in container A, B and C stored in 4°C ................... ‘ ..... 131

Table 15. Effect of container design and transport vibration on
CIE L*, a* and b* color values of fresh-cut cantaloupe packaged
ll'l container A, B and C, stored in 4°C .......................................................... 133

viii

Table 16. Effect of fruit dice size and transport vibration on
CIE L*,a* and b“ color values of fresh-cut cantaloupe packaged
in container A, B and C, stored in 4°C .......................................................... 135

Table 17. Effect of container design and transport vibration on
firmness values of fresh-cut cantaloupe packaged in
container A, B and C, stored in 4°C ............................................................. 137

Table 18. Effect of fruit dice size and transport vibration
on firmness values of fresh-cut cantaloupe packaged in
container A, B and C, stored in 4°C ............................................................. 138

LIST OF FIGURES

Figure 1. Power density spectrum as described in ASTM 4169

Assurance Level II ........................................................................................ 37
Figure 2. Minimally processed fresh-cut cantaloupe (size: 1 in3) before
transport vibration ........................................................................................ 40
Figure 3. Cantaloupe filled containers packaged in corrugated box ............. 41

Figure 4. Cantaloupe filled containers packaged in corrugated boxes
subjected to random vibration. ..................................................................... 41

Figure 5. Comparison of control versus minimally processed
fresh-cut cantaloupe after transport vibration ............................................... 42

Figure 6. FCC samples coded with 3 digit random number on a tray
for a panelist ................................................................................................ 44

Figure 7. Controlled temperature room with fluorescent bulbs for
taste testing fresh-cut cantaloupe ................................................................ 44

Figure 8. PET containers:
left to right Container A; Container B; Container C ....................................... 49

Figure 9. F resh-cut cantaloupe processing to determine effect of
container design on quality of FCC .............................................................. 50

Figure 10 Fresh-cut cantaloupe processing to determine effect of
fruit dice size on quality of FCC .................................................................... 51

Figure 11. Sensory scores for fresh-cut cantaloupe appearance
over storage period ...................................................................................... 54

Figure 12. Sensory scores for fresh-cut cantaloupe flavor over storage
period .......................................................................................................... 55

Figure 13. Sensory scores for fresh-cut cantaloupe texture over storage
period ........................................................................................................... 57

Figure 14. Sensory scores for fresh-cut cantaloupe overall liking
over storage period ..................................................................................... 58

Figure 15. Kramer firmness of fresh-cut cantaloupe over storage period ..... 59

Figure 16. Mean aroma scores for commercially available fresh
cut cantaloupe ............................................................................................. 62

Figure 17. Mean color scores for commercially available fresh-cut
cantaloupe .................................................................................................. 63

Figure18. Consumer panel mean sweetness scores for commercially
available fresh-cut cantaloupe ..................................................................... 64

Figure 19. Consumer panel mean firmness scores for commercially
available fresh-cut cantaloupe .................................................................... 66

Figure 20. Consumer panel mean overall quality scores for commercially
available fresh-cut cantaloupe ..................................................................... 66

Figure 21. Mean firmness measurements for commercially
available fresh-cut cantaloupe ................................................................... 67

Figure 22. Average L“ value for commercially available
fresh-cut cantaloupe ..................................................................................... 67

Figure 23. Average a* value for commercially available
fresh-cut cantaloupe .................................................................................... 68

Figure 24. Average b" value for commercially available
fresh-cut cantaloupe .................................................................................... 68

Figure 25. Trained panel mean aroma scores of fresh-cut cantaloupe
(Size 2.5cm) as an affect of container design; 500 mile trip ......................... 75

Figure 26. Trained panel mean color scores of fresh-cut cantaloupe
(Size 2.50m) as an affect of container design; 500 mile trip ........................ 75

Figure 27. Trained panel mean sweetness scores of fresh-cut cantaloupe
(Size 2.5cm) as an affect of container design; 500 mile trip ......................... 76

Figure 28. Trained panel mean firmness scores of fresh-cut cantaloupe
(Size 2.5cm) as an affect of container design; 500 mile trip ......................... 76

Figure 29. Trained panel mean overall quality scores of fresh-cut cantaloupe
(Size 2.5cm) as an affect of container design; 500 mile trip ......................... 77

Figure 30. Effect of container design L* values of
freshocut cantaloupe (Size 2.5cm); 500 mile trip .......................................... 77

Figure 31, Effect of container design a* values of fresh-cut cantaloupe
(Size 2.5cm); 500 mile trip ........................................................................... 78

xi

Figure 32. Effect of container design b* values of fresh-cut cantaloupe
(Size 2.5cm); 500 mile trip ........................................................................... 78

Figure 33. Effect of container design on firmness values of
fresh-cut cantaloupe (Size 2.5cm); 500 mile trip .......................................... 79

Figure 34. Trained panel mean aroma scores of fresh-cut cantaloupe
(Size 1.5cm) as an affect of container design; 500 mile trip ......................... 79

Figure 35. Trained panel mean color scores of fresh-cut cantaloupe
(Size 1.50m) as an affect of container design; 500 mile trip ......................... 80

Figure 36. Trained panel mean sweetness scores of fresh-cut cantaloupe
(Size 1.5cm) as an affect of container design; 500 mile trip ......................... 80

Figure 37. Mean firmness scores of fresh-cut cantaloupe
(Size 1.5cm) as an affect of container design; 500 mile trip ......................... 81

Figure 38 Mean overall quality scores of fresh-cut cantaloupe
(Size 1.5cm) as an affect of container design; 500 mile trip ......................... 81

Figure 39. Effect of container design L* values of fresh-cut cantaloupe
(Size 1.5cm); 500 mile trip ........................................................................... 82

Figure 40. Effect of container design a* values of fresh-cut cantaloupe
(Size 1.5cm); 500 mile trip ........................................................................... 82

Figure 41. Effect of container design b* values of fresh-cut cantaloupe
(Size 1.5cm); 500 mile trip ........................................................................... 83

Figure 42. Effect of container design on firmness values of
fresh-cut cantaloupe (Size 1.5cm); 500 mile trip .......................................... 83

Figure 43. Trained panel mean aroma scores of fresh-cut cantaloupe
(Container A) as an affect of cut fruit size; 500 mile trip ............................... 88

Figure 44. Trained panel mean color scores of fresh-cut cantaloupe
(Container A) as an affect of cut fruit size; 500 mile trip ............................... 88

Figure 45. Trained panel mean sweetness scores of fresh-cut
cantaloupe (Container A) as an affect of cut fruit size; 500 mile trip ............ 89

Figure 46. Trained panel mean firmness scores of fresh-cut
cantaloupe (Container A) as an affect of cut fruit size; 500 mile trip ............ 89

Figure 47. Trained panel mean overall quality scores of fresh-cut
cantaloupe (Container A) as an affect of cut fruit size; 500 mile trip ............ 90

xii

Figure 48. Trained panel mean aroma scores of fresh-cut cantaloupe
(Container B) as an affect of cut fruit size; 500 mile trip ............................... 90

Figure 49. Trained panel mean color scores of fresh-cut cantaloupe
(Container B) as an affect of cut fruit size; 500 mile trip ............................... 91

Figure 50. Trained panel mean sweetness scores of fresh—cut
cantaloupe (Container B) as an affect of cut fruit size; 500 mile trip ............ 91

Figure 51. Trained panel Mean firmness scores of fresh-cut
cantaloupe (Container B) as an affect of cut fruit size; 500 mile trip ............ 92

Figure 52. Trained panel mean overall quality scores of fresh-cut
cantaloupe (Container B) as an affect of cut fruit size; 500 mile trip ............ 92

Figure 53. Mean aroma scores of fresh-cut cantaloupe (Container C) '
as an affect of cut fruit size; 500 mile trip ..................................................... 93

Figure 54. Trained panel Mean color scores of fresh-cut cantaloupe
(Container C) as an affect of cut fruit size; 500 mile trip ............................... 93

Figure 55. Trained panel Mean sweetness scores of fresh-cut
cantaloupe (Container C) as an affect of cut fruit size; 500 mile trip ............ 94

Figure 56. Trained panel mean aroma scores of fresh-cut cantaloupe
(Container C) as an affect of cut fruit size; 500 mile trip ............................... 94

Figure 57. Trained panel mean overall quality scores of fresh-cut cantaloupe
(Container C) as an affect of cut fruit size; 500 mile trip ............................... 95

Figure 58. Effect of cut fruit size on L* values of fresh-cut cantaloupe
(Container A); 500 mile trip .......................................................................... 95

Figure 59. Effect of cut fruit size on a* values of fresh-cut cantaloupe
(Container A); 500 mile trip .......................................................................... 96

Figure 60. Effect of cut fruit size on b* values of fresh-cut cantaloupe
(Container A); 500 mile trip .......................................................................... 96

Figure 61. Effect of cut fruit size on L* values of fresh-cut cantaloupe
(Container B); 500 mile trip .......................................................................... 97

Figure 62. Effect of cut fruit size on a* values of fresh-cut cantaloupe -
(Container B); 500 mile trip .......................................................................... 97

Figure 63. Effect of cut fruit size on b* values of fresh-cut cantaloupe
(Container B); 500 mile trip .......................................................................... 97

xiii

Figure 64. Effect of cut fruit size on L* values of fresh-cut cantaloupe
(Container C); 500 mile trip .......................................................................... 98

Figure 65. Effect of cut fruit size on a* values of fresh-cut cantaloupe
(Container c); 500 mile trip ........................................................................... 99

Figure 66. Effect of cut fruit size on b* values of fresh-cut cantaloupe
(Container C); 500 mile trip .......................................................................... 99

Figure 67. Effect of cut fruit size on firmness values of
fresh-cut cantaloupe (Container A); 500 mile trip ........................................ 100

Figure 68. Effect of cut fruit size on firmness values of
fresh-cut cantaloupe (Container B); 500 mile trip ........................................ 100

Figure 69. Effect of cut fruit size on firmness values of
fresh-cut cantaloupe (Container C) 500 mile trip .......................................... 101

Figure 70. Principal component analysis of commercial fresh-cut
cantaloupe from 6 different vendors ............................................................. 108

Figure 71. Correlation between expected and predicted values for
commercial fresh-cut cantaloupe from 6 different vendors ........................... 109

Figure 72. Principal component analysis of commercial fresh-cut
cantaloupe packaged in ‘Container 8’; Fruit size-2.5cm Day 1 .................... 110

Figure 73. Principal component analysis of commercial fresh-cut
cantaloupe packaged in ‘Container 8’; Fruit size-2.5cm Day 4 .................... 111

Figure 74. Principal component analysis of commercial fresh-cut
cantaloupe packaged in ‘Container B’; Fruit size-2.5cm Day 7 .................... 112

Figure 75. Principal component analysis of commercial fresh-cut
cantaloupe packaged in ‘Container B’; Fruit size-2.5cm Day 10 .................. 113

Figure 76. Principal component analysis of fresh-cut cantaloupe at day
1,4,7 and 10 day; packaged in ‘Container 8’; Fruit size-2.5cm;
subjected to random vibration for 60 minutes assurance level II .................. 114

Figure 77. Principal component analysis of fresh-cut cantaloupe
stored at day1,4,7 and10, packaged in ‘Container B’; Fruit size-2.5cm;
subjected to random vibration for 120 minutes assurance level Il........... ..... 115

Figure 78. Principal component analysis of fresh-cut cantaloupe
stored at day1,4,7 and10, packaged in ‘Container 8’; Fruit size-2.5cm;
subjected to random vibration for 60 and 120 minutes assurance level II... 116

xiv

Figure 79. Correlation between expected and predicted values for

fresh-cut cantaloupe at day 1,4,7 and 10 day; packaged in ‘Container 8’;

Fruit size—2.5cm; Subjected to random vibration

for 60 minutes (500 mile) assurance level II ................................................. 117

Figure 80. Correlation between expected and predicted values for
fresh-cut cantaloupe at day 1,4,7 and 10 day; packaged in ‘Container B’;
Fruit size-2.5cm; Subjected to random vibration

for 120 minutes (1000 mile) assurance level II ............................................. 118
Figure 81. Corrugated box containing nine ‘Contianer A’ packages ............. 122
Figure 82. Corrugated box containing nine ‘Container 8’ packages ............. 123
Figure 83. Corrugated box containing nine ‘Container 0’ packages ............ 124

Figure 84. Container A Random Vibration Test Set up in
Accordance to ASTM 4728 .......................................................................... 125

Figure 85. Container B Random Vibration Test Set up in
Accordance to ASTM 4728 .......................................................................... 126

Figure 86. Container C Random Vibration Test Set up in
Accordance to ASTM 4728 .......................................................................... 127

1.INTRODUCTION

The United States Department of Agriculture (USDA) and Food and Drug
Administration (FDA) describe ‘fresh’ and ‘minimally processed’ fruits as products
that have been freshly cut, washed, packaged and maintained with refrigeration.
Initially the food service industry was the main customer for fresh-cut products,
but in the past decade fresh-cut products have become increasing popular in
restaurants, supermarkets and warehouse stores. Food service and restaurants
prefer fresh-cut products because the man power needed for preparation and
waste handling is eliminated and product can be delivered at short notice. This
makes fresh-cut products convenient with the added benefit of reduced waste for
retail consumers as an item. Fresh produce has been growing rapidly in US.
supermarkets. Fresh-cut sales rose from $3.3 billion in 1994 to $11 billion in
2005 and the Fresh-cut Fruits (FCF) industry is at approximately $1 billion
category (IFPA, 2004). Once fruits are harvested and undergo the cutting
process, there is loss in quality. The loss in quality is primarily due to water loss,
respiration, ripening, and enzymatic discoloration of cut surfaces, microbial
degradation and mechanical damage. Therefore the success and expansion of
fresh-cut will be dependent on continual marketing of quality products.

Over the last several years consumers have become very conscious of
the nutritional value of their daily diet. They recognize that fresh-cut fruits are not
only convenient but adding it to their daily diet will provide them the additional
nutrition required to maintain good health. Fruits and vegetables are a major

source of vitamins (Vitamin C, Vitamin A, Vitamin Ba, thiamin and niacin),

minerals and dietary fiber, which reduce the risk of cancer, heart disease and
degenerative diseases, along with carotenoids, flavonoids and other phenolics
(Doll, 1990, Rimm et al., 1996, Tee 1992, Grassmann et al., 2002, Gaziano &
Hennekens, 1993). Therefore, it is important that the post cutting operations are
optimized such that there is minimal loss in nutrients during storage of fresh-cut
fruits.

Besides nutritional wholesomeness, some of the key attributes which
make fresh-cut fruits an appealing food category are aroma, flavor, color and
texture. If these attributes are maintained at a level which is acceptable by
consumers then the likelihood that consumers will buy the same fresh-cut
product increases. The primary reasons for rapid deterioration of cut fruit quality
is biochemical and physiological changes during processing, storage,
transportation and handling. Therefore, it is an ongoing challenge to maintain a
certain level of quality. Various postharvest and post cutting techniques have
been implemented to achieve these goals, such as treating fresh-cut fruits with
anti-browning solution, anti-microbial agents, controlled atmosphere storage,
modified atmosphere packaging, irradiation, osmotic dehydration and ethanol
vapor treatment (Gonzalez et al., 2000, Beaudry, 2000, Qi and Watada, 1999,
Lerici et al., 1985) . Depending on the type of fruit characteristics the above
mentioned techniques can be implemented in combination to achieve the
desirable level of quality.

The most common packaging used in the fresh-cut fruit industry are rigid

containers made from Polyethyleneterephthalate (PET) and Polystyrene (PS).

These containers may have a rigid lid, non—perforated film or perforated film as a
closure, depending on the packaging requirements of the fresh-cut fruit. Rigid
containers come in different shapes and sizes, depending on the serving size
and utility (on-the—go, club store quantity), so fresh-cut fruit can be packaged
accordingly. Single serve rigid containers used for packing fresh-cut fruits have
varying heights causing fruit dices to be packed either in a single layer or multiple
layers or oriented randomly in the container. These fruit dices packed in rigid
containers will experience physical movement and repetitive impacts during
transportation. Fruit pieces will tend to move into voids within the container,
causing frictional damage of surface tissue and leading to quality degradation. It
is expected that fruit dices which are packed in layers will experience relatively
lesser physical movement than randomly oriented fruit dices in a container during
transportation. The intensity of the impact and physical movement is dependent
on the location of the rigid container on a unitized load of fresh-cut fruit in
corrugated boxes. Rigid containers located on the top layer of the unitized load
will experience more physical movement due to vibration caused during
transportation. This is due to magnification of vibration forces with increasing
stack height resulting in maximum bouncing of in the top layers. The severity of
the surface tissue damage can be dependent on the rigid container design. A
rigid container with straight side walls may restrict fruit dice movement more
effectively than a container with a sloping side wall. Similarly, ribbed faces of a

rigid container may contribute to the severity of surface tissue damage caused by

repetitive impacts on the ribbed bottom face or side wall. These factors make it
essential to consider the container design in a fresh-cut fruit packaging operation.

It is known that during the cutting operation, surface tissue is wounded
and is highly susceptible to sensorial and physiological degradation compared to
the whole fruit (Gorny et al., 2000). This can be magnified when a product is
transported over a considerable travel distance for distribution to a retail market
(Chonhenchob and Singh, 2006). The fruit dice size can be an important factor to
consider during the cutting operation. The size of the fruit dice plays an important
role during the filling operation. Smaller dices will have larger surface area to
volume ratio than a larger dice, making it more susceptible to surface tissue
damage during transportation. Similarly, during the filling operation a cube shape
fresh-cut fruit piece can be more effectively packed in layers than a randomly
shaped fruit piece. The randomly shaped fresh-cut fruit piece will experience
more physical movement during transportation compared to a cube shape fresh-
cut fruit piece, resulting in surface tissue and quality degradation. Therefore, it is
pertinent to know during the cutting operation the recommended shape and fruit
dice size that can withstand transportation abuses without compromising the
quality of fresh-cut fruit.

Upon reviewing several research studies performed over the past decade,
it is observed that a considerable amount of research effort has been focused on
maintaining the quality of fresh-cut fruits. Most of the research work has been
focused on understanding to maintain quality, nutritional value and extending

shelf life of fresh-cut fruits through various available post harvest techniques and

post cutting treatments. There has not been a study which develops a
methodology to assess the quality of fresh-cut fruit affected by fruit dice size,
container design and transportation.

This study selected three PET container designs, where Container A,
square shaped, had straight side walls with a shallow height (1.75 inches).
Container 8 rectangular shaped, had a sloping side wall with a medium height
(2.625 inches) and Container C parfait cup shaped, which had a wide mouth,
sloping side wall transitioning to a straight side wall with a tall height (3.1 inches).
Similarly, the two dice sizes selected for this study were 2.5cm and 1.5 cm
cubes. It was hypothesized that a smaller fruit dice packaged in a taller container
with sloping side walls will reduce the quality of fresh-cut fruit during

transportation.

2. LITERATURE REVIEW

2.1 Fresh-cut Fruit and Quality

The International Fresh-cut Produce Association (IFPA) defines fresh-cut
produce as fruits or vegetables that have been trimmed and/or peeled and/or out
completely into usable product that is either bagged or packaged to provide
consumers with convenience, high nutrition and flavor while still maintaining its
freshness (Lamikanra, 2002). Fresh-cut produce acquired a marketplace in the
retail industry during the 1990s, lettuce, cabbage, and carrots among other
vegetables (Brody, 2002). These products were made available to consumers as
‘ready to eat’ product after sanitization, cleaning and distribution in controlled
refrigeration thus making it a popular healthy food choice (Ahvenainen, 1996). A
similar approach has been adopted in the fresh-cut fruit industry, where similar
processing technologies are utilized to make available minimally processed
fresh-cut fruits. Fresh-cut produce has been a rapidly growing industry in the US.
Fresh-cut sales rose from $3.3 billion in 1994 to $11 billion in 2004 (IFPA, 2004).
The Fresh-cut fruits (FCF) industry is currently (approximately) $1 billion (IFPA,
2004). This category has not yet reached its potential market share of the fresh-
cut industry. However, there are several challenges in maintaining the quality of
such fresh-cut fruit products.

Quality is a term often used in postharvest and food packaging but it is
rarely defined. There are several perspectives and concepts of quality in
postharvest handling and distribution. The two primary concepts that define

quality are the ‘Product-Oriented Quality” and ‘Consumer-Oriented Quality’

(Shewfelt, 1999). Product-oriented quality is often used by postharvest
researchers, producers and handlers and consumer-oriented quality is used by
consumers, marketers and economists. Product-oriented quality is described as
quality changes of specific attributes that can be quantified and plotted as a

function of time and directly related to physiological changes (Shewfelt, 1999).
The specific attributes are package headspace 02 and 002 composition,

firmness, color, and total soluble sugar, which are measured with analytical
instruments and results can be analyzed and reproduced. The accuracy and
precision of the data analyzed provides internal validity of a scientific study (van
Trijp and Schifferstein, 1995). A product oriented quality evaluation is best suited
for assessing cultivar selection, harvest techniques and post harvest treatments
with an emphasis on appearance leading to extended shelf life. Consumer
oriented quality is defined by consumer behavior and product performance in a
marketplace providing external validity of product performance in a market place.
This involves understanding consumer attitudes by using consumer panels to
determine acceptability/unacceptability and willingness to purchase. The results
can be utilized in identifying quality attributes that drive acceptability, and in
conjunction with sensory descriptive analysis, the critical quality attributes can be
verified (Conner, 1994; Shewfelt et al., 1997). Consumer oriented quality is better
suited to produce a distribution system that is sensitive to consumer needs with
an emphasis on flavor at the expense of appearance leading to shorter shelf life

(Shewfelt, 1999). In view of these two quality concepts, it is essential to design a

postharvest and packaging study where it considers appearance, flavor and
texture to be of equal importance to meet both consumer and distributor needs.

In fresh-cut fruits the greatest hurdle to commercial marketing is its limited
shelf life, which is due to excessive tissue softening and cut surface browning.
The primary reason is the rapid deterioration of cut-fruit quality due to
biochemical and physiological changes during processing, storage,
transportation and handling (IFPA 2005). Mechanical operations like cutting,
peeling, and coring reduce the shelf life of fresh-cut fruit commodities. Wounding
tissues results in metabolic activation which increases respiration rate and in
some cases ethylene production (Varoquaux & Wiley,1997) leading to post-
climacteric stage ripening. These changes adversely affect fruit flavor, texture,
appearance, nutrient retention and increase safety concerns. Since fresh-cut
fruits are more perishable than intact fruits (Watada et al., 1996), research efforts
are being directed towards developing better approaches in processing,
handling, packaging and storage to minimize their impact on cut fruit quality.
Consumers have also become more critical of the use of synthetic additives to
preserve food or enhance characteristics such as color and flavor (Bruhn, 2000).
This has led to adOpting minimal processing techniques in place of traditional
methods of preservation while retaining nutritional and sensory quality (Ohlsson,
2002)

As mentioned earlier, fresh-cut fruits have been gaining popularity in the
past decade as consumers recognize their convenience and added benefit of

nutritional value. Is the same level of nutrition maintained in fresh-cut fruits

compared to whole fruits? A major benefit to high fruit intake is the increase
consumption of vitamins, minerals and dietary fibers (Doll, 1990, Rimm et al.,
1996, Tee 1992, Grassmann et al., 2002, Gaziano & Hennekens, 1993).
Postharvest processing can lead to nutritional loss in quality. This is observed in
the case of Vitamin C. It is affected by physical damage, extended storage, high
temperatures, and low relative humidity (Nunes et al., 1998, Lee & Kader, 2000,
Kader 2002, Hussein et al., 2000). Similarly, the antioxidant properties of a cut
fruit can be depleted by surface exposure due to cutting and oxidation (Klein,
1987, Huxsoll et al.,1989 Wright & Kader, 1997). Gil et al., 2006 studied the
change in quality and nutrition retention in fresh-cut fruits compared to whole
fruits during storage. The study included pineapples, mangoes, cantaloupes,
waterrnelons, strawberries and kiwis. It was found that the antioxidant properties
of fresh-cut fruits did not vary much more than whole fruits during processing and
storage. However there was a reduction in Vitamin C content in the case of fresh-
cut fruits compared to whole fruits during storage and processing. On the basis of
visual quality, the fresh-cut fruits studied were unacceptable by day 6 or 9
depending on the fruit.

When purchasing a fresh-cut fruit product, a consumer considers a blend
of attributes. They consider the appearance, texture and flavor of the product
before making a purchase. The value of such a product to a consumer is a
combination of the above mentioned attributes. The relative importance of each
quality attribute depends upon the fruit. Consumers judge quality of fresh-cut fruit

on the basis of appearance and freshness at the time of purchase. However,

subsequent purchase is dependent on the quality of flavor, aroma and texture of
the product. Researchers utilize these parameters to design research and gauge
the quality of minimally processed cut fruits. There are several sets of criteria
used to assess the quality of a product. A practical approach to assess quality is
to determine acceptability of a product compared to a criterion, the quality limit.
Below this limit, the product is rejected (T ijkens,2000). The acceptance limit is
defined principally by psychological and economic factors, whereas quality of a
product is mostly defined by the intrinsic properties (eg. aroma, appearance,
flavor and texture). Once these intrinsic properties have fallen below the
acceptance limit the product is considered to have reached the end of its shelf
life under standard storage conditions (T ijskens, 2000).

Consumers take product appearance into considerationxas a primary
criterion (Kays, 1999). Color is considered to have a key role in food choice, food
preference and acceptability. It can substantially influence the threshold for taste
and aroma perception (Clydesdale, 1993) Appearance is the size, color, gloss
and visual defects of a whole or cut fruit. In the case of whole fruits, appearance
can be flawed due to insect infestation, disease and bruising due to physical
forces. Cut fruit can experience tissue browning caused by polyphenol oxidase
that catalyzes the oxidation of phenolic compounds to produce brown pigments.
Consumers perceive a firm and juicy texture to be highly desirable while
consuming minimally processed fruits and associate it with freshness and
wholesomeness (Bourne, 2002; Fillion &‘ Kilcast, 2002). Texture includes

firmness, crispiness, juiciness and toughness depending on the fruit. Soft fruits

10

cannot be shipped long distances without proper handling and packaging due to
mechanical injuries. Therefore various fruits are harvested at a maturity level
where it may not have reached its optimum flavor quality but can withstand such
abuses during transit. The factors which include flavor descriptors are
sweetness, soumess, bitterness, aroma and off-flavor. These factors are
perceptions of various compounds in fruits. The sugar content influences
sweetness as organic acids influence sourness. Similarly, certain off-flavors and
odors can be a result of pre or post cut treatment and chemical degradation of
fruit. It is important that these factors are quantified through extensive sensory
testing to determine the minimum level of consumer acceptance. Also, with
growing health concerns, consumers are resorting to more nutritional options like
cut fruits in their diet (IFPA,2004). Since fruits are a good source of vitamins,
mineral and dietary fibers, it is essential they are not depleted of these nutrients.
Nutrient depletion can be a result of improper post harvest, post cut treatment or
physical damage. Therefore it is very essential for a consumer to purchase a
FCF product without any defects and in its finest condition (Watada and
Qi,1999). However subsequent purchase of the same product depends on the
consumers’ satisfaction of its flavor and textural properties upon consuming the
product. Thus one of the on-going challenges is to protect and extend the shelf
life of these highly perishable and minimally processed FCF.
Quality of whole fruits is dependent on cultivars, cultural practices and
climatic conditions, maturity at harvest and harvesting methods (Solomos, 1997).

These factors consequently impact cut-fruit quality. The state of maturity of

11

processed fruit has been shown to influence the damage inflicted during
mechanical operations on the cut fruit surface. Studies performed previously
have shown that the more advanced the ripeness the more susceptible the fruit is
to wounding during processing (Gorny et al., 2000, Gorny et al., 1998). On the
contrary it has been observed that ethylene production doubled in apple slices
from partially ripened apples stored in passive modified atmOSphere in the first
week compared to ripe apples (Solvia-Fortuny et al., 2003).

Additional factors which affect FCF quality are method of preparation,
temperature, humidity, package atmosphere and sanitation (Watada et al.,1996).
Some studies have reported that blunt cutting blades used during the cutting
operation led to a slight increase in respiration and the ethylene production rates
of fresh-cut melon (Portela and Cantwell, 2001). Similarly, cutting direction
appears to play an important role in the wounding response of many fruits. It has
been observed in bananas that a 1 cm-thick transverse section produced less
ethylene and showed the lowest respiration rates (Abe et al., 1998). Some
research has shown that wound induced ethylene production can be reduced by
treating whole fruit or cut fruit with 1-Methylcyclopropene (1-MCP) treatments, as
in the case of apples (Jiang and Joyce, 2002). The study showed that 1-MCP will
bind itself to the superficial cell receptors and block ethylene from its binding site,
thereby reducing ethylene induced ripening and its effect on intact climacteric
fruit quality. Similarly at temperatures between 10-20°C respiration rates and Q10
values were observed to be higher than 040°C for several cut fruits (Watada et

al., 1996). This can deteriorate product appearance, flavor and aroma. Therefore,

12

it is recommended that FCF should be stored at lower temperatures unless there
is a risk of chilling injury. Similarly, very low levels of oxygen in the package
induces anaerobic respiration in fresh-cut fruits, which can lead to the
development of undesirable anaerobic respiratory volatiles and growth of
anaerobic micro-organisms growth (Watada et al., 1996). Therefore, it is
necessary to maintain CO2 and 02 levels in specific ranges to avoid deterioration
of FCF quality and has been recommended in several studies that 3-5% 02 and
540% C02 is optimum for fresh fruits and vegetables storage (Paul and

Clarke,2002; Lee et al. 1996).

2.2 Respiration
Minimally processed vegetables and fruits are living tissues even after
cutting. Damaged plant tissues exhibit an increase in respiratory rate (Theologis
and Laties, 1978). It has been shown that tissues with high respiratory rates have
shorter postharvest lives (Eskin, 1990). The process of respiration involves
combining 02in the air with organic molecules in the tissue (usually a sugar) to
form various intermediate compounds and eventually CO2 and water. The energy
produced by the series of reactions comprising respiration can be captured as
high energy bonds in compounds used by the cell in subsequent reactions, or
lost as heat. Little can be done to alter the internal factors affecting respiration of
harvested fruits and vegetables, since they are largely a function of the
cbmmodity itself once harvested. However, a major part of postharvest

technology is devoted to reducing respiration and other metabolic reactions

13

associated with quality retention by manipulating the external environment.
Modifying the atmospheric composition in which the fresh-cut fruits are stored is
usually done to slow down the respiration rate, reduce metabolic rate and
maturation (Kader, Zagory, & Kerbel, 1989) and losses in fresh weight (Bottcher
et al., 2003). The headspace composition is modified by altering the 02 and CO2
concentration which affects the metabolic state of the product in turn affecting the
quality of the fresh-cut product. Respiration rates can then be evaluated by
monitoring the composition of O2 and CO2 in the headspace of the packages (Del
Nobile et al., 2006) to ensure optimum level of gas composition. Adequate 02
levels are required to maintain aerobic respiration. The exact level of 02 that
reduces respiration while still permitting aerobic respiration varies with
commodity. In most fruits and vegetables, an 02 level around 2 to 3% produces a
beneficial reduction in the rate of respiration and other metabolic reactions.
Levels as low as 1% improve the storage life of some fruits, such as apples,
when stored in optimal temperature conditions. At higher storage temperatures,
the demand for ATP may outstrip the supply and promote anaerobic respiration.
The need for adequate 02 shouldbe considered in selecting various postharvest
handling procedures, such as waxing and other surface coatings, film wrapping,
and packaging. Unintentional modification of the atmosphere in packaging can
result in production of undesirable fermentative products and development of foul
odors. Increasing the CO2 level of some commodities reduces respiration, delays
senescence and retards fungal growth. In low 02 environments, increased CO2

levels may trigger anaerobic respiration with the production of undesirable

14

metabolite and other physiological disorders (Oms-Oliu et al., 2002; Zager &
Kader, 1988). Some commodities tolerate brief (a few days at low temperatures)

storage in a pure N2 atmosphere, or in very high concentrations of CO2.

2.3 Factors Affecting Respiration

Respiration is affected by a wide range of environmental factors that
include light, chemical stress such as fumigants, radiation stress, water stress,
growth regulators, and pathogen attack (Biale and Young, 1981) . The most
important postharvest factors are temperature, atmospheric composition, and
physical stress (Kays, 1991).

2.3.1 Temperature

Without a doubt, the most important factor affecting postharvest life is
temperature. This is because temperature has a profound affect on the rates of
biological reactions, such as metabolism and respiration. Increased temperatures
cause an exponential rise in respiration (Biale and Young, 1981). The Van't Hoff
Rule states that the velocity of a biological reaction increases 2 to 3-fold for every
10 °C (18 °F) rise in temperature (Salveit, 1996).

The temperature quotient for a 10 °C interval is called the Q10. The 010
can be calculated by dividing the reaction rate at a higher temperature by the rate
at a 10 °C lower temperature, i.e., Q10 = R2/R1 (Biale and Young, 1981). The
temperature quotient is useful because it allows us to calculate the respiration
rates at one temperature from a known rate at another temperature. However,

the respiration rate does not follow ideal behavior, and the 010 can vary

15

considerably with temperature (Biale and Young, 1981). At higher temperatures,

the 010 is usually smaller than at lower temperatures. Typical rates for 010 are:

Table 1. Q10 rates at different temperatures

 

 

 

 

 

 

 

 

Temperature Q10
0 to 10 °C 2.5 to 4.0
10 to 20 °C 2.0 to 2.5
20 to 30 °C 1.5 to 2.0
30 to 40 °C 1.0to1.5

 

(Salveit, 1996)

Although respiration is normally reduced at low, but non-freezing
temperatures, certain commodities, chiefly those originating in the tropics and
subtropics, exhibit abnormal respiration when their temperature falls below 10 to
12 °C (50 to 53.6 °F). Typically 010 is much higher at these low temperatures for
chilling sensitive crops than it would be for chilling tolerant ones. Respiration may
increase dramatically at the chilling temperatures or when the commodity is
returned to non-chilling temperatures (Biale and Young, 1981). This enhanced
respiration presumably reflects the cells’ efforts to detoxify metabolic
intermediates that accumulated during chilling, as well as to repair damage to
membranes and other sub-cellular structures (Kays, 1991). Enhanced respiration
is only one of many symptoms that signal the onset of chilling injury.

As the temperature rises beyond the physiological range, the rate of
increase in respiration falls. It becomes negative as the tissue nears its thermal
death point, when metabolism is disorderly and enzyme proteins are denatured
(Biale and Young, 1981). Many tissues can tolerate high temperatures for short
periods of time and this property is used to advantage in killing surface fungi on

some fruits. Continued exposure to high temperatures causes phytotoxic

16

symptoms, followed by complete tissue collapse (Biale and Young, 1981).
However, conditioning and heat shock, such as short exposure to potentially
injurious temperatures, can modify the tissue’s responses to subsequent harmful
stresses.
2.3.2 Physical Stress

Even mild physical stress can perturb respiration. Physical abuse can
cause a substantial rise in respiration that is often associated with increased
ethylene evolution. The signal produced by physical stress migrates from the site
of injury and induces a wide range of physiological changes in adjacent, non-
wounded tissue (Biale and Young, 1981, Kays, 1991, Abeles et al., 1992). Some
of the more important changes include enhanced respiration, ethylene
production, phenolic metabolism and wound healing (Kays, 1991). Wound-
induced respiration is often transitory, lasting a few hours or days. However, in
some tissues wounding stimulates developmental changes, such as promotion of
ripening that result in a prolonged increase in respiration (Barberan, 1997).
Ethylene stimulates respiration and stress-induced ethylene may have many
physiological effects on commodities besides stimulating respiration (Abeles et
al., 1981).
2.3.3 Stage of Development

Respiration rates vary among and within commodities. Storage organs
such as nuts and tubers have low respiration rates. Tissues with vegetative or
floral meristems such as asparagus and broccoli have very high respiration rates

(Biale and Young, 1981). As plant organs mature, their rate of respiration

17

typically declines. This means that commodities harvested during active growth,
such as many vegetables and immature fruits, have high respiration rates (Biale
and Young, 1981).

After harvest, the respiration rate typically declines; slowly in non—
climacteric fruits and storage organs and rapidly in vegetative tissues and
immature fruits. The rapid decline presumably reflects depletion of respirable
substrates that are typically low in such tissues (Biale and Young, 1981). An
important exception to the general decline in respiration following harvest is the
rapid and sometimes dramatic rise in respiration during the ripening of climacteric
fruit (Biale and Young, 1981). This rise, which has been the subject of intense
study for many years, normally consists of four distinct phases: 1) pre-climacteric
minimum, 2) climacteric rise, 3) climacteric peak, and 4) post-climacteric decline.

Several fresh-cut fruits have shown higher respiration rates than whole
fruits (Watada et al,1990;Cantwell, 1992). It has been shown that wounding a
fruit induces a change in the mitochondrial structure as well as increases their
numbers. This explains the higher respiration rates due to higher aerobic
mitochondrial respiration (Asahi,1978). Higher respiration rates has been linked
to shorter shelf life (Kader,1987). It is assumed that cutting fruits will shorten their
shelf life (Rolle and Chism,1987). Respiration in cut fruits is also influenced by
storage temperature. Higher respiration rates are more prevalent in cut fruits
stored at higher temperature (Watada et al,1996). Shredded cabbage had the
lowest respiration rate at a storage temperature of 25°C followed by 5°C, 7.5°C

and 10°C (Cantwell 1992). Similarly sliced green tomatoes stored at 8°C had a

18

40% increase in their respiration rate, compare to that of an intact tomato
(Mencarelli et al.,1989). Increased respiration rate in cut fruit can be also be a
result of anaerobic respiration. If cut fruit is stored at high temperature and at
oxygen levels which are below the threshold to induce anaerobic metabolism,
then the cut fruits will sustain high respiration rates (Lakakul et al., 1999).
Therefore, it is essential to control temperature and package atmosphere to
inhibit anaerobic respiration as it can lead to accumulation of anaerobic
metabolites producing off-flavors (Ke et al., 1991)

Several studies have been devoted to fresh-cut physiology (Watada et al,
1990, Brecht 1995, Watada et al 1996). The fundamental principle behind fresh-
cut quality is that they are living tissues, as a consequence if they are abused
during post harvest handling, processing and distribution it will result in certain
physiological responses (e.g. browning and purging). Microbial growth on cut
fruits is influenced by the physiology of the minimally processed product
therefore maintaining low microbial numbers is an essential part of maintaining

the quality of fresh-cut fruit.

2.4 Ethylene Production

It is well known that wounding a plant tissue leads to ethylene production.
Ethylene production as a consequence of cutting has been observed in tomato
(Lee et al.,1999), strawberry (Abeles et al., 1992) and papaya. (Paull and
Chen,1997). There are some fruits which do not produce ethylene or have

reduced ethylene upon cutting, like pear (Gorny et al,2000; Rosen and Kader

19

1989). Whereas cantaloupe melon has shown both high and low ethylene
release upon cutting (Hoffmann and Yang,1982 ; Luna-Guzman et al.,1999). A
possible explanation to this contradiction is that the melon was cut at two
different stages of maturity. If the melon was cut pre-climacteric stage then it will
show high ethylene release, whereas cutting at post-climacteric stage it showed
low ethylene release. Therefore, it is crucial to be aware of fruit maturity before
starting a fruit cutting operation as it may influence cut fruit quality with time. One
successful method to suppress ethylene production is to store fresh-cut fruits at

temperatures between 0 - 25°C (Madrid and Cantwell).

2.5 Preservative Treatments
The color of fresh-cut fruits is probably the main quality attribute
considered by consumers. One of the most limiting factors on the shelf-life of
minimally processed fresh-cut fruits is browning. The aroma and texture
attributes of fresh-cut fruit are secondary factors which a consumer considers
before buying a FCF product. Several studies have been conducted to reduce or
control browning in fresh-cut fruits, maintain desirable level of aroma volatiles
and texture during storage. The common practice to reduce browning in fresh-cut
fruit is post cutting treatment of fruits with anti-browning agents. Anti-browning
agents administered could be synthetic (1-Methylcyclopropene, sulfites) or
naturally occurring compounds and derivatives found In plants (Methyl
jasmonate, 4-hexylresorcinol) (Gonzalez et al., 2001 and Monsalve-Gonzalez et

al.,1995). There has also been a report of study performed using mild heat

20

treatment of whole fruit prior to the cutting operation to maintain desirable aroma
characteristics in cantaloupe (Lamikanra et al., 2005).

Gonzalez et al., 2000 studied the effect of anti-browning agents on fresh-
cut mangoes. The anti-browning agents used were 4-hexylresorcinol (HR),
potassium sorbate (KS) and D-isoascorbic acid (ER). They also studied the
effectiveness of a combination of these anti-browning agents to inhibit browning.
The combination solutions investigated in this study were HR (0.001M) + KS
(0.05M), HR(0.001M) +ER (0.5M) + KS(0.05M), ER(0.5M)+ KS(0.05M) and
HR(0.001M) + KS(0.05M). It was discovered that the two best performing
solutions were HR(0.001M) + KS (0.05M) and HR(0.001M) +ER (0.5M) +
KS(0.05M). Fresh-cut mangoes treated with these solutions produced a
reduction in color change (L*,a*,b*) and microbial growth while maintaining the
sensory characteristics of fresh-cut mangoes. Fresh-cut mangoes treated with
these two solutions retained high levels of citric acid, the main organic acid in
mango fruit. It was also observed that there was an increase in fructose and
glucose during storage. The combination HR+ER+KS was reported to be the
most effective in extending the shelf life of fresh-cut mangoes to 14 days. The
study showed that the combination of several browning inhibitors was more
effective than those applied individually.

The use of 1-methylcyclopropene (1-MCP) induces beneficial effects such
delay in physio-chemical changes related to fruit ripening, reduction of decay,
color properties and weight loss (Blankenship and Dole, 2003). Valero et al.,

2004 studied the effectiveness of 1-MCP treatment on plums packaged in bulk

21

and small card-board boxes. It was found that 1-MCP inhibited the typical
climacteric peak and delayed the change in properties related to fruit ripening. 1—
MCP treated plums packaged in small cardboard boxes showed significantly
lower fruit softening, decreased in titrable acidity and delayed color changes
compared to plums treated in bulk. Packaged plum in small card-board boxes
was more responsive to 1-MCP treatment as there was better gas diffusion over
the entire surface of the fruit enabling 1-MCP to block receptors more effectively.
Thus, parameters related to plum ripening, such as color chroma, TSS, fruit acidy
and softening was delayed compared to 1-MCP bulk treated plums. The study
showed that plum packaged in well aerated boxes and treated with 1-MCP had
an increased shelf—life compared to plums treated with 1-MCP in bulk.

One of the key problems in fresh-cut pineapple was browning after 6 days
of storage at 4°C (0 Connor et al., 1994). Gonzalez et al., 2003 studied the effect
of different concentration of ascorbic acid (AA), isoascorbic acid (IAA) and acetyl
cysteine (AC) to delay browning in fresh-cut pineapple packaged in polystyrene
trays stored at 10°C. The anti-browning agents reduced browning and decay
significantly. lsoascorbic acid was most effective in preserving the visual
appearance, firmness and reduced changes in L* and b* values of the pineapple
slices followed by acetyl cysteine and ascorbic acid. The pineapple slices treated
with IAA, AA and AC effectively increased the shelf-life up to 14 days at 10°C.

By processing FCF with anti-browning agent it provides a protective
layering over the exposed tissue. Several studies have reported that ascorbic

acid in combination with CaCl2 is an effective anti-browning agent (Chohenchob

22

and Singh, 2005). A commercially available anti-browning agent NatureSealm' is
a calcium ascorbate powder used extensively in the fresh—cut industry. Ascorbic
acid functions as a reducing agent to deter surface browning (Whitaker, 1994).
Calcium chloride treatment provides tissue firming and has been reported to

reduce browning (Drake and Spayd, 1983; Hopfinger et al.,1984).

2.6 Sensory Evaluation

Sensory analysis is a branch of food science in which a structured and
codified methodology is adopted to evaluate physical and organoleptic properties
of a food product. In basic terms it can be understood as a human response
interpreted by the person’s brain to a physical stimulus (Meilgaard et al, 1999).
Physical stimulus could be through any of the 5 senses a human possess (smell,
vision, taste, touch and auditory). The four main sensory attributes that are
generally evaluated for food products are appearance, aroma/odor, texture
(consistency and/or firmness) and flavor (Meilgaard et al, 1999). A subject’s
perception of each of these attributes is integrated during sensory evaluation of a
product. Unless the subject is trained to provide independent evaluation of each
attribute prior to the sensory evaluation of the product (Meilgaard et al., 1999).
Essentially there are three different types cf sensory tests. They are
discriminative tests, descriptive tests and affective tests. Discriminative tests are
performed to identify if there is a difference between samples. The intensity and
nature of difference is determined by performing a descriptive test. An affective

test is performed to determine a panelist’s preference, acceptance or degree of

23

liking between samples. The results are a subjective representation of a
panelist‘s attitude towards a product (Meilgaard et al, 1999). Sensory analysis is
an affective tool to detect off-odor and off-flavors caused by a compound having
a low threshold level, which can go undetected by instrument analysis (Peled and
Mannheim, 1977).

A study was performed by Schieberle and Hofmann, 1997, to evaluate the
odor impact of certain volatiles in model strawberry juices. They identified twelve
odor active volatiles which were representative of fresh strawberry juice from a
previous study (Schieberle, 1994). A group of six trained panelists determined
the intensity of eight odor qualities as detected in fresh strawberry juice
compared to a model strawberry juice with the odor active volatiles. It was found
that the flavor profile of the model juice was very similar to that of fresh
strawberry juice. To gain better insight into their model juice as to how much of
an impact each of the odor active volatiles presented, they prepared 11 model
juices with one missing odor active volatile of the twelve odor active volatiles a
typical strawberry juice is eXpected to contain. A triangle test was performed in
which they used the complete mixture of twelve odorants as a control. It was
determined that lack of 4-hydroxy-2,5-dimethyl-3(2H furanone and (Z)-3-hexenal
caused a clear change in the overall strawberry like odor, thereby showing that
both are character impact odorants in strawberry flavor (Schieberle and
Hofmann, 1997). Such systematic approach to identify key odorants in food can

assist in developing a product where strawberry flavor is desired.

24

Sensory evaluation can be used to determine an acceptable level of acid
and sugar levels in diced tomatoes to ensure consumer acceptability and
freshness impact on flavor. A study was performed to determine the affect on
diced tomato taste and impact on freshness affected by six different ratios of
sugar and acid levels (Malundo et al., 1995). Descriptive analysis was conducted
to understand the effect on sweet, sour and fresh tomato impact and a consumer
test was conducted to rate acceptability of diced tomatoes. The sugar and acid
levels affected the tomato taste (sweet and sour) but did not significantly affect
the descriptive ratings for fresh tomato impact, as it is more a function of volatile
compound concentrations than sugar and acid levels (Kader et al., 1977). The
results indicated that when tomato has a pH of about 3.74 or 0.80% TA,
increasing the sugars can lead to an improved flavor quality (as per the
consumer acceptability test). However, beyond these levels of pH and TA
increasing acid levels affected negatively on the consumer acceptability rates for
a given concentration of sugar. This study demonstrates that sensory evaluation
and particularly consumer testing is an effective tool to determine the affect of
taste components on flavor perception, independent of volatile aroma
compounds, thus paving a way to improve the quality of fresh tomatoes.

Instrument analysis may sometimes not correlate well with sensory
measurements, in particular off-odor/ aroma with low threshold levels stimulating
a response in a panelist, but undetectable by way of instrument analysis. It is
necessary to discover a relationship between sensory measurements and

instrument analysis where possible for certain attributes, such as texture. Harker

25

et al. (2002) investigated the relationship between sensory and instrument
measurements of apple texture. A group of trained panelists was directed to
evaluate a wide range of texture attributes (eight texture attributes) for different
cultivars, maturity and ripeness of apples. Similarly, three instrument tests were
performed to predict sensory response, which were puncture, twist and chewing
sound. It was discovered that the puncture test was better at predicting sensory
measurements than the other two methods. It was reported that for a panelist to
detect a difference in texture there should be a firmness difference of 6 newtons
(N) between samples (Harker et al., 2002). Such a finding facilitates researchers
in postharvest technology to make decisions on pre/postharvest treatments with
some level of confidence. However, it has been suggested that conventional
sensory analysis by trained/untrained panelists should not be replaced by
instrument analysis as some of the textural attributes (mealiness) is not
predictable by instrument analysis.

Flavor, aroma and texture are the key indicators of fresh-cut fruit quality
(Shewfelt, 1999) and it is a challenge to maintain these attributes at a level for
consumer acceptability level (Shewfelt, 1999). Sensory analysis is often
conducted to determine if there is a detrimental effect of postharvest processing
techniques in order to maintain the quality of fresh-cut fruit (Gonzalez et al., 2000
and Gonzalez et al., 2004). Similarly, sensory analysis techniques can be
implemented to determine the level of maturity that a fruit should reach before it
is harvested to prepare fresh-cut fruits with the desirable levels of sensory

properties. Sensory characteristics of fresh-cut cantaloupe are affected by the

26

harvest maturity of the whole fruit (Beaulieu et al., 2004). It was established that
descriptive analysis by a trained sensory panel that fresh-cut cantaloupe cubes
prepared from 1/4 slip mature cantaloupe were significantly firmer than 1/2, 3/4
and full slip matured fruit. Correspondingly, 1/4 slip cubes had significantly lower
fruit and sweet aromatic flavor than 1/2, 3/4 and full slip maturities (Beaulieu et
al., 2004). The study suggested in order to achieve the desirable sensory
characteristics, fresh-cut cantaloupe should be prepared from fruits which are
greater than equal to 1/2 slip mature.

Sensory analysis techniques have been implemented to determine quality
of fresh-cut fruit as affected by cutting tools (Cantwell and Portela, 2001) and
shape of cut fruit (Lopez et al., 2005). Cantwell and Portela, 2001 found that
cantaloupe pieces prepared using sharp borers maintained a marketable quality
for 6 days compared to those prepared with blunt borers which were
unacceptable after 6 days. Pieces prepared with a blunt blade show higher
surface translucency scores making it visually unacceptable. However, blade
sharpness did not affect aroma and off-odor scores. Similarly, Lopez et al.,
(2005) discovered that papaya cubes had a better overall quality index than

slices stored at 5°C. It is evident that sensory evaluation is a critical component in

determining the quality of fresh-cut fruits.

27

2.7 Electronic Nose Technology

The electronic nose is defined as an instrument comprised of electronic
chemical sensors with partial specificity and an appropriate pattern recognition
system, capable of recognizing simple or complex odors (Gardner and Barlett,
1993).

The electronic nose system generates headspace gas a over the sample
being tested, exposes the headspace gas to the sensors, records the sensors’
response, and analyzes the data. Different types of sensors are commercially
used in electronic noses, and include metal oxide sensors, conducting polymers,
and quartz crystals. Metal oxide sensors are made from zinc or tin oxide. These
sensors are operated between two electrodes at 300 °C. The aroma compounds
get oxidized on the surface of the sensor and change the resistance of the
sensor. Conducting polymer sensors are obtained by electro-polymerization of a
thin film of polymer across the gap between gold-plated electrodes. The electrical
conductance of the film changes according to the odor compounds adsorbed on
its surface. In the quartz crystal category, two different types of sensors are used.
One is based on sensing the mass of the aroma compound adsorbed into the
stationary phase coated on the crystal surface. The adsorption changes the
frequency of vibration of the crystal, due to change in mass. These sensors are
called quartz microbalances. The second type of sensor is a surface acoustic
wave device. It operates similarly to quartz microbalance, apart from the fact that
a surface wave is used to measure the absorbed quantity of aroma compound

(Cutler,1999).

28

As reviewed by Schaller et al (1998), there is another type of metal oxide
semi-conductor sensor used in the commercial electronic noses, known as a
metal oxide semiconductor field-effect transistor (MOSFET) sensor. A MOSFET
sensor is comprised of three layers: a silicon semiconductor, a silicon oxide
insulator, and a catalytic metal such as palladium, platinum, iridium or rhodium.
The catalytic metal is also called gate. The applied voltage on the gate and
contact creates an electrical field, which alters the conductivity of the transistor.
Hence when polar, odor compounds interact with the metal gate and the electric
field is modified, which eventually modifies the current flowing through the
senson

The electronic nose is an analytical instrument that can recognize flavors,
odors and volatile compounds. It has many advantages over the subjective
sensory panel evaluation of odors and flavors as it eliminates the fatigue factor,
inconsistency, and the high cost involved in human sensory analysis. An
electronic nose is composed of a chemical sensing system including as a sensor
array and a pattern recognition system. Each sensor is sensitive to a certain
volatile compound and generates a signature or pattern characteristic of the
vapor. Different chemicals can be presented to the sensor array, which is then
used to build a database of signatures. Such a database can be used to train the
pattern recognition system of the electronic nose (Giese,2000).

Cutler (1999) pointed out that analytical measurement techniques such as
GC-MS can detect individual components in a volatile vapor, but such

components do not necessarily represent the combined sensory effect of the

29

Si

 

vapor. Moreover, trained human subjects are not always available to perform
sensory analysis.

Schaller et al (1998) pointed out that the electronic nose has been
successfully used as a quality control tool to evaluate quality of various food
products such as meat, grains, coffee, beer, mushroom, cheeses, sugar, fish,
blueberry, orange juice, cola, and alcoholic beverages. It is also being widely
used to analyze off-flavors in food due to packaging.

Henio and Ahvenainen (2002) used the E-nose to analyze the taints
caused by pigments of printed solid boards. The objective of the experiment was
to determine the effect of printing inks on the sensory properties of the packaging
material, using the E-nose, which was correlated with human sensory evaluation
and other headspace methods such as GC-MS. Twenty samples were studied,
which included unprinted solid board, lacquered solid board, offset printed solid
board with 14 different colors, and offset printed, lacquered solid board with 4
colors. The E-nose was found to successfully discriminate the different board
samples based on their coloring agents or lacquering. The results also were
correlated with the off-flavor perceived during sensory evaluation. Winquist et al
(1993) used E-nose to study the quality of ground beef and pork and also
estimate storage time in a refrigerator, based on the organoleptic property of the
meat after storage. The electronic nose used consisted of a gas sensor array
combined with a pattern recognition routine. Samples of ground beef and pork,
stored in a refrigerator, were studied. The E—nose was successful in identifying

the type and quality of meat.

30

Benedetti et al (2002) explored the use of an electronic nose to study the
shelf life of ripened Taleggio cheese packaged in paper. The electronic nose
used for the study had an array of 10 MOFSET sensors and 12 MOS sensors.
The E-nose was found to effectively classify and discriminate among cheese
samples based on differences in their storage time and temperature. The
different storage times and temperatures influenced the aroma characteristics of
the cheese, which was sensed by 6 of the 22 sensors, with good discrimination
power.

Van Deventer and Mallikarjunan (2002) analyzed and compared the
performance of three electronic nose systems as a quality control tool, to detect
retained printing solvents in packaging. Metal oxide semiconducting sensors,
conducting polymer sensors, and quartz microbalance sensors. Each system
was used to test 3 different film classes, with varying retained solvents. It was
concluded that E-nose with conducting polymer sensor technology had the
highest discriminatory power. However, all the electronic noses were found to be
capable of discriminating among the film samples at different levels of retained
solvents.

Willing et al (1998) used an electronic nose to measure odors from
paperboard, intended for packaging applications. Nine different paperboards
from a wide range of board grades were analyzed using the electronic nose. The
electronic nose was equipped with 10 MOFSET sensors, 4 Tagushi sensors, and
1 carbon dioxide sensor. The partial least squares regression (PLS) method was

used to correlate electronic sensor responses with sensory panel descriptors.

31

Some electronic sensor responses correlated well with a selected number of
panel descriptors, while others did not fit with panel descriptors.

Electronic nose technology is in a continuous development process, both
with respect to hardware and software technology. It still has some
disadvantages. It cannot provide sufficient quantitative information for certain
aroma differences (Harper, 2001). In addition, the electronic nose system is
prone to sensor drift, which occurs due to degradation of individual sensors. Drift
results in a gradual change in output over time without any significant change in
input. Thus, it hinders the reproducibility of the system. Calibration of sensors
and sensor replacement after a fixed time interval can help in minimizing this
problem (Maneesin,2001). Moreover, the sensors are sensitive to moisture.
Conducting polymer sensors are more sensitive to moisture than the metal oxide
sensors, which is minimized by using a filter and an air conditioning unit

(Culter,1 999).

2.8 Packaging

Packaging plays an important role in containing and maintaining quality of
perishable products. Several technologies have been developed to maintain
quality of fresh-cut fruits by way of post harvest treatments, such as anti-
browning treatments, anti-microbial treatments, irradiation, and mild heat
treatment (Gonzalez et al., 2000, Beaulieu, 2007, Ferrer & Harper, 2005,
Lamikanra et al., 2005). In the past decade modified atmosphere packaging

(MAP) has played an increasingly important role in the perishable product

32

industry. An important goal in modified atmosphere packaging is to maintain a
sufficiently low 02 concentration to influence metabolism of the product to extend
it’s shelf life (Beaudry, 2000). In some cases altering the C02 concentration in
the package headspace positively influences the shelf life of fruits and
vegetables (Qi et al.,1998 and Aguayo et al., 2004).

By itself, modified atmosphere packaging to extend shelf life of fresh-cut
fruits may not be enough to achieve the goal to maintain quality of cut fruits.
Optimal storage temperature and appropriate post cutting treatments are
necessary to meet these goals. Marrero and Kader, 2005 studied the optimal
temperature required to enhance keeping quality of fresh—cut pineapples in
modified atmosphere package. They established that storing fresh-cut
pineapples in modified atmosphere package at 10°C resulted in a shelf life of 4
days, whereas the shelf life of cut pineapples stored at 22°C and 0°C was found
to be over 14 days.

Similarly, another study conducted by Zhang et al., 2006 showed that
MAP of strawberries prolonged shelf-life by 4—6 days. However, application of
edible coating on strawberries prior to packaging extended the shelf-life by 8-10

days. Micro-perforated films are being used for modified atmosphere packaging
to maintain a desirable O2/C02 concentration in the package headspace (Welt

and Abdellatief, 2007). This study showed, however, that micro-perforated film
may not be suitable for all types of fresh-cut vegetables. They diScovered that
non-perforated films would satisfy MAP requirements for rutabaga, sweet potato,

Yellow squash, and 50/50 blend of yellow squash and zucchini. Whereas, micro-

33

perforations were only suitable for fresh-cut sweet potato to satisfy MAP
requirements. Modified atmosphere packaging was used to maintain the quality
of fresh-cut mango (Buta et al.2000; Beaulieu and Lea, 2003, Chohenchob et al.,
2006), cantaloupe melon and pineapple (Chohenchob et al., 2006). Currently, the
common packaging material used are PET (Polyethyleneterephthalate) and PS
(Polystyrene) for rigid containers. Such rigid containers are non-biodegradable
which contributes substantially towards solid waste in a landfill. To combat this
issue a biodegradable material known as PLA (Poly lactic acid) is being used to
make rigid containers. Rigid containers made from PLA are slowly becoming
popular in the fresh-cut fruit industry to replace PET and PS rigid containers.
However, it is yet to be verified if PLA containers are capable of maintaining the
quality of FCF during storage and transportation in a cold chain distribution
system. Thus, it is necessary to determine the effect of an anti-browning agent
on minimally processed cantaloupe packaged in rigid containers made from PLA,

as affected by transportation (Chohenchob et al., 2006).

2.9 Distribution of Fresh Fruits

Distribution and marketing of fresh fruits and produce comprise multiple
processes, including storage handling and transportation over land or sea to
various markets, sometimes over 1000 miles away from the orchards or farm,
where it is grown. Fruits and vegetables can be distributed as whole or minimally
processed to various distribution centers and retail outlets. Consumers in urban

areas have high per capita income and can afford to spend it on premium quality

34

pre-packaged ready to eat cut fruits. This is evident in the fresh-cut fruit industry,
which is reported to be a $1 billion market (IF PA, 2004). These products need to
be in premium condition at retail outlets so that consumers get maximum value
for the price. To ensure quality, expensive sophisticated operations including
cleaning, disinfecting, processing, packaging and controlled atmosphere cold
storage is practiced. Modern produce houses generally have a packing house
located near the growing area. Without proper packaging fruits at such maturity
levels can be easily bruised and abused which can lead to deteriorating quality in
downstream operations. It is in individual grower’s prime interest to send out the
maximum amount of produce to a produce house. Even though quality inspection
practices are in place it may not be enough to ensure that the highest quality of
products are being distributed. To ensure quality, marketing organizations have
quality control schemes and reward the growers as per the quality of their
products. To assist in these efforts research and development is dedicated to
understanding the produce distribution system and analytically to determine the
best solution to maintain high quality and standards for such products. The goal
of research and development is to have an interdisciplinary approach that
considers the bio-chemical behavior and processing of fruits and vegetables,
along with packaging and distribution systems.

It is well. known that whole fruits like mango, banana, tangerine and
Papaya get bruised and quality deteriorates very rapidly. Such spoilage is
dependent on road conditions and type of trucks used to transport fruits

(Chohenchob and Singh, 2005). Since fresh-cut fruits have exposed tissue,

35

highly perishable (Watada et al., 1996) and even more susceptible to damages
during transportation.

Jarimopas, Singh and Saengril studied the effects of vibration in truck
shipments on packaged tangerines in Thailand. (2005). A part of this study
measured the effect of road conditions on the vibration levels. The study
measured Iaterite, asphalt and concrete road surfaces. Results showed that the
effect of road condition and trailer speed makes a big impact on the vibration
produced and this affects bruising of fruit. The highest damage was produced on
unpaved roads (laterite) road surfaces. These surfaces are usually formed from
clay and pebbles.

The raw vibration data for different type of shipments is usually measured
as a time-acceleration history and is sampled periodically using data recorders.
The data is then analyzed to form power density spectrums (PDS). These are
plotted on a log-log scale with power density (PD) along the y-axis and frequency
in the x-axis. The average PD within a band of frequencies is calculated as root
mean square acceleration measured in 9’s at any instant within a 1 hertz (Hz)
bandwidth (BVV), and N is the number of instant samples for a given segment of
vibration.

Figure 1 shows a typical Power Density Spectrum. This spectrum is from
the composite truck spectrum shown and described in American Society of
Testing and Materials (ASTM) D4169 for an Assurance Level II. This is the most
widely used vibration spectrum for random vibration testing internationally. The

FDA also recommends the use of this spectrum for validation of all

36

pharmaceutical and food products. Packaging engineers use this spectrum to
program vibration test equipment in accordance with ASTM 04728 to conduct

random vibration tests.

 

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It should be noted that it is almost impossible and usually extremely
expensive and time consuming to recreate the exact measured vibration levels in
a laboratory. The reason is that vibration testers are not capable of large
displacements that may with large potholes or uneven surface irregularities. As a
result, various test method developing organizations have developed PD
“composite” spectrums that combine various spectrums from road travel, speed,
and truck types and then use accelerated levels to reduce test time. The ASTM
D4169 Assurance Level II is the most widely accepted vibration spectrum

representing vibration in a steel spring suspension trailer traveling in the United

37

States. International Safe Transit Association (ISTA) has developed a correlation
of test time to travel time. ISTA Test Method 3E recommends correlating 30
minutes of test time in a random vibration test to 300 miles. A 1500 mile trip
therefore reflects a 180 minute or 3 hour test. ASTM D4169 allows a range
between 30 minutes to 6 hours to represent domestic and international

shipments.

38

3. MATERIALS AND METHODS
3.1 Phase-1
Effect of Anti-browning Agent and Transport Vibration
3.1.1 Fresh-cut Processing
This part of the study was performed to identify a suitable anti-browning
solution for post cutting treatment. Whole cantaloupe was purchased from a local
supermarket. The whole cantaloupe was washed and dipped in a commercial
sanitizer- Fruit & Vegetable Wash (SC Johnson Professional, Sturtevant, WI)
(100-ppm chlorine) for 5 minutes. They were then stored in a 4°C:l: 03°C in walk-
in chamber for a period of 12 hours prior to cutting. Once cantaloupes
equilibrated to the desirable temperature, they were cut in 22°C: 4°C
environment which has been cleaned and sanitized. After removing seeds and
peels, the cantaloupes were cut into 1-inch cubes using a sharp stainless steel
knife cleaned in a 100pm chlorine solution (Figure 2). Cantaloupe pieces were
dipped in two anti-browning solutions: Treatment-A (2% ascorbic acid + 1%
calcium chloride + 0.5% citric acid) and Treatment-B (3% NatureSealTM
containing calcium ascorbate) for 2 minutes. Following, 180 :t Sgrams of
cantaloupe pieces were packaged in bio-based clamshell containers (19.1 x 16.5
x 4.4 centimeters) made from polylactide (PLA) (Figure 3) procured from
Wilkinson Industries, Nebraska.
Corrugated boxes were designed using ArtiosCad 7.6 (EskoArtwork, Gent,
Belgium) and an ArtiosKornsberg Premiumline 1930 cutting table (EskoArtwork,

Gent, Belgium) was used to make the corrugated boxes. The corrugated box

39

were made of C-flute; brand FEFCO 0306 AB the dimensions- were 48.9 x 40.6 x
15.6 cm. Twelve cantaloupe filled containers were packaged in these corrugated
fiberboard boxes, 6 containers per layer, and subjected to random vibration as
described in ASTM 04169 at an Assurance Level II for 60 minutes to represent
approximately 500 miles of truck travel in the United States. The vibration table
(Lansmont Model 10000-10, Inc, Monterey, CA, USA) shown in Figure 4 was
used to generate a power density spectrum in accordance with ASTM 04728.
The cantaloupe filled containers were stored at 4°C: 03°C for 12 hours before
further evaluation and testing. These cantaloupe filled PLA containers were
compared to “control” samples (anti-browning solution + non-vibrated samples)
packaged and stored under identical conditions for the same period of time as

the tested (Figure 5).

   
   
 

    
 
 

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transport vibration

40

 

Figure 3. Cantaloupe filled containers packaged in corrugated box

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subjected to random vibration.

41

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Figure 5. Comparison of control versus minimally processed fresh-cut
cantaloupe after transport vibration

3.1.2 Sensory Evaluation

For each set of treated samples, a non-vibrated control container
containing cut cantaloupe was compared to the vibrated and treated samples.
Sensory evaluation was conducted for preliminary screening for the type of anti-
browning solution to be used in subsequent studies. Also, to determine overall
effect on quality of fresh-cut cantaloupe subjected to transport vibration
compared to control samples not subjected to transport vibration. Appearance,
flavor, texture and overall liking of the cut cantaloupe were evaluated by an
experienced eight-member panel on a quality scale of 1-9 hedonic (9= Like
extremely, 7=like moderately, 5= neither like nor dislike, 3=dislike moderately,

1=dislike extremely) for days 1, 4, 7 and 10. A score of 5 was determined as the

42

limit of marketability. Each panelist was provided with 4 samples (Table 2) in 2 oz
cups labeled with random numbers. The test setup is shown in Figures 6 and 7.

Table 2. Sensory evaluation samples provided to panelists

 

 

 

 

 

Samples Description

Control A Non-vibrated and Treatment A
Tested A Vibrated and Treatment A
Control B Non-vibrated and Treatment B
Tested B Vibrated and Treatment B

 

 

 

 

3.1.3 Instrument Texture Analysis

A Kramer shear press (Model FTA-300,FTC,Stering,VA) was used to
determine flesh firmness at 23°C at days 1, 4, 7 and 10 to compare it with texture
scores from sensory evaluation. A sample holder (6.6 x 6.6 x 6.4 cm) was loaded
with 60 grams of cut cantaloupe cubes. Upon placing the sample holder in the
test cell 10 movable blades were lowered at 20 cm/min, compressing the cut
samples to a distance of 10.2 cm. The force required to compress the test

sample was recorded.

3.1.4 Statistical Analysis

The data was analyzed uSing statistical software Minitab 13.1 (Minitab Inc,
State College,PA, USA). Analysis of variance was performed on sensory and
firmness data and the means were separated using Fisher’s LSD at significance

level of p s 0.05.

43

         
   

  
 

   

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8‘ _ .4 ‘ . . a . _ 3..., . .. £7133 '. . ~. 3' .
Figure 6. CC samples coded with 3 digit random number on a tray for a

panelist

Figure 7.Controlled temerpa‘ture Iroo with fluorescent bulbs for taste
testing fresh-cut cantaloupe

44

3.2 Phase 2

This research was divided into three parts. The first part was to determine
if there is a quality difference in fresh-cut cantaloupe procured from different
commercial or foodservice vendors. The second part was designed to determine
if there is any effect of the container design on fresh-cut fruit quality (Table 3).
The third part was designed to determine the effect of dice size and shape on
fresh-cut fruit quality (Table 4). The treatments to determine effect of container
design are shown in Table 3 as Experimental design-1. The treatments to
determine size of fruit dice are shown in Table 4 as Experimental shown-2. An
illustration of fresh-cut fruit processing, preparing packaged FCC, quantitative
descriptive and physiochemical analysis to determine effect of container design

and effect of dice size is shown in Figures 9 and 10.

3.2.1 Quality of Commercial Fresh Cut Cantaloupe

Commercial fresh-cut melons were procured from six commercial or food
service vendors. A consumer sensory panel of 65 untrained panelists evaluated
melons on a 1-9 hedonic scale for aroma, color, sweetness, firmness and overall
quality. Panelists were recruited from MSU students, staff and faculty of both
sexes. The consumer sensory panel testing was conducted under fluorescent
bulbs and in a temperature controlled room (23°Ct3) dedicated to sensory
testing. Panelists were provided with three blind samples in 202 cups labelled
with a specific 3 digit random code, water and cracker on a tray (Figure 6). A

LabScanXE colorimeter, (Hunter Associates Laboratory, Inc, Virginia,USA) was

45

used to determine fruit color (CIE L*, a* & b* values), total soluble solids was
measured using hand refractometer Atago PAL-1 (Atago Co., Ltd., Tokyo,
Japan), pH and titrable acidy (malic acid) was analyzed and data was collected
at days 1, 4, 7 and 10. A TA-XT2I (Texture Technologies Corporation, New York)
texture analyzer equipped with a probe diameter 50 mm was used to determine

flesh firmness value to compare it with texture score from sensory evaluation.

3.2.2 Effect of Container Design and Dice Size on Fresh-Cut Cantaloupe
The whole cantaloupe was washed and dipped in a commercial sanitizer
(100-ppm chlorine) for 2 minutes. After removing seeds and peels, melons were
cut into Size-2.5cm and Size-1.5 cm cubes. Melon pieces were dipped in a
commercially available anti-browning solution (NatureSealTM) for 2 minutes and
drained in a colander for 2 minutes then packaged in 3 PET containers (A, B and
C) of different dimensions. The pictures of the 3 container design can be seen in
Figure 8. Container A is a square shaped (4.75 inches x 4.75 inches x 1.75
inches) (Appendix A), Container B is a rectangular shaped ( 5.25 inches x 4.75
inches x 2.625 inches) (Appendix B) and Container C is a parfait cup (diameter:
4.7 inches height: 3.1 inches) (Appendix C). Nine PET containers of each
container design were packaged in a customized corrugated boxes constructed
for each container design. Corrugated boxes were designed using ArtiosCad 7.6
(EskoArtwork, Gent, Belgium) and an ArtiosKornsberg Premiumline 1930 cutting
table (EskoArtwork, Gent, Belgium) was used to make the corrugated boxes. The

corrugated boxes were made of C-flute; brand FEFCO 0306 AB. The dimensions

46

for the corrugated box to package ‘Container A’, Container 3’ and ‘Container C’
were 15.75 inches x 14.96 inches x 2.16 inches, 16.92 inches x 14.76 inches x
3.34 inches; 14.76 inches x 14.37 inches x 3.54 inches respectively. The
corrugated boxes for each container design were stacked to a height of 3 feet,
the top two boxes of the stack contained packaged fresh-cut cantaloupe.
Vibration forces magnifies with increasing stack height resulting in maximum
bouncing of fresh-cut fruits in the top layers. Therefore, the fruit dices were
expected to experience repetitive impacts during transportation and loss in
quality. The stack of corrugated boxes were placed on an electro—hydraulic
vibration table as seen appendices DE, and F (Lansmont Model 10000-10, Inc,
Monterey, CA, USA) to generate a power density spectrum in accordance with
ASTM D4728. They were vibrated for two different test times of 60 minutes and
120 minutes (ASTM 4169, Assurance Level II) representing 500 and 1000 mile
shipping distance (ISTA 3J,2006)

Six MSU students (24—27 years) were selected with prior cantaloupe
eating experience to be part of a 6-member trained panel. Panelists were trained
to detect specific attributes including aroma, color, sweetness, firmness and
overall quality. All the panelists participated in training over a period of 3 training
sessions. The trained panel evaluated melons on a 1-15 unstructured scale for
aroma, color, sweetness, firmness and overall quality at days 1, 4, 7 and 10.
Panelists were provided a control sample with three blind samples in 202 cups
labelled with a specific 3 digit random code (Figure 6). The trained sensory panel

testing was conducted under fluorescent bulbs and in a temperature controlled

47

room (23°C:l:3). Panelists were provided with three blind samples in 202 cups
labelled with a specific 3 digit random code, water and cracker on a tray. To
determine effect of container design on quality of FCC, panelists evaluated
samples packaged in either Container A or B or C subjected to the same
vibration time and were of the same fruit dice size. To determine effect of fruit
dice on quality of FCC, the panelists evaluated dice size 2.5cm and 1.5 cm
packaged in the same container design and subjected to the same vibration time.

A TA-XT2i (Texture Technologies Corporation, New York) texture analyzer
equipped with a probe diameter 50 mm was used to determine flesh firmness
value at 1, 4, 7 and 10 days to compare it with texture score from sensory
evaluation. A LabScanXE colorimeter, (Hunter Associates Laboratory, Inc,
Virginia,USA) was used to determine fruit color (CIE L*, a* & b* values) at days
1, 4, 7 and 10. A decrease of L* values indicates a loss of brightness, and a more
positive a* value indicates increase in redness, whereas a more positive
b*indicates increase in yellowness. Total soluble solid contents of fresh-cut fruits
were measured using a hand held refractometer Atago PAL-1 (Atago Co., Ltd.,

Tokyo, Japan) at days 1, 4, 7 and 10. The changes of in-package O2 and CO2
concentrations were measured using an headspace analyzer (6600 O2/CO2

Headspace Analyzer, Illinois Instrument, Illinois, USA) at days 1,4,7 and 10.

48

 

     

, 2"” ‘__. .M .34.:é ..~.'..""
'M
A" E '22!"

Figure 8. PET contai ft to Fight C

Container C

Table 3. Experimental design-1 to determine effect of container design on
ualiy of fresh-cut cantaloupe

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Treatment Container Type Vibration Time (minutes) Dice Size (cm)
Control-1 A 0 2.5
Control-2 B 0 2.5
Control-3 C 0 2.5
Control-4 A 0 1.5
Control-5 B 0 1.5
Control-6 C 0 1.5

Treatment-1 A 60 2.5
Treatment-2 B 60 2.5
Treatment-3 C 60 2.5
Treatment-4 A 60 1 .5
Treatment-5 B 60 1 .5
Treatment-6 C 60 1 .5
Treatment-7 A 120 2.5
Treatment-8 B 120 2.5
Treatment-9 C 120 2.5
Treatment10 A 120 1.5
Treatment-1 1 B 120 1 .5
Treatment-12 C 120 1.5

 

 

 

 

 

 

49

Table 4. Experimental design-2 to determine effect of fruit dice size on quality
of fresh-cut cantaloupe

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Treatment Dice Size (cm) Vibration Time (minutes) Container Type
Control-1 2.5 0 A
Control-2 2.5 0 B
Control-3 2.5 0 C
Control-4 1.5 0 A
Control-5 1.5 0 B
Control-6 1.5 O C
Treatment-1 2.5 60 A
Treatment-2 2.5 60 B
Treatment-3 2.5 60 C
Treatment-4 1 .5 60 A
Treatment-5 1 .5 60 B
Treatment-6 1 .5 60 0
Whole Melon

Sanitize 100ppm Chlorine
Pre-cool 4°C ;12 hours

I

Peel and Cut: Size A

T

Sanitize 100ppm
Chlorine~2 mins J

 

 

 

 

 

Antibrowning
Solution ~ 2mins

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

L
I I L I
Container A Container B 1— Container C 1 Control
ASTM 4169 ASTM 4169 ‘ ASTM 4169 ;
60mins 60mins l 60mins
Store at 4°C I
Av 12 hours 3

 

 

 

 

1

Quantitative descriptive analysis, Physiochemical analysis
Day 1, 4, 7 and 10 days

 

 

 

 

 

 

 

Figure 9. Fresh-cut cantaloupe processing to determine effect of container
design on quality of FCC

5O

 

 

Whole Melon
Sanitize 100ppm Chlorine
Precool 4°C ;12 hours

I

Peel and Cut: Container
A

I

Sanitize 100ppm Chlorine~2 mins
Antibrowning Solution ~ 2mins
Drain ~ 4mins

ASTIthe4169 4169 60mins

60mins L
Store at 4°C l
¢ 12 hours

7

Quantitative descriptive analysis, Physiochemical analysis
Day 1, 4, 7 and 10 days

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 10 Fresh-cut cantaloupe processing to determine effect of fruit dice size
on quality of FCC

3.2.3 Electronic Nose Methodology

In this study, the Fox 3000 E-Nose system was used to analyze the
olfactory profiles of commercially available fresh-cut cantaloupe and fresh-cut
cantaloupe prepared in the laboratory subjected to various , experimental
treatments. A fixed quantity of each fresh-cut cantaloupe (29) was weighed into
10ml glass vial and sealed in triplicate. The samples were loaded in the auto

sampler tray of the E-Nose and activated by Alpha-Mos software. During each

51

cycle, each vial was automatically transferred to the oven and agitator, to
generate headspace volatiles. The headspace volatiles were collected from the
heated vial using a syringe and injected in to the sensor array chamber, to
generate the olfactory response profiles. The experimental run conditions are
shown in Table 5. The data obtained for the various replicates of fresh-cut
cantaloupe samples were analyzed by multivariate statistical methods such as
Principal Component Analysis (PCA) and Partial Least Square (PLS) to
understand the degree of sample discrimination and correlation with sensory
scores of the fresh cantaloupe samples with various treatments.

Table 5. E-Nose system conditions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

System Parameters Run Condition
Sample quantitflgL 1.5
Incubation time (sec) 300
Incubation temperature (°C) 35
Agitation speed (rpm) 500
Syringe type (ml) 5
Syringe fill speed (ul/sec) 500
Syringe temperature (°C) 40
Flushing time 120
Vial type (ml) 10
Injection volume (ul) 5000
Injection speed (ul/sec) 2500
Acquisition time (sec) 600
Acquisition period (sec) 1
Delay (sec) 180
Flow (ml/min) 300

 

 

 

 

3.2.4 Statistical Analysis

The collected data from sensory, color and texture analysis was analyzed using
Minitab 13.1 (Minitab Inc, State College, PA, USA). Analysis of variance was

Performed on sensory, color and firmness data and the means were separated

using Fisher’s LSD at significance level of p s 0.05

52

4. RESULTS AND DISCUSSION
4.1 Phase 1
Effect of Anti-browning Agent and Transport Vibration

4.1.1 Sensory Evaluation

4.1.1.1 Appearance

Fresh-cut cantaloupes (FCC) treated with ‘Treatment A’ (2% Ascorbic
Acid +1% Calcium Chloride + 0.5% Citric Acid) and ‘Treatment 8’ (3%
NatureSeal TM containing Calcium Ascorbate) packaged in PLA containers and
subjected to random vibration for 60 minutes (‘Tested A’ and ‘Tested B’), were
evaluated for various sensory attributes. In general the appearance of the FCC
deteriorated as function of storage time (Figure 11). One day after processing
and vibration there was no significant difference in appearance (Figure 11)
between cut cantaloupe processed according to Treatment A and Treatment B.
However, cut cantaloupe ‘Control B’ (6.63a) appeared to be better than ‘Control
A’ (6.25a), ‘Tested A’ (6.25a) and ‘Tested B’ (6.25a) at day 1. Even though
‘appearance’ deteriorated with time (Figure.7) It was observed that the
appearance scores for ‘Tested B’ (6.13a) samples were higher than ‘Control A’
(5.75a), ‘Tested A’ (5.75a) and ‘Control B’ (5.38a) by day 10. This indicates that

fresh-cut cantaloupe treated with NaturesealTM and subjected to vibration had the

best appearance over a period of 10 days.

53

 

 

       

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

p
1 Appearance
9T“‘”‘"‘“‘”“‘“‘i““"T‘T‘T
8 6.633 6.88b I
7 _ 6.25a 5,00b6-88b 550a 6.50a l
6 1 ' ,,, 4.88a -' 5.75a 5.
'a 5 1 ,
5 I j
;a 4 l ,
3%
I l
2 1 l
1 I
l
Day1 Day4 Day7 Day1 0
Storage Time
lIllControl A EJTested A EControl B DTested B

 

 

 

Figure 11. Sensory panel scores for fresh-cut cantaloupe appearance during
storage; n=8; 1=dislike extremely; 9= Like extremely
*Mean scores with different letters are significantly different
4.1.1.2 Flavor

It was observed that the day 1 flavor scores compared to Day 4 had lower
hedonic scores for all the treatments except for ‘Tested B’ (7.0a) at day 1. Also,
at day 1, 4 and 7 ‘Tested B’ cantaloupe samples had better flavor scores than its
‘Control 8’ samples (Figure.8). Similarly at days 1 and 7 ‘Tested 8’ samples had
higher flavor scores than ‘Control A’ (Figure.12). This is possibly due to ripening
of cut cantaloupe as a result of higher respiration rate and ethylene induced
ripening during storage. Ethylene production as a consequence of cutting has
been observed in tomatoes (Lee et al.,1970), strawberries (Abeles et al., 1992)

and Papayas (Paull and Chen,1997) leading to accelerated ripening. Similarly,

cantaloupe has shown high ethylene release upon cutting (Hoffmann and

54

Yang,1982). Also, several cut fruits have shown higher respiration rates than
whole fruits (Watada et al,1990;Cantwell, 1992) leading to shorter shelf life.
Therefore accelerated ripening due to increased ethylene production and
respiration rate can explain better ‘flavor’ scores at day 1, 4 and 7 for the
samples which were exposed to mechanical vibration. According to Oliu and
Fortuny et al. (2007) study an increase in ethylene production accelerated
ripening. Also, physical damage to fruits was enhanced by mechanical vibration
resulting in a change in quality (Jarimopas et al.,2005). This indicates that
vibration during distribution has some positive effects in enhancing flavor of
fresh-cut cantaloupes. However at the end of the study ‘Control A’ samples had

the highest flavor scores (Figure.12) (Jarimopas et al., 2005).

 

 

          

 

 

 

 

 

 

 

 

 

 

 

 

Flavor
97 —-_ s,_-___- _ --___,,_,
3 I 7.000 6.75a l
7 1 6.008 - 6253 5-003 6.50a 625 5.75a 1
5.88a E-fl 5.88a 575 . a ,
1 .- .. . 4.50a 5.63a.
3‘ 6 l5.38a I:: _. ‘
.3 5 J I. IIIIIIII
5 :5 515:3:
2 4 I : 2222522;
2 55555553
1 1 2:55:55;
0 in» - - w--.
Day1 Day4
Storage Time
lllControl A ElTested A EControl B flTested B #

 

Figure 12. Sensory scores for fresh-cut cantaloupe flavor over storage period;

n=8; 1=dislike extremely; 9= Like extremely _
*Mean scores with different letters are Significantly different

55

4.1.1.3 Texture

Overall the texture of all the sample treatments deteriorated with time
except for ‘Control A’ samples (Figure 9). ‘Control A’ samples had the highest
texture scores (6.75) at day 4 and day 10 (7.0) compared to day 1 samples
(5.75) (Figure 13). Initially, ‘Tested 8’ had the best texture scores but it
deteriorated with time from a score of 7.13 at day 1 to 5.88 at day 10. ‘Control A’
samples had the best texture (7.0) at day 10 compared to the remaining
treatments. The texture of ‘Tested A’ and ‘Control 8’ was observed to be similar
at days 1, 4 and 7 (Figure13). At the end of the study the “Control A’ samples
(7.0a) were observed to have the best texture followed by ‘Control 8’ (6.0a),
‘Tested 8’ (5.88a) and ‘Tested A’ (5.38b) samples by day 10. The FCC control
samples were wounded by cutting which has an effect on the texture of cut fruit
(Lamikanra et al., 2003), the ‘Tested A’ and ‘Tested B samples were subjected
to random vibration causing further wounding of the cut surface in addition to

cutting wounds. This explains the better texture of the FCC control samples than

the ‘Tested A’ and ‘Tested 8’ samples.

56

 

 

 

 

 

     

Texture

        

 

         

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

9 _c__- _.__-_,E ___ _.fi_.___-_ _E_ _ _-h
7.13a 6.75a .
8 6.63ab 5 883 I
6.38ab - , ' 7.00a 6.00a ,1
7 55;. 5.888 6.38a
5.75bi =0 ' 6 00a 6.258
6 55*;;'_- ' 5_ ; 5-888 5.38b 5.88a:
a E:
'5 5 g; i
5 E5: {
E 4 5 :55 l
3 E55 1
I .5:-
2 i E5 ‘
l E:;
1 1 E5:
0 L E5 5 -, . _ , . . a
Day1 Day4 Day7 Day1 0
Storage Time
L lIlControl A ElTested A BControl B BTested B

 

 

 

 

 

 

 

 

 

 

 

Figure 13. Sensory scores for fresh-cut cantaloupe texture over storage;n=8
period;1=dislike extremely; 9= Like extremely

*Mean scores with different letters are significantly different

4.1.1.4 Overall-liking

initially it was observed that ‘Tested 8’ samples had the highest overall-
liking score of 7.0 at day 1 followed by ‘Control 8’ (6.5ab), ‘Tested A’ (5.75ab)
and ‘Control A’ (5.63b) (Figure.10). At day 4 ‘Control A’ samples were rated to
have the highest overall score of 7.13. ‘Tested A’ samples were preferred over
‘Control A’ samples at day 7. Similarly, ‘Tested 8’ samples had higher overall
scores than ‘Control A’ at day 1, 4, 7 and 10 (Figure.14). According to Bai et al.,
(2001) fresh-cut cantaloupe had a shelf life of 9 days in naturally modified
atmosphere packaging. An overall acceptability score of greater than or equal to

5 was deemed acceptable in this study. Therefore, ‘Tested A’ reached the end of

shelf life by day 10. However, the control samples and ‘Tested 8’ samples were

57

still acceptable at the end of day 10. This indicates that with proper post cutting

treatment there can be a positive effect of vibration on fruit quality during storage.

 

 

 

 

 

 

 

 

 

 

 

    

 

 

 

 

 

 

 

[ 9 Overall Liking
) 7.00a 7.13a if 7‘ — fl!
3 6.50ab ,
6.50a 5 253 6 00
7 6.13a 6138 ' ' a
6.003 6.13a
463,) 6.25al
6 5}; 563a :. . 5.883 - ‘
5;? 5 l
5
g 4
3 7
l
2 _
1 4
0
Day1 Day4 Day7 Day1 0
Storage Time
HIControl A ElTested A EControl B ETested B

 

 

 

Figure 14. Sensory scores for fresh-cut cantaloupe overall liking over
storage;n=8; period; 1=dislike extremely; 9: Like extremely
*Mean scores with different letters are significantly different

4.1.2 Firmness Measurements

A general trend in firmness value was not observed as a function of
storage time. By day 10, the firmness values for ‘Control A’ samples (178.23 lb)
were observed to be the highest followed by ‘Tested 8’ (159.20b), ‘Control B’
(149.20b),and ‘Tested A’ (94.02c).Comparing this trend to texture scores as
observed at day 10 by the panelists (Figure13), it can be seen that cut fruit
firmness is related to its texture during storage. Also, the firmness of the cut
cantaloupe was observed to decrease somewhat from day 1 to day 10 for

‘Tested A’ and ‘Tested 8’ samples (Figure 15). Thus, there is a distinct effect of

58

vibration on firmness values during transportation. Hence, softening of
cantaloupe flesh can be expected as these samples are subjected to vibrational
forces during the transportation. This is due to wounding of surface tissue caused
by repeated impacts during vibration (Jarimopas et al., 2005). It was interesting
to find that vibration tested out cantaloupe with ‘Treatment B’ (159.20 lb) had
higher firmness values than ‘Treatment A’ (94.02 lb) at day 10. This indicates that
commercially available anti-browning solution NaturesealTM performs better in
preserving cut fruit texture in a transport environment than an anti-browning

solution prepared in the laboratory (2% Ascorbic Acid +1% Calcium Chloride +

 

 
 
  

 

     
  

 

 

 

0.5% Citric Acid).
Firmness

”Or-r ‘ — * ‘ "18517— "’— _, 1
16477 . a 178.23 a 159.20 b .

180 '1' 144.87a a 158403 1325503 149.90b

160 J 132.173 156.238 155.208
”12193 145503 112.56b 940—5“? l
140 1 ;:;j; E: i l
g 120 1 {3;}; 109.47b E: i l
moi E2 2 i
.2 801 E5 5
so 4‘ ‘35 5 i
40 i, E5 5 N
20 l E; i ,
0’5 5' , E' -;,
Day1 Day4 Day7 Day1 0
Storage Time
lllControl A ElTested A EControl B BTested B

 

 

 

 

 

 

 

Figure 15. Kramer firmness of fresh-cut cantaloupe over storage period;n=8;
*Mean scores with different letters are significantly different

59

4.1.3 Key Findings of Phase 1

Vibration of fresh-cut—cantaloupe packaged in sample containers had a
positive effect on the flavor and overall acceptability. The ‘overalloliking’ scores
were higher than a quality score of 5 (like slightly) for all samples except for
vibrated-Treatment-A. The texture of vibrated cut-cantaloupe (Treatment A)
deteriorated with time. There was some evidence of correlation between texture
scores and firmness values at day 10 (Figures 12 & 15), which shows that flesh
firmness and texture scores at the end of the storage period was better in control
samples compared to vibration tested samples. Thus, it can be said that
vibrational forces during transportation has an effect on texture quality of cut
cantaloupe. Treatment-B (‘NatureSealm’) fresh-cut—cantaloupe subjected to
vibration performed better than Treatment-A for appearance, flavor, texture and
overall acceptability. No sliminess or mold growth was observed in any of the
samples during a 10-day storage study. Based on these findings, the anti-
browning solution ‘NatureSealTM’ was selected as a post cutting treatment of

fresh-cut cantaloupe for subsequent experiments.

60

4.1

4.1

de

WE

 

slal- ..l.~l||.l..l .rl.

4.2 Phase 2
4.2.1 Quality of Commercial Fresh-cut Cantaloupe

This part of the overall project was performed to understand consumer
desires for fresh-cut cantaloupe, by researching what was currently available
from various commercial sources. The methodologies developed in this study
were used to train sensory panelists in the succeeding studies. Also, information
from this study was used in selecting package for the study. Fresh-cut
Cantaloupe (FCC) was procured from 6 different commercial retail and food
service vendors (Meijer lnc., Kroger Co., L&L Food Center Inc., Sysco Corp.,
Coastal Produce Distributor and Del Monte Corp.). Fresh-cut cantaloupe was
packaged in various sizes of rigid containers. There was no uniformity in dice
sizes between commercial suppliers. It was found that Meijer and Kroger
performed the fresh-cut operation in their backroom using whole fruits that were
on the shelf in the store and which were harvested 1-2 weeks prior to reaching
the store. A consumer panel of 65 untrained panelists evaluated these samples
on a hedonic scale 1-9 for aroma, color, sweetness, firmness and overall quality.

Upon completion of the sensory analysis of the fresh-cut cantaloupe, it
was found that aroma characteristics of Sysco FCC were rated the highest by the
consumers followed by Meijer, Del Monte, Kroger, L&L and Coastal (Figure 16).
Sysco FCC was rated the highest (7.54) and the lowest was Coastal FCC (6.30)
(Figure 16.). Aroma ratings for Sysco and Meijer FCC were significantly higher
than Del Monte, Kroger, L&L and Coastal FCC. Significantly higher aroma ratings

for Sysco and Meijer FCC could be attributed to the harvest maturity and/or the

61

 

S

 

post harvest shelf life of the whole fruit. It is possible the whole fruits used by
Sysco and Meijer were harvested at or more than 1/2 slip maturity, which could
explain the higher level of sweet aromatic volatile compounds in the package
headspace (Beaulieu et al., 2004). Similarly, it is possible that the whole
cantaloupe fruit was from traditional shelf life category, which has been reported
to have higher total volatile content than long shelf life and extended shelf life

cantaloupe cultivar (Lamikanra et al., 2003).

 

SYSCO. Meijer,
7-443 Delmonte,

7 6.84b Kroger, LL. Coastal,
.. 6‘51b 6.41bc 6.30s

Intensity

 

Aroma

 

 

_‘

Figure 16. Consumer panel (n=65) mean aroma scores for commercially
available fresh-cut cantaloupe

Mean scores with different letters are significantly different (p<0.05)

The color characteristics of Meijer FCC were rated the highest by
consumers followed by Del Monte, Sysco Kroger, L&L and Coastal (Figure 17).
Meijer FCC was rated the highest (7.88) compared to Coastal FCC (7.31) (Figure

16.). Color ratings for Coastal FCC were significantly the lower than Meijer and

62

Del Monte FCC. Consumers ratings indicated that panelists liked the orangish-
yellow color that was more evident in the Meijer and Del Monte FCC compared to
Coastal FCC (Figure 17). This is supported by the color values L* ,a *and b* as
seen Figures 22,23 & 24. In Figure 22 it can be seen that Del Monte FCC had
significantly lower L* values than Coastal FCC, indicating that Coastal FCC was
lighter and more translucent compared to Del Monte FCC. Similarly, Del Monte
FCC had significantly lower b* values (Figure 24) compared to all the FCC
samples, which indicates that Del Monte FCC had a orangish color, which the

consumers seemed to like.

 

 

9 _y _ _
Meijer, Delmonte, Sysco Kroger LL
8 . 7388 7.713 7.53ab 7.48ab

Coastal
7.48ab 7.31b

       

........
........
........

 

0'!

Intensity

A
_L

21 ;3:3;?::;:;t;s;:

1T 33333331E23233j2

 

 

0 lb“--__,

 

 

_¥

Figure 17. Consumer panel (n=65) mean color scores for commercially available
fresh-cut cantaloupe

Mean scores with different letters are significantly different (p<0.05)

According to the consumer panel, Sysco FCC was rated to have the
highest sweetness ratings (7.59) and was significantly different than the

remaining FCC procured from the different sources (Figure 18). This is supported

63

by the total soluble solids results showing that Sysco TSS had the highest

compared concentration to the other FCC products (Table 6).

 

Meijer Delmonte
6.55b 6.55b LL
6.06

Coastal
4.8d

Intensity

 

Sweetness

 

Figure 18. Consumer panel (n=65) mean sweetness scores for commercially
available fresh-cut cantaloupe; Mean scores with different letters are significantly
different (p<0.05)

Significantly higher sweetness ratings for Sysco FCC could be attributed to the
harvest maturity and/or the post harvest shelf life of the whole fruit. It is possible
that the whole fruits used by Sysco were harvested at or more than 1/2 slip
maturity, which could explain the higher level of sweet aromatic flavor
compounds (Beaulieu et al., 2004). Similarly, it is possible that the whole
cantaloupe fruit cultivar was from a traditional shelf life category, which is
expected to be sweeter (Lamikanra et al., 2003). Titratable acidityand pH did not

seem to play a significant role in the sweetness ratings of the FCC samples

(Table 6).

64

 

 

.' ibn‘ l

Table 6. Difference in pH, Titrable acidity and total soluble solid
concentration of commercial fresh-cut cantaloupe

 

 

 

 

 

 

 

 

 

 

 

 

 

 

pH TA TSS

Meijer 7.1 0.0223 8.0
Kroger 6.8 0.0603 8.0
LL 7.4 0.0313 8.5
Sysco 7.1 0.0357 10.8
Coastal 6.6 0.0715 8.0
Delmonte 6.9 0.0737 7.8

 

 

 

 

 

Consumers were not able to distinguish a significant difference in texture or
firmness between the FCC samples (Figure 19). However, Sysco FCC had the
highest firmness rating (6.83) and Coastal FCC had the lowest firmness rating.
Variability within the FCC samples using untrained panelists could have led to
indistinguishable texture results. However, the texture analysis values obtained
from the texture analyzer, shows that Sysco had the highest firmness value of
168.86 N followed by Kroger, L&L, Coastal, Del Monte and Meijer FCC
(Figure.21).

Overall, the consumers rated Meijer FCC as the best followed by Sysco,
Kroger, Del Monte, L&L and Coastal FCC (Figure 20). Overall quality was a
collective response of aroma, color, sweetness and texture of the sample. Meijer
and Sysco FCC samples were consistently rated to have aroma, color,
sweetness and firmness ratings in the top 3 compared to the remaining FCC

samples. The sensory, color and firmness results from this part of the study were

used to train panelists in the succeeding part of this research study.

65

 

Sysco. Delmonte Meijer LL Kroger Coastal

6.54a

6.78a 6.78a 6.76a 6.68a

Intensity

 

F irmness

 

 

 

Figure 19. Consumer panel (n=65) mean firmness scores for commercially
available fresh-cut cantaloupe

Mean scores with different letters are significantly different (p<0.05)

 

     
       
     
    

 

 

    

   

 

 

 

9 E» - -_-_-# --_ _ ._,_
{ Meijer
3 .
Kroger Delmonte LL
« ::-::-'.:.;.;.:.: 6.60b
7 2f§f§§gfj§f§f§ 652*” 6.210
6 ‘ Coastal
2: é§;§€}:§é31;§§5 4.92d
5 'iif.i:i;::f;ij
E 4 ‘
3*
2 ,
1 l 252:;ifiiiéiii2i
0 __ ij;3j3;ij?;§ji
Overall Quality

 

 

 

 

Figure 20. Consumer panel (n=65) mean overall quality scores for commercially
available fresh-cut cantaloupe. .Mean scores with different letters are Significantly
different (p<0.05)

66

“An—5‘ "-—

 

ll!\

.‘.41.

 

 

E3 Sysco B Kroger LL Cl Coastal B Delmonte Meijer)

200

180

160 150.92b
138.80c

Force (N)
a A
o

 

F irrnness

 

 

 

Figure 21. Mean firmness measurements for commercially available fresh-cut
cantaloupe Mean scores with different letters are significantly different (p<0.05)

 

100
90
'80
Kroger
70 65.07a Coastal Meijer
60.49!) 59.74b LL Sysco
t
6° 53.95c 53.950 Dem”

50
40
so
20
1o

 

0

Li

 

 

 

Figure 22. Average L* value for commercially available fresh-cut Cantaloupe
*Mean value with different letters are significantly different (p<0.05)

67

 

M8516". LL. Sysco, Delmonte. Coastal, Kroger
16.76 16.15 16.15 15.40 14.90 1471'

 

 

 

 

Figure 23. Average a* value for commercially available fresh-cut cantaloupe
Mean value with different letters are significantly different (p<0.05)

 

 

LMeijer LL El Sysco B Coastal B Kroger B Delmonte]

 

 

 

 

   

 

 

 

50 T______-_ —.—*~* ~ - Am » ——— Tfl
45 Meijer LL Sysco
4o-
351 36553 34.92ab 3492ab 3325;: $0556 031111106116
30 ~
20 ~
5
0 s;5§2';s§s.§_5§532
b.

 

 

Figure 24. Average b* value for commercially available fresh-cut cantaloupe
Mean value with different letters are significantly different (p<0.05)

68

4.2.2 Effect of Container Design

A consumer’s decision to buy fresh-cut fruit is dependent on its quality.
Quality is dependent on sensory characteristics of the product at the time of
purchase. Sensory quality of these products is a cumulative effect of aroma,
appearance (color), flavor (sweetness) and texture, and the overall quality of the
product is assessed by the consumer. The two most important quality indicators
for cantaloupe are color and sweetness (Fisher and Bennett, 1991 and Gross
and Sams, 1984). In addition, cutting operations and transportation can result in
broken cells and tissue damage leading to fruit decay. Therefore, the texture of
cut fruit has a significant effect on the fresh-cut cantaloupe (FCC) quality. Quality
is maintained during storage through a combination of various post harvest
treatments, post-cutting treatments, modified atmosphere packaging and storage
temperature. A key factor which remains to be understood is can packaging
design affect the quality of fresh-cut cantaloupe. Sensory evaluation was
performed on fresh-cut cantaloupe (Size-2.5cm and Size-1.5cm) packaged in
“Container A’ square shaped (4.75 inches x 4.75 inches x 1.75 inches),
Container B rectangular shaped ( 5.25 inches x 4.75 inches x 2.625 inches) and
Container C (Cup top diameter: 4.7 inches height: 3.1 inches) to understand the
effects of container design and transportation.

The headspace Oz and C02 concentrations in the package during storage are
shown in Tables 9-10. The lower limit for 02 below which fruit injury such as
discoloration or other disorder can occur for fresh-cut cantaloupe is

approximately 3% at 4°C (Beaudry, 2000). The oxygen concentration in all the

69

containers ranged between 17—18% during 10 days of storage. Therefore the risk
of fruit decay due to low 02 concentration was minimal. Similarly, Oi et al, 1999
found that a modified atmosphere package of 2% 02 + 10% C02 in the package
headspace was beneficial in maintaining quality and retarding increased
metabolism and microbiological growth. The 002 concentration in all 3 container
package headspace ranged from 2-4% during the storage period. This was within
the tolerance level of C02 (10%) for fresh-cut cantaloupe (Tables 11-12). Beyond
this level fresh-cut cantaloupe can be susceptible to fruit decay caused by
undesirable levels of oxygen and carbon dioxide.

The trend for the aroma intensity of fresh-cut cantaloupe showed an
increasing intensity for all 3 container designs during storage (Figure 25 and 34).
This trend was observed for both fruit sizes (2.5cm and 1.5cm) subjected to a
transport vibration time 60 mins (500 miles) (Figure 25 and 34). The Aroma
intensities of the FCC packaged in ‘Container 8’ and ‘Container C’ were rated
higher than ‘Control’ and ‘Container A’ FCC samples. This was expected as
Container B and Container C are taller and have more void spaces than
‘Container A'. This can lead to more frictional damage of the cut fruits during
vibration causing further tissue damage and release of sweet aromatic volatiles.
It was observed that FCC (Size-1.5cm) had higher aroma intensity when
packaged in Container C at days 1, 4 and 7 compared to Container A and
Container B.

The color intensity of FCC increased during storage during storage for both

fruit sizes and subjected to a transport vibration time 60 mins (500 miles) for all

70

types of containers (Figures 26 and 35). FCC samples packaged in ‘Container 8’
and ‘Container C’ were rated to have the higher color intensity compared to
control and ‘Container A’ FCC samples. Color ratings were higher for FCC
samples which underwent transport vibration to simulate a 1000 mile trip
compared to FCC samples which were simulated to travel a 500 mile trip (Figure
26 & appendix G). The ClE L* values for FCC samples showed a gradual
decreasing trend during storage (Figures 30 and 39). Lower L* color values
indicate a darker surface. Container C FCC samples had the lowest L" values by
day 10 indicating a darker surface color than FCC sample packed in Container A
and B (Figures 30 and 39). This suggests that there was accelerated fruit
degradation due to enzymatic browning as result of tissue damage in Container
C compared to Container A and B FCC samples. A similar trend was observed
for a* values (Figures 31 and 40) and b* values (Figures 32 and 41). The CIE
color value 3* indicates redness and b* indicates yellowness on the cut surface.
It was observed that during storage a* and b* values decreased for all the FCC
samples. ‘Container A’ FCC samples had highest a* and b* values indicating
yellowish-reddish color compared to Container B and C FCC samples. As
discovered from the study mentioned in the previous section, consumers liked
the more yellowish-reddish color. Therefore, on the basis of color analysis,
Container A maintained the best color quality of the fresh-cut cantaloupe followed
by Container B and Container C.

The sweetness of the FCC sample increased during storage for both fruit

sizes and transport distances for all types of container (Figures 27 and 36). FCC

71

samples exposed to transport vibration conditions were more sweet than control
samples during storage (Figures 27,36 and appendix G). Container B had the
highest sweetness rating at the end of the storage study as seen in Figure 27 for
Size-2.5cm. Whereas Container B and C had higher sweetness rating than
Container A and control samples at day 10 for Size-1.5cm (Figure 36). This is
supported by the total soluble solids content where it was observed that fresh-cut
cantaloupe packaged in ‘Container B and “Container 0’ had higher %TSS than
‘Container A’ at Day 10 (Table 7-8), for both fruit sizes at the end of the storage
stage. This effect was more evident for FCC samples which were simulated to
travel a 1000 mile trip (Appendix G).

Firmness of FCC samples decreased during storage for both fruit sizes and
transport distances for all types of container (Appendix G). Control samples (un-
vibrated samples) were rated to more firm than FCC samples subjected to
transport vibration (Figures 28 and 37). Container C FCC samples were found to
have the lowest firmness at the end of the storage study as seen in Figures 28
and 37. This was more evident for FCC Size B (1.5cm) samples which were
simulated to travel a 1000 mile trip (Appendix G). Texture analysis to measure
firmness of fresh-cut cantaloupe cubes was performed during the storage period.
It was found that flesh firmness decreased for all the FCC samples during
storage (Figures 33 and 42). The firmness values of FCC samples packaged in
Container C were significantly lower by Day 10 compared to FCC samples stored
in Container A and B (Figures 33 and 42). This indicates that there is more

frictional damage of surface tissue of FCC samples in Container C, caused by

72

vibration during transportation. It was also observed that FCC samples exposed
to a 1000 mile trip had lower firmness values than FCC samples exposed to a
500 mile trip in Container C (Appendix G). The firmness values for FCC samples
in Container A and B were not affected as much by the simulated shipping
distances.

Overall quality of fresh—cut cantaloupe packaged in ‘Container 8’ had the
highest firmness for both fruit sizes and shipping distances during storage
(Figures 33-34, 51-52). Container B FCC samples had higher overall quality
during Day 4 and 7 as seen in Figures 33-34, 51-52.

Sensory analysis, color analysis (CIE L*,a* and b*) and texture analysis of
fresh-cut cantaloupe samples suggests that Container B is capable of
maintaining better sensory quality attributes compared to Container A and C.
Fresh-cut cantaloupe packaged in Container B a medium height container with
sloping side walls had better sensory characteristics than Container A (shallow
height and straight wall) and Container C (T all height and sloping side wall). It
was found that shallow height container will preserve better texture properties
during transportation. However, the trained panel rated fruit dices packaged in
Container B higher for overall quality as they were firm and juicier than FCC
packaged in Container A. Similarly, FCC sample packaged in Container C with a
taller height and sloping side walls showed poorer sensory characteristics and

firmness measurements compared to FCC samples packaged in Container B

rigid containers.

73

 

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Table 7. Change in percent total soluble solid concentration of fresh-cut

 

 

 

 

cantaloupe (2.5cm) in Container A, B & C during storage
Container 1 4 7 10
A 8.0101 8.3102 8.4102 82101
B 8.2101 8.6101 8.5102 8.5102
C 8.1102 8.4102 8.3102 8.5101

 

 

 

 

 

 

Table 8. Change in percent total soluble solid concentration of fresh-cut

cantaloupe 1.5cm) in Container A, B & C during storage

 

 

 

 

 

 

 

 

 

Container 1 4 7 10
A 7.9101 8.1102 8.2103 8.2101
B 8.1102 8.3101 8.3102 8.5102
C 8.0101 8.1102 8.2101 8.4101

 

Table 9. Percent 02 concentration of fresh-cut cantaloupe (2.5cm) in Container

A, B & C duringgstorage

 

 

 

 

 

 

 

 

 

 

Days Stored
Container 1 4 7 10
A 18.4121 17.5131 19.1121 18.8131
B 18.5123 17.9124 17.9125 17513.2
C 18.2121 17113.1 17813.1 17.6121

 

Table 10. Percent 02 concentration of fresh—cut cantaloupe (1.5cm) in Container

A, B & C during storage

 

 

 

 

 

 

 

 

 

 

Days Stored
Container 1 4 7 10
A 18.2121 17.4124 19.3125 18.3121
B 18.8124 17.7125 17.9121 17.7123
C 18.4125 18.7129 17.1125 17.5126

 

Table 11. Percent 002 concentration of fresh-cut cantaloupe (2.5cm) in
Container A, B & C durigq storage

 

 

 

 

 

 

 

 

 

 

Days Stored
Container 1 4 7 10
A 3.5108 5111.3 2910.9 2911.9
B 3.5111 3.9116 4.8113 25121
C 3.8105 4211.8 4.8116 2.7122

 

Table 12. Percent C02 concentration of fresh-cut cantaloupe (2.56m) in
Container A, B & C during storage

 

 

 

 

 

 

 

 

 

 

 

 

Days Stored
Container 1 4 7 10
g A 3.3107 5711.3 3.3107 2910.8
__ B 3.7105 3.9112 4.9109 2.5109
g C 3.9107 4.3113 4.8112 2511.1

 

74

 

 

 

 

 

 

 

 

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Figure 25. Trained panel (n=6) mean aroma scores of fresh-cut cantaloupe (Size
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Figure 26. Trained panel (n=6) mean color scores of fresh-cut cantaloupe (Size -
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Figure 27. Trained panel (n=6) mean sweetness scores of fresh-cut cantaloupe
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Figure 28. Trained panel (n=6) mean firmness scores of fresh-cut cantaloupe
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76

 

 

 

 

 

 

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Figure 30. Effect of container design L* values of fresh-cut cantaloupe (Size -
2.5cm); 500 mile trip

77

 

 

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Figure 31. Effect of container design a* values of fresh-cut cantaloupe (Size-
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Figure 32. Effect of container design b" values of fresh—cut cantaloupe (Size-
25cm); 500 mile trip

78

 

 

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Figure 33. Effect of container design on firmness values of fresh-cut cantaloupe
(Size -2.5cm); 500 mile trip

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 34. Mean aroma scores of fresh-cut cantaloupe (Size -1.5cm) as an effect
of container design; 500 mile trip

79

 

 

 

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Figure 36. Trained panel (n=6) mean sweetness scores of fresh-cut cantaloupe
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80

 

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Figure 37. Trained panel (n=6) mean firmness scores of fresh-cut cantaloupe
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Figure 42. Effect of container design on firmness values of fresh-cut cantaloupe
(Size-1.50m); 500 mile trip

83

4.2.3 Effect of Dice Size

Quality of fresh-cut fruit can be affected by the type of cut or cutting shape
(Lopez et al., 2005 and Aguayo et al., 2004). This study was performed to
explore the effects of transportation on cube sizes (fresh-cut cantaloupe) and
package design (3 types of containers) on the sensory properties such as color
and texture quality components of fresh-cut cantaloupe.

The aroma intensity for both cube sizes (2.5cm and 1.5cm) increased
during storage (Figures 43, 48 and 53). Unexpectedly, fresh-cut cantaloupe cube
sizes did not affect the aroma intensity in the package headspace. However, the
FCC samples subjected to vibration during transportation showed significantly
higher aroma intensity than the control samples during storage. This behavior
was consistent with both types of cube sizes packaged in all 3 types of
containers s (Figures 43, 48 and 53).

F resh-cut cantaloupe color increased during storage for both cube sizes in
all 3 types of containers (Figures 44, 49 and 54). The color intensity of cubes was
distinctly different from control (non-vibrated) samples at Day 4 and 7 for all 3
container treatment. The smaller cube size-15cm showed higher color rating for
FCC samples packaged in Container A and B at days 1,4 7 and 10 (Figure 44
and 49). Whereas in Container C FCC samples size-2.5cm and size-1.5cm had
the same color rating by day 10 (Figure 54). Similarly, transportation simulation
affected color of cube cut surfaces significantly compared to control samples
(Figures 58, 61 and 64). The CIE L*,a* and b* values decreased for both cube

Size-2.5cm and 1.50m in all 3 packaging containers during storage (Figures 58-

84

66). FCC Size 1.50m was significantly darker than Size 2.5 (Figures 58,61 and
64). This indicates that Size-1.5cm was more susceptible to tissue damage and
fruit decay caused by vibration during transportation, than Size-2.5cm samples.
Similarly, a* and b* values indicate the red and yellow color of the flesh
respectively. It was observed that fresh-cut cantaloupe samples for cube Size
2.5cm had a higher degree of yellowish-red color (orangish) compared to Size
1.5cm (Figures 59,62 and 65). As discussed earlier (Quality of Commercial
Fresh-cut Cantaloupe) consumer rated melons with a brighter yellowish-red color
higher than darker yellowish-red color. Therefore, the color analysis results
suggest that fresh-cut cantaloupe prepared to a cube size of 2.5cm is better
suited to withstand vibrational damage during transportation, thus maintaining the
desirable yellowish-red color during storage.

The sweetness intensity for both cube sizes (2.5cm and 1.5cm) increased
during storage (Figures 45, 50 and 55). The control samples had lower
sweetness ratings than FCC samples subjected to transportation abuse during
storage. Fresh-cut cantaloupe cube Size-2.5 cm had significantly more
sweetness than Size-1.5cm for samples packaged in Container B and Container
C (Figures 50 and 55). The FCC Size-1.5cm cubes were susceptible to more
frictional damage than Size-2.5cm FCC samples, as a result of vibration during
transportation. This can result in increased respiration rate and enzymatic
degradation due to tissue wounding and cell damage resulting in decreased
sweet aromatic compounds (Lamikanra and Watson 2000, Lamikanra and

Watson, 2001 and Lamikanra and Watson, 2004).

85

Fresh-cut cantaloupe (FCC) sample firmness decreased during storage
for both cube sizes, this was consistent with all 3 containers (Figures 46, 51 and
56). As illustrated in Figures 46, 51 and 56, control and Size-2.5cm FCC samples
had significantly higher firmness ratings than Size-1.5cm at days 7 and 10, for all
3 containers. This indicates that a cube size of 1.5 cm is more susceptible to
textural damage during transportation compared to a cube size of 2.5cm.
Quantitative descriptive analysis of FCC firmness was supported by instrument
texture analysis results as shown in Figures 67, 68 and 69. Texture analysis of
fresh-cut cantaloupe indicates that cube Size-2.5cm required significantly higher
compression force compared to cube Size-1.50m (Figure 67, 68 and 69). In
addition, the textural properties of cube Size-1.5cm was adversely affected when
packaged in Container C. This indicates that a cube size of 1.5 cm is susceptible
to extensive surface tissue damage during transportation when packaged in a
deeper container (Container C-3.1 inches) compared to a shallow container
(Container A-1.75 inches and Container B-2.625 inches).

The overall quality of fresh-cut cantaloupe deteriorated for both Size-
2.5cm and Size-1.5 cm samples packaged in ‘Container B’ and ‘Container C' but
had a higher overall quality than control samples (Figures 52 and 57). Whereas
FCC samples in ‘Container A’ did not show much difference in overall quality
during storage (Figures 47). This indicates that aroma, sweetness and texture of
FCC samples packaged in Container A did not appeal to the panelists compared
to the FCC samples packaged in Container B and C. Panelists were trained to

look for sweet aromatic flavor compounds and reddish-yellow color. FCC

86

samples were less susceptible to physical abuse during transport vibration in
Container A. This could be explained since Container A was shallower and the
dices were arranged in one layer compared to multiple layers in Container B and
randomly oriented dices in Container C suffered more surface tissue damage.
Therefore, dices in Container A had less sweet aromatic compound released
during transport vibration and a lighter flesh color resulted during storage. It was
observed that sample Size-2.5cm had relatively higher overall quality than Size-
1.5cm in Container B and Container C. This indicates that a cube size of 2.5cm is
less susceptible to tissue damage and fruit decay during storage and
transportation.

Sensory analysis, color analysis (CIE L*,a* and b*) and texture analysis of
fresh-cut cantaloupe samples suggested that a fruit size of 2.5cm is capable of
maintaining better sensory quality attributes compared to fruit size 1.5cm during
transportation and storage. Fresh-cut cantaloupe Size-2.5 cm had better sensory
characteristics than Size-1.5cm packaged Container A, B and C. It was found
that Size-2.5cm preserved better texture properties than 1.50m during
transportation. A smaller size fresh-cut cantaloupe cube was found to be more
susceptible to surface tissue damage during transportation as determined by
texture analyzer. Similarly, color properties were better maintained for Size-
2.5cm during transportation than Size-1.5cm packaged in Container A, B and C
rigid containers. This suggested that the overall quality of fresh-cut cantaloupe
will be best maintained during transportation when cubes are diced to a size of

2.5 cm.

87

 

 

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Figure 55. Trained panel (n=6) mean sweetness scores of fresh
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Figure 67. Effect of fruit dice size on firmness values of fresh-cut cantaloupe
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of fresh-cut cantaloupe

101

4.2.4 Correlation of E-Nose and Sensory Results

Multivariate statistical techniques were used to analyze olfactory response
data from E-Nose. The degree of discrimination between fresh-cut cantaloupe
were studied using principle component analysis (PCA). The correlation between
E-Nose and sensory results were determined using partial least square
technique.

Principal component analysis was used to ascertain the similarity or
dissimilarity between fresh-cut cantaloupes, as an effect of different treatments
and to understand the relationship between E-Nose sensor responses. Principal
component analysis involves recognizing patterns of association in multivariate
data sets. When PCA is applied to a data set, the E-Nose sensor responses are
mathematically converted to a new set of variables called components. Each
component is expressed as linear combination of the original E-Nose sensor
response. The principal component (PC1) explains the maximum amount of
variation possible in one direction for given data set. Thus, PCl contains the
maximum amount of information. The second principle component (P02) is
orthogonal to PC1 and explains the maximum amount remaining variation (Alpha
MOS Fox 3000 Manual). The degree of discrimination indicated how well the
sensor responses are able to distinguish between the different treatments of
fresh-cut cantaloupe, based on their olfactory profile. A high degree of
discrimination would imply that the E-Nose is capable and efficient in
discriminating the fresh-cut cantaloupe subjected to different treatments (storage

days and transportation distance). Such a procedure can be used to determine

102

aroma volatile difference in fresh-cut cantaloupe (FCC) prepared from whole
fruits of different maturity and ripeness (Beaulieu et al, 2004 and Oliu and
Fortuny, 2007).

The partial least squares (PLS) method was used to correlate the E-Nose
sensor responses of the different FCC procured from various commercial
vendors and FCC subjected to different experimental treatments, to the sensory
analysis results. PLS is based on a linear regression technique, which is used to
extract the quantitative information. The data collected from E-Nose sensors
were used to build a model than can predict the sensory panel aroma score for a
fresh-cut cantaloupe product. Quantitative measurements (sensory panel score)
are contained in matrix Y, where as Y’ is the predictive values and X is the matrix
built with E-Nose detector measurements. The PLS generates a ‘matrix that
minimizes the distance between Y and Y’ giving Y’=XB. The ‘8’ matrix is used to
predict quantitative information (sensory aroma score) for a fresh-cut cantaloupe
sample. The measurement matrix is multiplied by B to obtain the prediction
(Alpha MOS Fox 3000 Manual).

The primary aim was to ascertain if there was a difference in the olfactory
responses from E-Nose sensors for fresh-cut cantaloupe samples obtained from
six different vendors and effects of experimental treatment on fresh-cut
cantaloupe. Principal component analysis was performed on the olfactory
response of the E-Nose sensors, to understand its capability to differentiate
between FCC samples, based on a degree of discrimination. A set of twelve

sensor responses was generated for each sample. PCA reduces the factor of

103

variability between various sensor response by a linear combination of the
responses. The location of a FCC sample in two dimensional PCA plot gives an
idea of their similarity or dissimilarity in between FCC samples.

4.2.4.1 E-Nose Analysis of Commercial Fresh-cut Cantaloupe

Three replicates of fresh-cut cantaloupe procured from six different
commercial vendors were analyzed using E-Nose according to run conditions as
shown in Table 5. E-Nose was found to be efficient in discriminating the
commercial FCC samples (Figure 70). A high discrimination percentage (94%) in
PCA profiles as seen Figure 70 indicates that E-Nose was successful in
distinguishing between the constituent volatile components present in various
FCC samples. This could be as result of different ripeness levels, harvest
maturity or post cutting treatments while preparing the fresh-cut cantaloupe (Oms
and Fortuny, 2007,Luna-Guzman and Barett, 2000, Lamikanra et al., 2003,
Bealieu, 2004)

The olfactory data generated by the E-Nose for all the commercial FCC
samples were correlated with the sensory panel results using partial least
squares (PLS) linear regression model. The correlation between the expected
(consumer panel response) and predicted values (E-Nose sensor response) are
illustrated in Figure 71, for the commercial FCC sample olfactory response. It is
evident from Figure 71 that a good correlation percentage (88%) exists between
the predicted and expected values. If an unknown commercialFCC sample was
analyzed using E-Nose, a predicted value can be obtained for it based on its

sensor response. Based on the linear regression correlation model, an expected

104

sensory score for aroma can be estimated, which could be a reliable indication of
an actual sensory score based on sensory response. According to the sensory
panel aroma ratings Sysco and Meijer FCC samples had significantly higher
aroma scores from the remaining FCC samples (Figure 16) and consumers liked
the aroma of these samples. If the estimated aroma score for an unknown FCC
sample according to the linear regression model is close to the expected aroma
scores of Sysco and Meijer FCC samples, then it can be expected with a certain
level of confidence that a consumer panel will like the aroma of this unknown
FCC samples during a real time sensory evaluation. However, the robustness of
such a linear regression model is dependent on the number of different FCC
samples utilized in its development.
4.2.4.2 E-Nose Analysis of Fresh-cut Cantaloupe Subjected to Random
Vibration Spectrum

Fresh-cut cantaloupe (FCC) was prepared to a size of 2.50m and
packaged in ‘Container 3’. These containers were subjected to transport
vibration for 60 minutes (simulating a distance of 500 miles) and 120 minutes
(simulating a distance of 1000 miles). Three replicates of each treatment were
analyzed using E-Nose according to run conditions as shown in Table 5 at Day 1,
4, 7 and 10. Principal component analysis of olfactory responses of the E-Nose
sensors was found to have a high discrimination index between the FCC control
and FCC samples subjected to 500 mile and 1000 mile transport vibration at
days 1, 4, 7 and 10 (Figures 72-75). The discrimination index steadily increased

from a 76% at day 1 to 96% by day 10. This suggested that aromatic volatiles

105

released by FCC samples subjected to transport vibration were significantly more
than non-vibrated control samples. From previous studies it was indicated that
aroma volatile content changes due to harvest maturity, cutting and cultivars
(Beaulieu et al., 2004 and Lamikanra et al., 2003). In this study, whole fruits used
were from the same cultivar and similar harvest maturity. Therefore it can be
asserted with some certainty that the change in aroma content in package
headspace was primarily due to vibration damage.

Similarly, PCA was performed on olfactory responses of E-Nose sensors,
to compare the similarity or dissimilarity between FCC samples affected by days
stored and subjected to transport vibration (Figures 76-77). There is a high
percentage of discrimination (93%) between olfactory responses obtained for
FCC samples subjected to a random vibration for 500 mile transport vibration at
day 1, 4, 7 and 10 (Figure 76). A similar, high discrimination index (93%) was
found for FCC samples subjected to a random vibration for 1000 mile trip (Figure
77). This suggests that the content of aroma volatiles in the package headspace
changes as an effect of days stored. Aroma volatiles content can change due
change in package atmosphere as shown in a study by Lavilla et al (1999), ,
where a relationship of volatile production was associated to sensory quality
affected by different control atmosphere treatments. Similarly, a study performed
Beaulieu and Grimm where it was that the sensory aroma score was significantly

different between days stored under due to changing package atmosphere

conditions. However, in this study the 02 and 002 concentration in ‘Container 8’

as seen in Tables 8-11 did not surpass the threshold limit for oxygen (>3%) and

106

carbon dioxide (>10%) (Beaudry, 2000 and Qi et al., 1999). Therefore it can be
suggested that vibration damage was effective in contributing towards aroma
volatile change in package headspace. A principal component analysis of the
olfactory responses for both FCC samples subjected to random vibration for 500
and 1000 mile trip showed that there is high percentage discrimination (93%)
between all sample treatments as an effect of day stored and duration of random
vibration (Figure 78). This shows that vibration damage during transportation can
affect volatile aroma content in package headspace during storage.

The olfactory data generated by the E-Nose for FCC samples subjected to
random vibration for a 500 and 1000 mile trip were correlated with the sensory
panel results using partial least squares (PLS) linear regression model. The
correlation between the expected (trained panel response) and predicted values
(E-Nose sensor response) are illustrated in Figures 79-80. It is evident from
Figures 79-80 that a good correlation percentage (96%) exists between the
predicted and expected values for both FCC samples subjected to random
vibration for 500 and 1000 mile trip. If an unknown FCC sample which is to be
delivered to a location as far as 1000 mile, the olfactory response can be
generated using E-Nose sensors. The olfactory response can be analyzed to
provide a predicted value based on the linear regression correlation model and
an expected sensory score for aroma can be estimated. This can provide
essential information about the quality of fresh-cut cantaloupe packaged in a
container to be delivered to a distant location prior to shipping a pallet load of

fresh-cut fruits.

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5. CONCLUSIONS

The results of this study have the following conclusions:

1.

The change in the sensory properties of the cantaloupe can be attributed
to the vibration movement observed by the fresh-cut fruit, package design
and storage time.

Fruit dice size and shape will affect fruit quality. In general, a greater loss
in the texture quality is likely of smaller sizes due to more total contact
surface area.

Trained panelists and analytical measurements can both provide useful
information, though the two may not be correlatable

Vibration test times (60 minutes and 120 minutes) representing shipping
distances of 500 miles and 1000 miles caused high level release of aroma
compounds in fresh cut fruit, that increased with storage time.
Transportation in general has an effect on the quality of fresh-cut

cantaloupe

. Due to physical contact among moving fruit pieces (In-shipment) change

in product quality can be affected. The size and shape of the container
can impact fruit movement. The best sensory fruit quality was maintained
in ‘Container 8’.

The methodology developed to assess the quality of fresh-cut cantaloupe
affected by fruit dice size, container design and transportation remains to

be verified for fruits with lower water content than melons. The findings of

119

this study may not necessarily be the same for fresh-cut fruits with lower
percentage of water content, which is a potential area of research in the
future. Similarly, a different fruit dice size may have a better result in a
different container shape these remain to be verified following the

methodology developed to assess quality of fresh-cut fruits in this

research study.

120

APPENDICES

121

APPENDIX A

Fi- ure 81 . Corru-ated box containin- nine ‘Contianer A’ o-ackaes

 

122

APPENDIX B

Fi-ure 82. Corru-ated box containino nine ‘Container 8’ coackaes

4y" '-
. :~.-'¢-.‘.‘-.3"i.-'.‘

’4 ,‘

 

123

APPENDIX C

Fi-ure 83. Corru-ated box containino nine “Container C’ packa-es

 

124

APPENDIX D

Figure 84. Container A Random Vibration Test Set up in Accordance to ASTM
4728

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125

APPENDIX E

Figure 85. Container B Random Vibration Test Set up in Accordance to ASTM
4728

 

 

126

APPENDIX F

Figure 86. Container C Random Vibration Test Set up in Accordance to ASTM

 

 

127

APPENDIX G

Table 13.Trained panel response (n=6) on effect of container design and
transport vibration on sensory characteristics of fresh-cut cantaloupe packaged in
container A, B and C, stored in 4°C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Vibration . .
Attributes Test Time 03:326. Days
minSL
Aroma 1 4 7 10
Container A 60 2.5 90011.5 97511.7 10.5011.3 95011.65
Container B 60 2.5 85011.8 97511.7 92511.3 10.751125
Container C 60 ‘ 2.5 9.2511 .75 102511.25 10.75115 11.0011 .75
Control - 2.5 55011.5 6.501125 70011.5 80011.5
Sweetness 1 4 7 10
Container A 60 2.5 7.1011 .25 7.2511 .75 85011.5 9.7511 .25
Container B 60 2.5 8.0011 .25 8.7511 .75 9.50115 10.50115
Container C 60 2.5 80011.75 85011.5 9.7511. 10.0011.
Control -- 2.5 65011.5 68010.5 72511.25 80011.75
Color 1 4 7 10
Container A 60 2.5 6.2511 .25 7.50115 7.251125 77511.5
Container 8 60 2.5 6.501125 67511.5 72511.25 80011.0
Container C 60 2.5 67511.5 70011.0 77511.25 87511.5
Control - 25 55011.5 5.7511 .25 65011.25 77511.5
Firmness 1 4 7 10
Container A 60 2.5 9.00110 87511.5 85011.25 85011.5
Container B 60 2.5 85011.5 80011.0 8.2511 .25 8.00110
Container C 60 2.5 9.00110 77511.25 72511.5 72511.5
Control - 2.5 87511.25 87511.5 85011.75 80011.0
Overall
Quality 1 4 7 10
Container A 60 2.5 7.50115 77511.75 7.75115 80011.25
Container 8 60 2.5 90011.5 9.5011 .25 9.2511 .75 9.001125
Container C 60 2.5 87511.5 80011.25 8.25115 90011.75
Control - 2.5 7.25115 7.001125 7.75115 82511.75
Aroma 1 4 7 10
Container A 60 1.5 85011.25 9.001175 87511.5 10.0011 .25
Container 8 60 1.5 87511.5 97511.5 90011.25 110011.75
Container C 60 1.5 9.501125 10.50115 10.7511 .25 10.50115
Control -- 1.5 6.0011 .25 55011.75 7.50115 85011.25
Sweetness 1 4 7 10
Container A 60 1.5 70011.5 7.501125 80011.0 87511.5
Container 8 60 1.5 75011.75 80011.25 85011.5 9.501125
Container C 60 1.5 75011.5 80011.25 90011.75 95011.25

 

 

 

 

 

 

 

 

128

Table 13 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Control - 1.5 6.0011.75 6.501125 7.50115 7.751125
Color 1 4 7 10
Container A 60 1.5 6.501125 7.00115 7.251125 75011.5
Container B 60 1.5 7.001125 7.251125 8.00115 8.501175
Container C 60 1.5 7.501125 7.251125 7.501075 8.751075

Control - 1.5 6.001125 57511.5 6.751075 7.001025
Firmness 1 4 7 10
Container A 60 1.5 8.00110 70011.0 6.751075 6.501075
Container B 60 1.5 7.251075 7.001125 6.50105 60011.0
Container C 60 1.5 7.00105 65011.5 6.00110 57511.0
Control - 1.5 8.00105 75011.0 65011.0 65011.25
Overall
Quality 1 4 7 10
Container A 60 1.5 7.001075 7.25105 72511.0 7.501125
Container B 60 1.5 8.50115 90011.75 8.75115 85011.5
Container C 60 1.5 8.251025 75011.5 7.75115 85011.25
Control - 1.5 6.7511.75 6.501175 7.251125 7.75115
Aroma 1 4 7 10
Container A 120 2.5 911.25 9.7511. 10.511. 1011.0
Container B 120 2.5 8.5105 97511.25 1111.0 11.2511.
Container C 120 2.5 811.0 8.75105 9.511 .25 10511.0
Control —- 2.5 610.5 6.5110 7.75105 811.25
Sweetness 1 4 7 10
Container A 120 2.5 7.2511. 7.2511. 811. 8.511.
Container B 120 2.5 911. 9.2511. 10.511. 10.7511.
Container C 120 2.5 8.511. 9.2511. 10.511. 11.2511.
Control - 2.5 711. 6.511. 7.2511. 811.
Color 1 4 7 10
Container A 120 2.5 710.5 7.51075 7.2511. 7,510.75
Container B 120 2.5 8.251 910.5 87511.25 9.251075
Container C 120 2.5 810.25 9.251075 910.5 9.251025
Control - 2.5 610.75 6.2511. 710.75 7.25105
Firmness 1 4 . 7 10
Container A 120 2.5 8.001075 8.251025 7.251125 7.751075
Container B 120 2.5 8.251025 80011.5 7.25105 7.501075
Container C 120 2.5 8.00110 7.751075 7.251075 70011.25
Control - 2.5 8.501025 8.251 8.501 8.00105

 

 

 

 

 

 

 

 

 

129

 

Table 13 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Overall
Quality 1 4 7 10
Container A 120 2.5 85011.25 9.2511. 9.2511 .25 90011.0
Container 8 120 2.5 9.001025 100011.25 95011.5 9.501125
Container C 120 2.5 9.501025 95011.5 85011.25 87511.25
Control - 2.5 7.0011. 7.251125 7.0011. 7.751125
Aroma 1 4 7 10
Container A 120 1.5 8.751025 92511.0 90011.0 102511.25
Container B 120 1.5 9.001025 10.001025 9.25110 11.2511 .25
Container C 120 1.5 97511.0 107511.25 11.001025 10.75110
Control - 1.5 62511.25 57511.25 7.75115 8.751025.
Sweetness 1 4 7 10
Container A 120 1.5 7.251025 7.75115 8.25110 9.001025
Container B 120 1.5 77511.25 82511.25 8.75105 9.75105
Container C 120 1.5 7.75110 8.251025. 9.251025 9.75110
Control - 1.5 6.2511. 67511.0 7.75115 80011.25
Color 1 4 7 10
Container A 120 1.5 67511.25 7.251025 75011.0 7.751025
Container B 120 1.5 7.25110 7.50105 82511.25 87511.0
Container C 120 1.5 7.751025 7.501025 7.751075 9.001075
Control - 1.5 62511.25 60011.75 70011.0 7.25115
Firmness 1 4 7 10
Container A 120 1.5 7.75105 6.751025 65011.0 6.25110
Container B 120 1.5 7.001025 67511.0 6.251025 5.751025
Container C 120 1.5 67511.0 6.25105 5.751075 5.501025
Control -- 1.5 77511.25 7.251025 6.251025 6.251025
Overall
Quality 1 4 7 10
Container A 120 1.5 6.501025 67511.0 6.75110 7.00110
Container B 120 1.5 80011.5 8.501025 82511.5 80011.25
Container C 120 1.5 7.75110 70011.25 7.25110 8.001025
Control -- 1.5 6.251025 6.001025 67511.5 7.25115

 

 

 

 

130

 

APPENDIX H

Table 14.Trained panel response (n=6) on effect of fruit dice size and transport
vibration on sensory characteristics of fresh-cut cantaloupe packaged in

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

container A B and C stored in 4°C
Attributes V43: :2); J12? Container Days
Aroma 1 4 7 10
Size-2.5cm 60 A 9.001125 9.7510. 75 1050110 95011.25
Size-1.5cm 60 A 71011.33 7.25110 85011.25 9.75110
Control - A 5.751125 6.001125 72511.0 8.25105
Sweetness 1 4 7 10
Size-2.50m 60 A 7.101085 7.251025 8.501025 9.75105
Size—1.5cm 60 A 7.00105 7.501025 8.001025 8.75105
Control - A 6.25105 6.651136 7.3811 .25 7.8811 .25
Color 1 4 7 10
Size-2.5cm 60 A 6.251025 7.501025 7.251025 7.751025
Size-1.5cm 60 A 6.501025 7.001025 7.251025 7.501025
Control - A 5.7511.25 5.7511.25 6.631035 7.381025
Firmness 1 4 7 10
Size-2.5cm 60 A 9.001025 8.751125 8.5010. 5 8.501125
Size-1.5cm 60 A 8.001025 7.001025 6.751125 6.501075
Control -- A 8.381025 8.131036 7.501025 7.251025
Overall
Quality 1 4 7 1O
Size-2.5cm 60 A 7.501025 7.751025 7.751025 8.00110
Size-1 .5cm 60 A 7.001025 7.251025 7.251025 7.501025
Control -- A 7.001025 6.751125 7.501025 8.001025
Aroma 1 4 7 10
Size-2.5cm 60 B 8.501025 9.751025 9.251025 1075110
Size-1.5cm 60 8 8.751025 9.751025 9.001025 11.00105
Control -- 8 57511.25 60011.25 7.251 82511.25
Sweetness 1 4 7 10
Size-2.50m 60 B 8.00105 8.751 9.501025 105011.25
Size-1.5cm 60 8 7.501025 8.001025 8.501025 9.501075
Control -- 8 6.251125 6.651065 7.381048 7.881123
Color 1 4 7 10
_Size-2.5cm 60 8 6.501025 6.751025 7.25105 8.001025
Size-1.5cm 60 B 7.001075 7.251075 8.001025 8.501075
LkControl - B 5.7511.25 5.7511.25 6.631058 7.381121

 

 

 

 

 

 

 

 

131

 

Table 14 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Firmness 1 4 7 10
Size-2.50m 60 B 8.501025 8.00105 8.25105 8.001025
Size-1.5cm 60 B 7.251025 7.001075 6.501075 6.001075

Control - 3 8.38105 8.131032 75011.25 7.25105
Overall
Quality 1 4 7 10
Size-2.5cm 60 B 9.001025 9.50105 9.251075 9.00105
Size-1.5cm 60 B 8.501 9.00105 8.751025 8.50105
Control - B 7.001 67511.25 7.501 8.001025
Aroma
Size-2.5cm 60 C 9.251025 10.25105 107510.25 11.00105
Size-1.5cm 60 C 9.501025 10.50105 10.75105 10.501025
Control - C 5.7511.25 6.001125 7.251075 8.251075
Sweetness 1 4 7 10
Size-2.5cm 60 C 8.00105 8.50105 9.75105 10.00105
Size-1.5cm 60 C 7. 5010.25 8.001025 9.001025 9.501025
Control - C 6.25105 6.65105 7.381 7.881025
Color 1 4 7 10
Size-2.5cm 60 C 6.751025 7.00105 7.75105 8.75105
Size-1.5cm 60 C 7.50105 7.251025 7.501025 8.751025
Control - C 5.7511.25 5.751125 6.631073 7.381085

Firmness
Size-2.5cm 60 C 9.001025 7.75105 7.25105 7.251075
Size-1.5cm 60 C 7.00105 6.501075 6.001025 5.751125

Control - C 8.381038 8.131068 7.50105 7.251025

Overall

Quality 1 4 7 10
Size-2.5cm 60 C 8.75105 8.00105 8.251075 9.001075
Size-1.5cm 60 C 8.251025 7.501025 7.75105 8.501025

Control - C 7.001075 6.75105 7.501025 8.001025

 

132

 

   

 

APPENDIX I

Table 15. Effect of container design and transport vibration on CIE L*, a* and b*
color values of fresh-cut cantaloupe packaged in container A, B and C, stored in
4°C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Co1ntainer 1:23:36 Dice Size Days
ype (mins) (cm)
L*
1 4 7 10
A 60 2.5 58.78113 57.091 57.90111 57.46111
B 60 2.5 57.97114 55.44115 55.63111 55.02115
C 60 2.5 54.90112 54.2311. 54.10114 54.17112
Control - 2.5 59.80115 58.9611. 58.90112 58.70111
a*
1 4 7 10
A 60 2.5 16.89114 16.10112 15.90111 15.40114
B 60 2.5 16.39116 16.12111 15.80113 15.07112
C 60 2.5 17.80112 17.56113 17.50118 17.40111
Control - 2.5 17.59113 17.50114 17.45114 16.89112
b*
1 4 7 10
A 60 2.5 33.60113 33.40115 33.30111 32.50114
B 60 2.5 32.86115 32.98114 32.54115 30.50114
C 60 2.5 31 .29113 31.20111 31.25118 31.27111
Control - 2.5 34.67113 33.9811. 33.70111 33.60118
L*
1 4 7 10
A 60 1.5 56.78109 55.09105. 55.90111 55.46113
B 60 1.5 56.90114 54.50108 54.10104 54.02118
C 60 1.5 53.20112 52.90111 52.70109 52.17112
Controfl — 1.5 58.80114 57.96111 57.90106 57.70119
a*
1 4 7 10
A 60 1.5 15.39104 14.60107 14.40109. 14.20108
B 60 1.5 15.30114 14.90113 14.40112 13.90116
C 60 1.5 16.10111 15.75101 15.59105 15.20108
Control - 1.5 15.84112 15.75113 15.70114 15.14113
b*
1 4 7 10
A 60 1.5 32.10104 32.40105 31 .80109 32.00109
B 60 1.5 31.46113 30.90107 31.04104 29.50111
C 60 1.5 29.40112 29.19113 28.92112 28.90111

 

 

 

 

 

 

 

133

 

Table 15 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Control - 1.5 32.92105 L32.23107 E195105j 31.85112
1*
1 4 7 10
A 120 2.5 57.75105 55.94111 55.25114 55.72108
B 120 2.5 52.49111 53.13108 52.54107 51.05115
C 120 2.5 53.40112 53.20112 52.00109 51.00109
Control - 2.5 59.50109 58.95111 55.90112 55.70108
3.
1 4 7 10
A 120 2.5 17.50107 15.70115 15.50118 15.30107
B 120 2.5 17.45115 16.78116 15.95111 15.71118
C 120 2.5 15.43113 15.29103 18.27104 15.12111
Control — 2.5 17.59102 17.50111 17.45111 15.59104
5*
1 4 7 10
A 120 2.5 33.40108 32.93109 32.10113 3198112
B 120 2.5 33.34113 3115113 31.73112 31.21111
0 120 2.5 31.38113 30.95105 30.89109 30.80107
Control .- 25 34.57105 33.98112 33.70105 33.50111
L.
1 4 7 10
A 120 1.5 55.75107 54.94104 54.25112 53.72109
B 120 1.5 51.32112 52.05113 51.54105 50.00115
C 120 1.5 51.40111 51.00102 50.7011. 49.72112
Control -. 1.5 58.80108 57.95115 57.90111 57.70114
3..
1 4 7 10
A 120 1.5 15.70105 14.90114 14.70105 14.50107
B 120 1.5 15.43113 15.30111 15.20114 14.91113
C 120 1.5 15.40105 15.14114 15.90103 15.02115
Control .- 15 15.54101 15.75111 15.70115 15.14111
5*
1 4 7 10
A 120 1.5 31.50105 32.10107 31.40104 30.89111
B 120 1.5 31.20114 30.50113 31.08112 29.20113
C 120 1.5 29.25109 28.50112 28.20104 27.95113
Control .. 1.5 32.92112 32.23107 31.95108 31.85109

 

 

 

 

 

 

 

134

 

Table 16. Effect of fruit dice size and transport vibration on CIE L*,a* and b* color

APPENDIX J

values of fresh-cut cantaloupe packa ed in container A, B and C, stored in 4°C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Dice Size Vibration Test Container Days
(cm) Tlme (mlns) Type
L"
1 4 7 10
Size-2.5 60 A 58.78116 57.09114 57.90112 57.46118
Size-1.5 60 A 56.78115 55.09113 55.90113 55.46111
Control - A 59.80119 58.96114 58.90115 58.70113
8*
1 4 7 10
Size-2.5 60 A 16.89112 16.10111 15.90119 15.40112
Size-1.5 60 A 15.39117 14.60114 14.40112 14.20114
Control - A 17.59118 17.50117 17.45117 16.89111
b"
1 4 7 10
Size-2. 5 60 A 33.60112 33.40119 33.301 32.50111
Size-1.5 60 A 32.10113 32.40121 31.80118 32.00121
Control - A 34.67114 33.98111 33.70112 33.60113
L*
1 4 7 10
Size-2. 5 60 B 57.97116 55.44113 55.6311 .1 55.02118
Size-1.5 60 8 56.90112 54.50115 54.10117 54.02111
Control - B 56.27115 57.0911. 56.67112 56.88117
at
1 4 7 10
Size-2.5 60 B 16.39115 16.12114 15.80116 15.07117
Size-1.5 60 B 15.30116 14.90117 14.40114 13.90112
Control - B 17.30111 17.10113 17.05113 16.22116
b*
1 4 7 10
Size-2.5 60 B 32.86116 32.98113 32.54118 30.50112
Size-1.5 60 B 31.46114 30.90116 31.04118 29.50112
Control — B 33.61111 33.59116 33.45118 31.64119
Li
1 4 7 10
Size-2.5 60 C 54.90112 54.23115 54.10117 54.17115
Size-1.5 60 C 53.20113 52.90116 52.70119 52.17115
Control - C 55.80113 56.90116 56.34119 56.58116
;

 

135

 

Table 16 continued

 

 

 

 

 

 

 

 

 

 

 

 

 

a*

1 4 7 10
Size-2.5 60 C 31.29116 31.20118 3125112 3127118
Size-1.5 60 C 29.40112 29.19119 28.92115 28.90119
Control — C 32.6712 32.89118 32.75111 32.10114

b*

1 4 7 10
Size-2.5 60 C 31.29119 31.20119 3125114 3127116
Size-1.5 60 C 29.40116 29.19113 28.92114 28.90112
Control - C 32.67114 32.89117 32.75119 32.10117

 

 

 

 

 

 

136

 

APPENDIX K

Table 17. Effect of container design and transport vibration on firmness values of
fresh-cut cantaloupe packaLed in container A, B and C, stored in 4°C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. Vibration Dice
001ntalner Test Time Size Days
ype minutes) (cmL
Force (Newtons)
1 4 7 10
A 60 2.5 68.90114 65.80116 63.10111 63.40112
B 60 2.5 68.70115 62.80123 60.40119 58.90121
C 60 2.5 60.90123 58.40125 54.80129 51.70128
Control -- 2.5 70.50119 68.40121 67.40119 67.10113
A 60 1.5 52.83119 50.80114 45.76121 48.40119
B 60 1.5 50.48123 45.29119 45.40115 43.90113
C 60 1.5 44.93121 35.89125 37.23119 31.30121
Control - 1.5 57.00122 54.00121 52.70119 5019117
A 120 2.5 70.90124 66.50121 64.90124 60.20125
B 120 2.5 70.501175 64.901234 61.401178 56.301278
C 120 2.5 56.801239 48.091183 46.071198 45.081123
Control - 2.5 70.501194 68.40115 67.40111 67.10116
A 120 1.5 49.39121 47.571 48.291213 46.381
B 120 1.5 45.901193 43.40121 39.801296 36.70120
C 120 1.5 31.801292 23.091192 21.07112 20.08116
Control - 1.5 57.001234 54.001234 52.70121 50.191267

 

 

 

 

 

 

 

137

 

 

III-I‘ll"

 

16D...

APPENDIX L

Table 18. Effect of fruit dice size and transport vibration on firmness values of

fresh-cut cantaloupe packaged in container A, B and C, stored in 4°C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Dice Size Vibration Test Container Da s
(cm) Time (mins) Type y
Force (Newtons)
1 4 7 10

Size-2.5 50 A 58.90121 55.80121 53.10120 53.40121
Size-1.5 50 A 52.53115 50.50125 45.75125 48.40111
C°"".§'55128' -- A 70.45112 59.55117 58.98118 55.59122
0°“t’1°'53‘29' -- A 55.89115 55.78119 54.39132 51.89121
Size-2.5 50 B 58.70117 62.80123 50.40129 58.90125
Size-1.5 50 B 50.45131 45.29121 45.40115 43.90121
0°"t'2°'58‘ze‘ —- B 70.10125 59.54125 57.79117 55.32124
C°mr1°'55ize' -- B 55.59123 55.21117 53.57124 52.73115
Size-2.5 50 C 50.90115 58.40117 54.80124 51.70122
Size-1.5 50 C 45.90115 43.40125 39.80121 35.70125
C°”t'2°'53‘ze' .- c 72.34121 59.57117 57.49122 55.79117
0°"t’1°'5$ize‘ -- C 58.57119 57.54118 54.12114 52.57115

 

 

 

 

138

 

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