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PHYSICOCHEMICAL AND SENSORY PROPERTIES OF
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presented by

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PHYSICOCHEMICAL AND SENSORY PROPERTIES OF AUTUMNBERRY
AND APPLICATION IN BREAD

By

Aileen Diana Tanojo

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

MASTER OF SCIENCE
Food Science

2009

ABSTRACT

PHYSICOCHEMICAL AND SENSORY PROPERTIES OF AUTUMNBERRY
AND APPLICATION IN BREAD

By

Aileen Diana Tanojo

Autumnberry is a low-cost underutilized fruit with potential for highly valuable
antioxidant/nutraceutical applications. Spectrophotometric and Oxygen Radical
Absorbance capacity (ORACFL) methods were used to test the lycopene content
and ORAC-value of both pureed and freeze-dried autumnberry (in dried weight):
29010.04 mg/g lycopene and 144.1414.86 umol TE/g ORAC-value, and after
freeze-drying, 0.701000 mg/g lycopene and 102261327 pmol TE/g ORAC-
value. A sensory trained panel (n=12) participated in descriptive analysis to
evaluate freeze—dried autumnberry fortified bread at 0%, 3%, 6%, and 9% levels.
A general linear mixed model was fitted using the mixed procedure of SAS. At
p<0.05, the significant differences were detected among all breads in term of
crumb color and autumnberry flavor, but not in yeasty flavor. The significant
differences in crust color and firmness were detected at 6% and 9% level; and at
0% and 9% for crumb cell uniformity. The 3%-fortified bread was preferred and
the closest to the control in terms of flavor and physical characteristics. The
Principal Component Analysis (PCA) demonstrated that the bread control had
distinct yeasty flavor, while the 9% fortified bread was strongly related to crumb
cell uniformity. Lycopene in autumnberry appeared to be easily degraded by

freeze-drying (75.86%) and only small quantity retained after baking (“l-9%).

DEDICATION

To my beloved Mom and Dad, Oei Giok Koen and Kardi Tanojo, for your
unconditional love, endless prayers, and supports. You have taught me to be
resilient, independent, and optimistic. Thank you for believing in me and always
being there for me every step of the way. To my older brother, Mustika Utomo
Tanojo a.k.a. Koko Bear, you are the best brother anyone could ask for, a best
friend, a role model. Thank you for your patience, emotional and financial
support, and your brotherly advices. Especially to my dearest Dad, you have
given us strength, inspiration, and the sweetest of memories that we will carry for
the rest of our lives. We miss you very much and will always keep you in our
thoughts and in our heart every single day. I cannot thank you enough for your
selfless sacrifice and tremendous dedication to your family that have shaped me
into the person that I am today. My wish is that one day, we will happily be

together again.

ACKNOWLEDGEMENTS

I would like to express my utmost appreciation to Dr. Kirk Dolan for being
a great advisor. I would like to thank him for all of his guidance, support,
academic and personal advices throughout my Master’s study here at State. He
made this whole experience such a pleasant one. He also made me want to be
able to speak Chinese Mandarin like he does!

I would also like to thank my committee member, Dr. Janice Harte, for
being such a wonderful mentor especially for her valuable inputs in the sensory
part of my thesis work. I enjoyed the time spent in product development teams
for various competitions under her guidance, as well as working as her TA for
two semesters. Some of the best learning experiences I had were with her as
well. Thank you, Dr. Maurice Bennink, for his valuable inputs on lycopene
analysis and for his patience and willingness to assist me any time of the day. I
would also like to thank my other committee member, Dr. Perry Ng, for his
tremendous guidance and advice in bread-making. Thank you for patiently
teaching me to be a well-rounded scientist. I am so grateful that I have a solid
and;supportive committee for my Masters Degree completion.

Thank you Dr. Ravi and Dr. Siddiq for your academic and career advises
throughout my study here. Bunch of thanks to my b.‘f.fs. Cynthia and Christanty
for their prayers and emotional supports for the past ten years, eventhough you
are thousand miles away, I feel that you are always here with me. Thank you

Mitzi Ma for visiting EL whenever you got a chance and hung out with me. I

enjoyed the good times we shared together and look forward for more to come.
Thank you for your friendship and for listening to my problems: you are like my
elder sister and have given me sisterly advices!

Thank you Shantanu, formerly known as “gundu,” for always be there for
me whenever I needed help. Although we fought and argued over things all the
time, you are still one of my best buddies! Thank you Rabiha a.k.a “binti” for your
academic and personal advices, for letting me stayed in your place whenever I
needed place to crash, and also for cooking delicious Malay/lndo foods for me.
You two are always being there for me during my ups and downs.

Thank you Nora Bello for your superb advice in statistical analyses, you
are a very passionate statistics mentor. Thank you Kevser for always willing to
share your thoughts and inputs into my research. Thank you George for the
serenade that I will always remember! Thank you Ibrahim and his family for
visiting me in BC. Thank you Megan for being a cool lab mate who cheered me
up during my thesis writing. Thank you Claudia for being so jolly all the time, you
brightened my days in the lab. Thank you Christa for your supports on my
graduation day! Thank you Hayati for encouraging me during my defense
preparation. Thank you Kathy Lai for your emotional supports and prayers, and
for hanging out with me in BC.

Thank you Uju for your counseling. Thanks Mishraji for singing in the lab!
Thank you to Danielle, Harlem, Patrick, Rico, all my panelists and others that I
may have failed to mention for being such a wonderful friends and for all the

good times we shared together. GO GREEN, GO WHITE, GO SPARTANS!!!

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. ix

LIST OF FIGURES ................................................................................. x
CHAPTER 1

INTRODUCTION .......................................................................... 1
CHAPTER 2

LITERATURE REVIEW .................................................................. 6

2.1. Nutraceuticals and Functional Foods as Food Ingredients ............ 6

2.2. Lycopene and Human Health ................................................. 7

2.3. Background of Autumnberry ................................................ 11

2.4. Common Drying Methods of Fruits and Vegetables ................... 13

2.5. Lycopene ......................................................................... 15

2.5.1 Structure of Lycopene ................................................ 15

2.5.2 Lycopene Degradation during Processing ...................... 16

2.5.2.1 Impact of Temperature on Lycopene Stability ........ 17

2.5.2.2 Impact of Light on Lycopene Stability .................. 18

2.5.2.3 Impact of Oxygen on Lycopene Stability ............... 19

2.6. Freeze-drying .................................................................... 19

2.6.1 Principles of Freeze-drying .......................................... 20

2.6.2 Issues and Concerns during Vacuum Evaporation ............ 23

2.7. Analyses of Antioxidant Capacity ........................................... 24

2.7.1. Total Phenolics Assay ................................................ 24

2.7.2 Oxygen Radical Absorbance Capacity (ORACFL) .............. 26

2.7.3 Spectrophotometry for Lycopene Determination ............... 28
CHAPTER 3

MATERIALS AND METHODS ....................................................... 31

3.1. Plant Material .................................................................... 31

3.1.1. Autumnberry Collection .............................................. 31

3.1.2. Autumnberry Puree ................................................... 31

3.2. Freeze-drying .................................................................... 32

3.2.1. Sample Preparation and Freezing ................................. 32

3.2.2. Operation ................................................................ 32

3.3. Moisture Content ............................................................... 34

3.4. Water Activity (A,,) .............................................................. 34

3.5. Sugar Content Analysis ....................................................... 34

3.5.1. Standard Solution Preparation ..................................... 35

3.5.2. Determining the Titration Factor ................................... 35

3.5.3. Sample Preparation for Invert Sugar Determination .......... 36

3.5.4. Sample Preparation for Total Sugar Determination ........... 36

vi

3.5.5. Determining the Invert Sugar and Total Sugar Content......36

3.6. Oxygen Radical Absorbance Capacity (ORACFL) ....................... 37

3.6.1. Sample Preparation ................................................... 37

3.6.2. Reagent and Standard Preparation ............................... 37

3.6.3. Experimental Setup for ORACFL .................................... 38

3.6.4. Data Analysis of ORACFL ............................................. 39

3.7. Total Phenolics by Folin-Ciocalteau Colorimetry ....................... 39

3.7.1. Preparation of Saturated Sodium Carbonate Solution ....... 39

3.7.2. Preparation of Gallic Acid Standard Solution ................... 40

3.7.3. Sample Preparation and Analysis of Total Phenolics.........40

3.8. Color Analysis ................................................................... 41

3.9. Titratable Acidity ................................................................ 41

3.10. Spectrophotometric Method for Lycopene Measurement ............ 42

3.11. Bread-making .................................................................... 43

3.11.1. Bread Texture ................................................. 44

3.11.2. Bread Volume and Density ................................. 45

3.12. Statistical Analysis for the Physicochemical Properties ............... 45

3.13. Sensory Tests and Evaluations ............................................. 46

3.14. Statistical Analysis for Sensory ............................................. 48

CHAPTER 4

RESULTS AND DISCUSSION ....................................................... 50
4.1. Physicochemical and Statistical Analyses of Autumnberry Puree

and Freeze-dried ............................................................... 50

4.1.1 CIE L*a*b* Color ....................................................... 50

4.1.2 Acid Content ............................................................ 52

4.1.3 Sugar Content .......................................................... 54

4.1.4 Moisture Content ...................................................... 56

4.1.5 Water Activity (Aw) .................................................... 57

4.1.6 Total Phenolics ......................................................... 58

4.1.7 ORAC-value (Total Antioxidant Capacity) ....................... 58

4.1.8 Lycopene Content ..................................................... 59

4.1.9 Conclusions ............................................................. 63

4.2. Bread Analyses .................................................................. 64

4.2.1. Bread Physical Analysis ............................................. 64

4.2.2. Bread Physicochemical Analysis .................................. 69

4.2.2.1. Bread Moisture Content and Water Activity ....... 69

4.2.2.2. Bread ORAC-value (Total Antioxidant Capacity)70

4.2.2.3. Bread Lycopene Content ............................... 71

4.2.3. Conclusions ............................................................. 72

4.3. Sensory Results and Analyses .............................................. 72

4.3.1. Consumer Acceptability Panel ..................................... 72

4.3.2. Trained Panel/Descriptive Analysis ............................... 75

4.3.2.1. Crust Color ................................................. 75

4.3.2.2. Crumb Color ............................................... 76

4.3.2.3. Crumb Cell Uniformity ................................... 77

vii

4.3.2.4. Firmness ................................................... 78

4.3.2.5. Yeasty Flavor ............................................. 79

4.3.2.6. Autumnberry Flavor ...................................... 80

4.3.3. Principal Component Analysis ...................................... 81

4.3.4. Conclusions ............................................................. 82

FUTURE RECOMMENDATIONS ............................................................ 83
APPENDICES ..................................................................................... 84
REFERENCES .................................................................................. 131

viii

Table 4.1.5.1.

Table 4.1.8.1.

Table 4.2.1.1.
Table 4.2.2.1 .1.

Table 4.2.2.2.1.
Table 4.2.2.3.1.

Table 4.3.1.1.
Table A.1.'
Table A.1.1.
Table A.1.2.
Table A.1.3.
Table A.1.4.
Table A.1.5.
Table A.1.6.

Table A2.

Table A].

Table A8.

LIST OF TABLES

Water activity of autumnberry puree and freeze-dried ....... 57

Summary of lycopene content of puree and freeze-dried
autumnberry ............................................................. 61

Summary table of the physical analysis of bread samples..67
Summary of bread moisture content and water activity......69

Summary of ORAC-values of the experimental bread
samples ................................................................... 70

Summary of lycopene content of the experimental bread
samples ................................................................... 71

Tukey’s test summary for consumer acceptability test ....... 73

Raw data of consumer acceptability panel ...................... 84
Summary and ANOVA table for aroma ........................... 88
Summary and ANOVA table for color ............................ 88
Summary and ANOVA table for appearance .................... 88
Summary and ANOVA table for body/texture .................. 89
Summary and ANOVA table for flavor ........................... 89

Summary and ANOVA table for overall acceptability. ........89

Raw data of physicochemical properties of autumnberry
puree and freeze-dried ............................................... 90

Bread attributes references for sensory trained panel......113

Raw data for descriptive analysis testing ...................... 115

Figure 2.5.1.1.
Figure 2.6.1.1.
Figure 2.6.1.2.

Figure 2.7.2.1.
Figure 2.7.2.2.

Figure 2.7.3.1.
Figure 4.1.1.1.

Figure 4.1.2.1.
Figure 4.1.3.1.

Figure 4.1.8.1.

Figure 4.1.8.2.

Figure 4.2.1.1.
Figure 4.2.1.2.
Figure 4.2.1.3.
Figure 4.2.1.4.

Figure 4.3.1.1.

Figure 4.3.2.1.

Figure 4.3.2.2.

LIST OF FIGURES

Trans-lycopene chemical structure ................................ 16
Freeze-dryer simplified schematic ................................. 21
Phase diagram of water .............................................. 22

Proposed fluorescinm) oxidation pathway in the presence

of AAPH .................................................................. 27
Total antioxidant properties calculations using ORAC

method.. .............................................................................. 28
UV-vis absorption spectrum of selected carotenoids ......... 30

Color CIE L*a*b* of pureed vs. freeze-dried autumnberry...52

Acid contents of pureed vs. freeze—dried autumnberry in

dried weight basis ...................................................... 54
Sugar contents of pureed vs. freeze-dried autumnberry

dried weight basis ...................................................... 56
Lyc0pene standard curve ............................................ 60

Average lycopene content of selected fruits and tomato
products .................................................................. 63

Typical results of the control bread texture analysis .......... 65
Typical results of the 3%-fortified bread texture analysis....65
Typical results of the 6%-fortified bread texture analysis. . ..66
Typical results of the 9%-fortified bread texture analysis....66

Summary of consumer acceptability test results for control,
10%, and 20% fortification (flour basis) using autumnberry

puree ...................................................................... 74
Trained panel crust color perception scores .................... 76
Trained panel crumb color perception ............................ 77

Figure 4.3.2.3.
Figure 4.3.2.4.
Figure 4.3.2.5.
Figure 4.3.2.6.

Figure 4.3.3.1.

Figure A.7.1.

Trained panel crumb cell uniformity perception ................ 78
Trained panel firmness perception ................................ 79
Trained panel yeasty flavor perception ........................... 80
Trained panel autumnberry flavor perception .................. 81

Principal Component Analysis (PCA) graph for the
sensory trained panel ................................................ 82

References for bread crumb cell uniformity ................... 114

xi

CHAPTER 1: INTRODUCTION

Carotenoids are naturally available in fruits and vegetables. Lycopene,
one of many other classifications in carotenoid family, is a natural orange-to-red
pigment mostly present in tomato, guava, rosehip, watermelon, and pink
grapefruit (Holden 1999). Lycopene has high levels of antioxidant properties that
have a high rate of quenching reactive singlet oxygen (‘02), which causes cell
damage leading to cell death, deoxyribonucleic acid (DNA) damage or mutation,
and protein damage and/or its functional alteration. It is also known as the most
efficient antioxidant among other carotenoids (Di Mascio and others 1989;
Chadwick and others 2003).

Various positive effects of lycopene on human health have been reported
in the literature. Due to its antioxidant characteristics, lycopene may also protect
against chronic degenerative diseases such as inflammation in arthritis and
atherosclerosis (Schmidl and Labuza 2000). The normal human body has
defenses against the free radicals. However, people under stress, high exposure
to cigarette smoke, pollution, illness and dietary deficiencies, are more prone to
having impaired antioxidants function. Epidemiological studies suggest that
people who consume diets rich in tomato and tomato products have a lower risk
of certain types of cancer, especially prostate, lung, and stomach cancers
(Weisburger 1998). Additionally, lycopene is also known to have a preventative

function towards cardiovascular diseases (Rao and Rao 2007).

As more scientific data on lycopene’s beneficial health effects become
available, food manufacturers seek more avenues to incorporate a natural source
of lycopene into daily food products to produce value-added or functional food
products. In addition, consumers have a growing interest in the use of “natural”
ingredients in food products, such as lycopene, that are perceived as safer and
healthier than the synthetic counterpart. The utilization of natural ingredients has
also‘ attracted many food manufacturers striving for a “clean label” (Fletcher
2006). Some popular applications of lycopene mostly extracted from tomato
include natural food colorant in juice and nutritional beverages, smoothies and
yogurt, and snack foods; as well as in the form of dietary supplements (Danzig
and Hartal 2001; USDA Food and Nutrition lnforrnation Center 2007).

Tomato (Lycopersicon esculentum) is the best known source of lycopene.
Fresh tomatoes and processed tomato products such as tomato sauces, pastes,
canned tomatoes, ketchup, and juice are the primary sources of daily lycopene
intake (0.5-5mg/day) (Chug-Ahuja 1993). In the United States, the tomato
source of lycopene account for 81% of all lycopene intake (Plummer 1999;
Fordham and others 2001; Grolier and others 2007). In recent years, the
average rate of increase in quantity in tomato and tomato products consumption
is 3% yearly. Together with this constant growth consumption, consumers not
only demand higher quality of tomato products but also edible convenience as an
important factor in fulfilling their daily lycopene intake (Business Wire 2007).

The United States Department of Agriculture (USDA) Fruit Laboratory has

recently discovered autumn olive berry, or better know as autumnberry, that once

was identified as a fruit of an invasive plant, autumn olive plant (Elaeagnus
umbellata), which was believed to possess high lycopene content. The USDA
researchers found that typical autumnberry has up to 17 times the lycopene
content (30-70 mg/100 g wet weight) as compared to fresh tomato (0.88 - 4.20
mg/100 g wet weight) (Bramley 2000; Boileau, 2002; Strax, 2006). This
discovery has shed a possibility of autumnberry being a better source of
lycopene than tomato. The pronounced tartness and slightly sweetness of the
berry makes it suitable to be processed into jams and jellies (Fordham and
others 2001). However, other edible convenience food applications have not
been explored.

“Let food be thy medicine and medicine be thy food,” a theory promoted
nearly 2,500 years ago by Hippocrates, father of modern medicine, has recently
regained more and more interest. According to the “2008 Food & Health Survey:
Consumer Attitudes toward Food Nutrition & Health” conducted by the
lntemational Food lnforrnation Council (IFIC), 67% of Americans are now
switching their diet toward healthfulness and wholesomeness (lntemational Food
lnforrnation Council Foundation 2008). Additionally, 80% of US population are
currently consuming, or would be interested in consuming, specific healthful
foods or beverages (Hasler 1998; Tan 2002; Foster 2008; lntemational Food
Information Council Foundation 2008). Practical applications of functional
ingredients, in this case, lycopene, into staple food products such as fortification
of bread are an excellent approach to accommodate recent consumer demand.

Examples of some existing fortified or enriched breads are whole grain breads,

breads with increased fiber, protein, vitamins, and minerals, but there are no
breads enriched with lycopene. The bread fortification trend is not only gaining
popularity in the US, but also worldwide, especially in Japan and France. The
global increase in health consciousness and the awareness of health benefits of
functional ingredients are resulting in a large array of nonstandard functional
bread products (Kubomura 2007; Foster 2008). This increased interest has
become the inspiration to create autumnberry-fortified bread, or so called
lycopene bread.

Lycopene in general is an open-chained carotenoid that is also highly
unsaturated due to its eleven conjugated double bond. This characteristic of
lycopene makes it considerably reactive with light, heat, oxygen and acid, which
can cause problems during food processing (Nguyen and Schwartz 1998).
Typically, the production of a fruit powder involves heat that evaporates the water
from the fruit juice or puree, and a grinding mechanism that converts the dried
product to smaller particles form. These processing steps easily diminish the
lycopene content in autumberry pureed; therefore, the freeze—drying technique is
proposed. Freeze-drying is the superior drying method, widely used in food
manufacturing, for producing fruit product from its liquid state to dehydrated fruit
powders. The freeze-dried fruit powders have the highest quality in term of
nutrient retention, chemical stability, and convenience, as compared to other
drying methods (Barbosa-Casanovas and others 2005). In addition, the
autumnberry powder, rather than the pureed form, is a better form to use for an

optimum bread fortification since its powder form is the same as the flour.

Although many studies have emphasized the physical and chemical
properties of other lycopene-rich foods, little is known about this recently known
fruit: autumnberry, and its physicochemical and sensory characteristics.
Because of the promising properties of autumnberries as a good source of
lycopene, they are attractive for the applications in foodstuffs, for example in

fortified bread.

The objectives of this research were:
1) To analyze the physicochemical properties of autumnberry pureed as
well as the freeze-dried autumnberry powder.
2) To determine the fate of nutraceuticalsof autumnberry (lycopene and
antioxidant capacity) after freeze-drying and baking process.
3) To fortify yeast-raised bread with freeze-dried autumnberry powder
and to analyze some of its physical, physicochemical, and sensory

attributes.

CHAPTER 2: LITERATURE REVIEW

2.1. Nutraceuticals and Functional Foods as Food Ingredients

Nutraceuticals and functional foods are closely related to one another and
the terms have been used interchangeably, however, they have not been clearly
defined. Nutraceuticals are often defined as products manufactured as dietary
supplements, such as those in pill or powder form, whose ingredients offer
medical or health benefits for disease prevention and/or treatment. Functional
foods, on the other hand, are food products in form of conventional foods
consumed in regular daily meal, for example fortified energy drinks or nutritionally
enhanced snack bars (lisakka 2003).

The extensive growth of nutraceuticals and functional foods has appeared
to be the channel towards consumers” healthier lifestyle worldwide. Globally, this
industry corresponds to approximately $75.5 billion in 2007 with growth
projections to $167 billion by 2010 (Basu and others 2007). In the United States
alone, the worth of this industry was $21.3 billion in 2006 and continues to grow,
thus placing the United States as the largest and fastest expanding nutraceutical
and‘functional food market in the world. In addition, 50% of the United States
multi-million dollar food market is related to the application of nutraceuticals and
functional food products (Datamonitor 2007).

Fortification has become more and more popular approach to incorporate
nutraceuticals and functional ingredients into food products. Initially, fortification

is the simplest and oldest method used to replenish nutrient lost in particular

staple foods after processing, mainly vitamins and mineral fortifications.
Recently, fortification has become more robust involving wide range of food stuffs,
various nutrients, and phytochemicals which now become more well accepted for
their proven positive health benefits and disease-preventing properties in many
clinical studies and researches (Myers 2005).

The projected sale of fortified food products in 2004 was $23.4 billion,
which is 3% increase over 2003 sales. Despite the broad availability of fortified
food products in the market, 27% of the consumers feel that they are deficient in
antioxidants (Sloan 2004). Carotenoids, especially lycopene, along with
essential fatty acid, and phytonutrients are among the top in the list of ingredients
for food fortification. Fortifying waters, energy/sport drinks, and hot beverages
with antioxidants such as lycopene is one of the most recent notable trends in
food industry (Myers 2005). Taken together, consumers’ escalating demand and
interest in fortified food product makes the fortification of staple foods, such as

bread, with lycopene-rich autumnberry a viable yet more accessible channel to

fulfill their needs.

2.2. Lycopene and Human Health

Lycopene is by far considered as the most effective antioxidant among all
other dietary carotenoids. Unlike B-carotene, lycopene does not get converted
into vitamin A after it is digested and metabolized. The conversion of carotenoids
into‘vitamin A actually weakens the antioxidant capacities. The acyclic structure,

the numerous conjugated double bonds, and high hydrophobicity of lycopene are

the characteristics of lycopene that contribute greatly to its antioxidant benefits
(Clinton 1998). Therefore, lycopene is believed to be a more powerful
antioxidant, thus, the most efficient quencher of singlet oxygen in biological
systems that has protective effects against certain tumors and cancers such as
prostate, lung, and stomach cancers (American Cancer Society 2007).

The proposed mechanisms by which lycopene could decrease certain
cancer risks are directly related to its antioxidant activities. Oxygen is very
crucial to sustain life; however, it can also be toxic due to its potential to unleash
free‘ radicals. The unstable and highly reactive free radicals have unpaired
electrons around them. These free radicals always try to capture electrons from
nearby stable molecules in order to gain stability. However, the molecule whose
electron was taken becomes a free radical and further starts a chain reaction, a
process that finally ends in undesirable disruption or damage of the cells.
Research studies have proven that oxidation through the free radicals processes
is associated with reduced body capabilities to fight serious illnesses such as
cancer and atherosclerosis (Chadwick 2003). Natural antioxidants lycopene
have the ability to neutralize these free radicals in our body by donating an
eIeCtron without loosing their own stability. Singlet-oxygen quenching reactions

6

of lycopene are summarized as follows:

102 + Lycopene -) 302 + 3Lycopene

3Lycopene 9 Lycopene + Heat

The sunlight and other chemical actions can convert ground-state oxygen (302)

to extremely reactive singlet oxygen (102). These reactive singlet oxygen

molecules can be quenched by the reaction with the lycopene to produce triplet-
excited lycopene, which would decays exotherrnically (Wildman 2001).

The ingested dietary lycopene possess ability to increase the lycopene
level in certain body tissues, and acts as an antioxidant that may trap the highly
reactive oxygen molecules. Lycopene would also increase the overall anti-
oxidant capacities, which further reduce the oxidative damage to lipid i.e.
lipoprotein, membrane lipids; and also proteins such as significant enzymes, and
DNA or other genetic materials, thereby lowering the oxidative stress. This
reduced oxidative stress may lead to the reduced risk of cancer and
cardiovascular disease. In addition, the increased lycopene levels in our body
may also regulate the gene functions, improve intercell communications,
modulate hormone and immune response, or regulate metabolism (Aganlval
2000). These functional characteristics help lower the risk for chronic disease

In general, the ’active’ form of lycopene is the cis-conformation (15-cis, 13-
cis, 9-cis, and 5-cis lycopene). The term ‘active lycopene’ is equivalent to the
term of ‘bioavailability’, which is the measure of the uptake of an ingested
substance by the body as assessed by its concentration in the blood or the
quantifiable biologic or functional effects of that nutrient (Stahl and Sies 1996).
Although about 90% of the dietary lycopene is found in stable linear all-trans

conformations, after food processing and cooking, these trans-isomers are

transformed to cis-isomers to some extent, <10% (isomerization). Human

tissues, however, contain only cis-confonnations. The cis-isomers of lycopene
are better absorbed than the all-trans form due to their non-linear (bend
configuration), greater solubility in human micelles, as well as their lower
tendency to aggregate; thus, they are more bioavailable to further function as
antioxidants (Boileau 2002).

Due to chopene’s health benefits and attractive color, lycopene has
become a valuable natural colorant in food industry. Recently, consumers have
a growing interest in the use of “natural” ingredients in food products, such as
lycopene itself, that are perceived as safer and healthier than the synthetic
counterpart (Feder 2009). Current applications of lycopene mostly include juice,
nutritional beverages and bars, smoothies, snack foods, cheese and yogurt.

Lycopene is unsaturated with eleven conjugated double bonds (covalent
bonds), which makes lycopene considerably reactive with light, heat, oxygen,
acid, and metal ions which can cause problems during processing (Bruno and
others 2007). Chang and others (2006) reported the use of freeze-drying as a
mean to produce shelf-stable lycopene from tomato. Freeze-drying is better than
other commonly used drying methods, such as hot-air-drying. As the matter of
fact, freeze-drying is considered the best method to dry and preserve lycopene
sensitive pigments and its antioxidant capacities. Freeze-dried lycopene-rich
fruit, such as the autumnberry, can be regarded as a source of food additives for
fortification and natural colorant. Additionally, from the economic viewpoint, the
extract lycopene from freeze-dried autumnberry could be further developed as

food additives useful in other food applications such as instant food products.

10

2.3. Background of Autumnberry

Autumnberries are the red berry-like fruit mottled with silvery brown dots of
the autumn olive plant (Elaeagnus umbellate Thund.). Autumnberry is also
commonly known as autumn olive berry and Japanese silverberry. The autumn
olive plant is a large shrub or a small tree that has fragrant, ivory-yellow flowers;
silvery-green leaves with waxy margins but not toothed; silvery-scaly twigs; and
brown-dotted stems with a few sharp thorns hidden among the leaves (Pyle and
Willis 2002).

Autumn olive and Russian olive (Elaeagnus angustifolia L.) are the two
major species of Elaeagnus in the Unites States, although the latter is found
mainly in New England and is less frequently seen in other regions. The autumn
olive itself has four known cultivars: ‘Cardinal,’ ‘Ellagood,’ ‘Elsberry,’ and
‘Redwing’ (Kartesz 2002; Pyle and Willis 2002). Originally from southern Europe,
and ‘western and central Asia (China, Korea, and Japan), autumn olive was first
introduced to the US around 1830 as an ornamental plant (Dirr 1983). Autumn
olive grows at a rapid rate during spring to summer throughout the eastern US,
from Maine to Alabama and west to Wisconsin. The fruit is produced and ready
for harvest in the summer to fall (Strax 2006).

A mature autumn olive tree (20 years old) can reach a maximum height of
4.5 m and generally fruit production is abundant. This species can tolerate a
wide range of environmental conditions; for example, it is easily adapted to
various soil types, does not need much water and nutrients, and is hardy to

-31°C (Kartezs 2002; Black 2005). Autumn olive is valued because of it serves

11

I

different functions: 1) it attracts wildlife, mainly birds and foxes that also help
spread the seeds; 2) it prevents erosion; 3) it carries out nitrogen fixation due to
its nitrogen-fixing root nodules thus allow it to thrive in infertile habitats; and 4) it
enhances certain types of agro-forestry, for example as a “nurse” tree, which
prepares the ground for black walnut trees (Fordham and others 2001). Feral
populations of autumn olive have invaded throughout the eastern US due to their
persistent nature, seed distribution by wild animals, and ability to survive in
inferior soil and environment conditions by fixing nitrogen. Autumn olive is on the
United States Department of Agriculture (USDA) Natural Resources
Conservation Service’s invasive species list, meaning that it should not be
cultivated where it is not already established. However, some other important
crop species and agro-forestry plants are also similarly listed for example water
chestnut, Japanese honeysuckle, garlic mustard, and Japanese barberry
(Kartezs 2002; Strax 2006).

In 2001, USDA Fruits and Phytonutrients Laboratory researchers
published the facts that autumnberries have a high carotenoid content; especially
lycopene (30-70 mg/100 g), which is approximately 17 times more abundant in
autumnberries than in fresh tomato. Lycopene, a potent antioxidant, is suitable
for nutraceutical use as well as a natural red colorant in food products (Fordham
and‘others 2001 ). Wang and others (2007) studied the antioxidant capacity and
anti-cancer properties of six genotypes of autumnberry. Although some
genotypes have higher antioxidant capacity than others, the results indicated that

the extracts from all autumnberry genotypes successfully inhibited proliferation of

12

human leukemia HL-60 cancer cells and human lung epithelial cancer A549 cells,
and also induced apoptosis (programmed cell death) of HL-60 cells. These
results suggest that consuming autumnberry may have positive health effects for
human; although further studies are needed for confirmation (Wang 2007).

‘ Despite autumnberry’s palatability to human and its high lycopene content,
only few references are available to human consumption of autumnberry in the
United States. These berries are also high in acidity, similar to blueberries and
blackberries, but somewhat astringent, with slightly sweet notes. Autumnberry,
however, is normally consumed in Asia (T anaka 1976; Pannar and Kaushal
1982). The annual productivity of autumnberry ranged from 0.5 to 15 kg per tree
with approximately 8-10% of the total berry weight is in the seed (Black 2005).
This sweet-tart fruits was utilized into jams, jellies, and fruit leather. It could also

be used for juice, flavoring, and other food products. Incorporating this fruit into a

baked product, however, has never been done.

2.4. Common Drying Methods of Fruits and Vegetables

Drying is the oldest universal method used to preserve a wide range of
fruits and vegetables, and other food products. Drying involves heat that
evaporates water and a mechanism that removes the moisture from foods to the
level where the growth of microorganisms and chemical reactions are slowed
down mainly to prevent spoilage. At the same time, drying also reduces the
weight and volume of foodstuffs and prolongs their shelf life, thereby, minimizing

the cost as well as the difficulties of packaging, storage, and transportation cost.

13

Whén drying fruits and vegetables, other valuable characteristics, such as
nutritive value, flavor, and color are important to retain. Common drying methods
for fruits and fruit-based products that yield fruits powder include spray-drying,
drum-drying, and freeze-drying.

Mechanisms of spray-drying involve the transformation of liquid food
products (i.e., in a solution, suspension, or paste) into dried particulate end
products (i.e., powders, agglomerates, or granules) where the liquid feed is
atomized or sprayed into a hot dry medium that evaporates the moisture. Only
limited varieties of fruit and vegetable have been spray-dried. Fruit juices, pulps,
and-pastes can not be effectively spray-dried without incorporating additive such
as maltodextrin to prevent caking. The temperature used in spray-drying fruits
and vegetables is usually quiet high, for example, tomato paste is spray-dried at
inlet temperature ranging from 138-150°C and at 75-90°C of the outlet
temperature, which can degrade some of nutrients in the food product to some
extent. Specific care of the final products must also be taken especially as they
are both hygroscopic and thermoplastic (Mujumdar 2006).

In the drum-drying process, the initial product has to be in liquid, slurry, or
pureed form where it is applied as a thin layer on the outer surface of a slowly
revolving and heated hollow stainless steel drum. It is one of the simplest and
economical drying methods. Typical drum-drying food products are usually in
powders and flakes forms, which include milk and milk products, soup mixes,
instant cereals, and potato flakes, which can be quickly rehydrated. The

applications in fruits, however, are not widely used especially for fruits that are

14

high in sugar and low in fiber such as in berries. The addition of fiber such as
low methoxyl pectin to these fruits pureeds is also needed for better final drum-
dried outputs (Barbosa-Canovas 2005; Mujumdar 2006). The raw materials have
to be able to withstand a high temperature (>140°C) for a short time. In fruit
pureeds, this can caramelized or molten the sugar as well as degrading the heat
sensitive compounds such as enzymes, vitamins, and protein (Desobry 1997;

Abonyi 2001).

2.5. Lycopene
2.5.1. Structure of Lycopene

Lycopene is responsible for the natural orange-to-red pigment in most
fruits and vegetables with higher concentrations in tomato, guava, rosehip,
watermelon, and pink grapefruit (Bruno and others 2007). Lycopene is the most
common subclass of carotenoids in the human diet. In the carotenoids family,
overall 600 different subclasses have been extracted from plants, and more than
20 of these are from tomato alone. Lycopene along with q-, 15-, v-, and (-
carotenes are classified as the hydrocarbon carotenes, while another major class
of carotenoids, oxygenated xanthophylls, includes B-cryptoxanthin, lutein, and
zeaxanthin. Lycopene is a lipohilic (oil-soluble) pigment/phytochemical, and
naturally exists in the all-trans form. On the other hand, xanthophylls are more
polar than carotenes due to the oxygenation, and they impart the yellow-to-brown

color in plants (Shi and others 2002).

15

Lycopene structure is characterized as a polyene hydrocarbon with a

symmetrical and acyclic structure containing 13 double bonds of which 11 are

conjugated double bonds arranged in a linear array and having molecular
formula of C40H55 (Figure 2.5.1.1.). In addition, the isomerization of lycopene
from the naturally predominant thermodynamically stable trans-form to less

stable cis-geometric forms happens as the result of exposure to heat, light,

oxygen, acid, or the present of metallic ions such as Cu2+ and Fe“.

 

Figure 2.5.1.1 . Trans-lycopene (molecular weight = 536.89 glmol).

Different isomers have different stabilities due to their molecular energy as
follows, highest stability: 5-cis a all-trans 2 9-cis 2 13-cis > 15-cis > 7-cis > 11-cis:

lowest (Aganlval 2000).

2.5.2. Lycopene Degradation during Processing

The major causes of lycopene degradation in food processing are
isomerization and oxidation. In general, lycopene undergoes isomerization
during thermal processing which converts lycopene from more stable (trans) to

less stable state (cis). This transformation results in the changes of the ratio of

16

trans and cis isomers present in the food products, which also affects its
biological activities (Bruno and others 2007). Other physical and chemical
factors such as elevated temperature, light exposure, oxygen, extreme pH, and
the Involvement of metal ions (Cu2+, Fe”), degrade lycopene in food products

(Shi and others 2007).

2.5.2.1. Impact of Temperature on Lycopene Stability

In most cases, the duration of thermal treatment has less effect on the
degradation of lycopene if the heating temperature is less than 100°C. The
application of higher temperatures will result in more significant lycopene loss
especially when the heating time is long. In addition, lycopene in general
undergoes isomerization with the application of thermal processing. When the
temperature is increased above 100°C, for instance to 180°C, in general, both
the trans and cis isomers of lycopene will degrade. The level of conversion of
trans isomers to cis isomers increases with the increase in treatment temperature
up to 100°C; however, it drops significantly at 180°C. The temperature increase
from 100°C to 180°C causes approximately 76% decrease in total lycopene
content. In general, the increasing temperature (100°C to 180°C) or increasing
heating time increases the degradation of trans and cis isomer of lycopene (Shi
and others 2007).

In the processing of tomato paste, aseptic technique is widely used at
temperatures below 100°C for 4 to 5 3. During this thermal processing, the

transformation of all-trans isomers to the cis-form of lycopene occurs. The cis-

i

17

form has been proven to be more bioavailable (easily absorbed by human
tissues) than the trans-form due to its non-linear bend structure, greater solubility
of in human micelles, and/or the lower tendency to aggregate. Interestingly, even
though the thermal treatment above certain temperature and time can degrade
the total lycopene, it can also be concluded that heating increases the
bioavalability of lycopene of tomato (Gartner and others 1997; Boileau and others
2002). Additionally, the total antioxidant activity considerably increases with heat
processing although other components, such as vitamin C, are reduced by heat
proCessing (Bruno and others 2007).

2.5.2.2. Impact of Light on Lycopene Stability

In general, light exposure causes total lycopene degradation. A study
suggested that after 6 d of light exposure at room temperature, approximately
94% of trans-lycopene degraded, and at the same time the percentage of the cis-
fonn increased inconsistently until day 2 when the cis-lycopene decreased.
Overall, during the light exposure, the isomerization and the lycopene
degradation occur simultaneously (Lee and Chen 2002).

Other study suggested that the exposure of lycopene to light caused no
significant change to total and all-trans lycopene, although significant loss of cis-
isomer lycopene was observed. In addition, the light irradiation caused the
decrease in total lycopene, trans, and cis isomers meaning that light induces
lycopene oxidation which leads to total lycopene degradation (Shi and others

2007)

18

2.5.2.3. Impact of Oxygen on Lycopene Stability

The oxidation of lycopene is irreversible and will lead to fragmentation of
the molecule, producing acetone, methylheptenone, Iaevulinic aldehyde and
probably glyoxal, which cause apparent color loss and typically hay or glass-like
odors evolve. Generally, lycopene undergoes destabilization about three times
higher in the presence of oxygen than in the absence of oxygen (Shi and others
2007). Nitrogen or argon flushing can be used to replace the atmospheric
oxygen in the headspace of the storage containers; or by leaving headspace as
minimum as possible in the containers. Furthermore, cis-isomers of lycopene
are more susceptible to autoxidation than the trans-form (Anguelova and

Warthesen 2000).

2.6. Freeze-drying

In food industry, freeze-drying, or Iyophilization, is used to prepare
dehydrated food powders from their original liquid state. Freeze-drying has
gained in popularity in recent years and is considered the most attractive drying
method in extending the shelf-life of foodstuffs. During freeze-drying, the
moisture in the product is withdrawn in the form of water vapor via sublimation
from its frozen state facilitated by vacuum suction. Freeze-dried products have
been identified of having superior qualities in term of taste, aroma,
color/appearance, texture/structure, and nutritional value retention.

The apparent advantage of this freeze dehydration process is that

moisture removal from the foodstuffs can be achieved without exposing them to

19

6

high temperature. Additionally, during freeze-drying, the product structure is
maintained in a more tolerable state, resulting in maximum nutrient and flavor
volatiles retention; minimum destruction to the products’ structure and texture;
minimized shrinkage and movement of the soluble solid due to the solid ice
structure in the products and the porous structure of the product assist in rapid
and complete rehydration of the product (Welti-Chanes 2007; Mujumdar 2006).
Therefore, freeze-drying is an ideal method for drying fruits and vegetables that
are generally high in nutritional/volatile compounds, heat sensitive, and delicate
in shape, structure, and texture. The final freeze-dried fruits are generally dry,
light, and porous, retaining their original shape and structure which makes It
convenient for packing and shipping. These products can be stored for more
than one year with minimum losses on their physical, chemical, microbiological,
and organoleptic properties if properly packaged (Oetjen and Haseley 2004;

Barbosa-Canovas 2005).

2.6.1. Principles of Freeze-drying

The main components of freeze-dryer are an evaporator and a condenser
that are located inside of a vacuum chamber, a refrigeration system, and a
vacuum pump. The evaporator generates heat as the source of energy for
drying, and the condenser gathers the vapors produced from the products. The
steam ejector or vacuum pump facilitates the vacuum and low-pressure
conditions in the chamber. Figure 2.6.1.1. shows the simplified schematic of

research-scale freeze-dryer.

20

Heated Plate

 

 

 

 

_
H
H Product Tray
H
—

\ Drying Chamber

 

Non-condensibles

‘ #:rmal L_I Exhaust

Vapor Flux

 

 

 

 

 

Condenser
Vacuum Pump

Figure 2.6.1.1. Freeze-dryer simplified schematic (source: Barbosa-
Canovas and others 2005).

Two steps of freeze-drying process involve the freezing of the product with
the aid of dry ice to approximately -20 to —40°C or below, and the application of
heat to the product to directly sublime the ice in the product to water vapors
under vacuum and low pressure condition. This sublimation can only be
accomplished below the triple point of the water (at <627 Pa, 0°C) shown in

Figure 2.6.1.2..

21

V

   

 

 

2
3
a:
m
2
o.
Vapor
L Solid
cc
:1.
h III. IIIIIIIIIIIIIIIIIIIIIIIII III-II-
8 §\ Triple Point
Sublimation
c; >
o (3 Temperature

Figure 2.6.1.2. Phase diagram of water (source: Barbosa-Canovas and
others 2005).

Freeze-dryer, as it is indicated previously, is designed to remove
maximum amount of aqueous solution or other solvents in a food product under
controlled conditions without degrading other components. In the initial stage,
the refrigerator system of the freeze-dryer and the dry ice cooled the product
rapidly to below its eutectic point (the point where water goes 'directly' from solid
to liquid without partially melting to a solid-liquid combination) to support the
sublimation process. The vacuum system discharges all non-condensable
gasses from the chamber, which basically facilitates the water vapor migration
from the product to the condenser as well as creating the vapor pressure
differential necessary to enhance sublimation process. This vacuum condition

also helps prevent oxidation of the food sample by removing the air (Barbosa-

22

Canovas and others 2005).

In the vacuum chamber, controlled heat was applied to the frozen sample
from the heating plate. The temperature in the condenser must be about -40°C.
In the last stage of freeze-drying cycle, a higher heat setting again is desired to
discharge any of the remaining vapors. The applied heat to the frozen sample
results in the constant vapor migration from the product to the condenser, which
supplies sufficient energy to drive off the vapors to ensure continued sublimation.
The water vapor molecules leave the product and migrated toward the low-
pressure areas in the vacuum system surrounding the condenser. Once the
vapors got into contact with the condenser, the vapors emitted the energy and
turned into ice. The refrigeration system automatically seeks the lowest possible
temperature in proportion with the product load. At the end of the process, a
desirable moisture content of the final freeze-dried product is 1% - 4% (Oetjen

and Haseley 2004).

2.6.2. Issues and Concerns during Vacuum Evaporation

Despite its great extent of benefits for drying fruits and vegetables, freeze-
drying has some drawbacks. Freeze-drying is high in processing, energy, and
capital costs due to the slow drying rate, the use of vacuum and heat, and the
needs of specific packaging materials.

Resistance to heat and mass transfer causes the slow and long drying
time. This happens because it is difficult to obtain a homogenous ice crystal
distribution in the frozen food products to speed up the freeze-drying process.

The energy costs are expensive because the materials have to be completely

23

frozen first and the use of vacuum at low pressure and the heat supply to sublime
the ice as well as the bound water (a water portion of a tissue that does not form
ice crystals until temperature lower than -20°C). Although it is light and
convenient, the hygroscopic freeze-dried food product needs a special packaging
material to control oxidation as well as to prevent the moisture absorption from

surroundings (Welti-Chanes 2007; Mujumdar 2006).

2.7., Analyses of Antioxidant Capacity

Lycopene, one of the most prominent phytochemicals along with other
dietary antioxidants such as phenolic compounds, vitamin C and E, has relatively
high protective effects against oxidative stress and reduces the risk of developing
certain types of cancer, inflammation, cardiovascular diseases, and age-related
disorders. Therefore, more and more researchers are studying the measurement
of antioxidant capacity of food stuffs in daily human consumption. However, this
study has been an ongoing challenge to separate each antioxidant compound
independently for analysis because of the interactions of these antioxidants with
other components in the food system, as well as the possible synergistic effects
amdngst the antioxidant compounds in the matrix (Motchnik and others 1994;

C30 and Prior 2001; Koracevic and others 2001).
2.7.1. Total Phenolic Assay

The total phenolic assay is also known as the Folin-Ciocalteau (FC)

colorimetry assay. Folin-Ciocalteu assay was initially developed in 1927 for the

24

analysis of proteins (tyrosine). Since then, this method has been standardized
for determining the antioxidant capacity and phenolics In variety of foods
products including wine and dietary supplements in general (Singleton and Rossi
1965; Prior and others 2005).

The basic mechanism of this assay involves oxidation and reduction
reactions, which is the measurement of oxidation of phenols by the reagent
containing a mixture of tungsten and molybdenum oxide. The outcome of this
metal oxide reduction during the assay is a blue colored solution that exhibits a
broad light absorption with a range of 745 — 750 nm in general, and a maximum
at 765 nm. The intensity of light absorption at that wavelength is proportional to
the concentration of phenols. This color development is normally slow; however,
proper elevated temperature can be applied to speed up the reaction. Excessive
heating can cause rapid subsequent color loss and timing the assay
measurement becomes an issue. Regardless of its ease of use and high
precision, any compounds in the sample of interest containing phenolic groups
such as reducing sugars, ascorbic acid, amino acids, enediols, and reductones
will be detected, thus, limits the efficacy of this method for determining specific
flavanols or flavonoids in food samples. In addition, some other substances, for
example nonphenolic organic substances such as adenine, adenosine,
benzaldehyde, glycine react with the Folin-Ciocalteau reagent. Some inorganic
substances, such as sodium phosphate, react with the reagent and interfere with
the final measurement of elevated phenolic content (Singleton and Rossi 1965;

Prior and others 2005; Wrolstad and others 2005).

25

2.7.2. Oxygen Radical Absorbance Capacity (ORACFL)

ORACFL is Oxygen-Radical Absorbance Capacity assay utilizing
fluorescein (FL) (3’,6’-dihydroxyspiro[isobenzofuran-1[3H],9’[9H]-xanthen]-3-one)
as the fluorescent probe and 2,2’-azobis(2-amidinopropane) dihydrochloride
(AAPH), as a peroxyl radical generator. This assay measures the ability of a
compound or group of compounds to quench (the term “absorb") oxygen
radicals, which indicates the antioxidant potential of foods.

Principal mechanism of ORACFL involves the measurement of antioxidant
inhibition of peroxyl radical induced oxidations by AAPH and thus demonstrates
the classical chain breaking antioxidant activity by hydrogen atom transfer
mechanism. Peroxyl radical reacts with a fluorescent probe to yield a non-
fluorescent product, which can be easily monitored by measuring fluorescence in
the function of time at incubation temperature at 37°C (Prior and others 2005).
The proposed fluorescein oxidation pathway in the presence of AAPH according
to Cu and others (2001) is presented in Figure 2.7.2.1 ..

The ORAC reactants consists of fluorescein as the fluorescent probe,
AAPH as the radical generator, and food sample containing antioxidants at
proper series of dilutions or Trolox dilutions (a cell-permeable, water-soluble
derivative of vitamin E with known potent antioxidant properties) as the control,
which are dissolved in sodium phosphate buffer solution (Davalos and others
2004). The incubation temperature for the reaction mixture is 37°C and the

fluorescence is measured every minute until the fluorescence is completely lost.

Technically, the higher the antioxidant capacity of a product, the longer this

26

 

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22:0 ucm :0 "ouhzomv Im<< ho 3:32.. 2: E 23353 :oanxo 3": £03.32“. nomonohn. ._..~.~..N 059“.

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27

reaction goes to completion. Data analysis of the results of ORAC assay is

obtained by calculating the area under the kinetic curve (AUC) and net AUC

(AUCsampIe - AUCbIank); while the standard curve is obtained by plotting the

concentrations of Trolox in the blank sample against AUC of the sample

containing antioxidants (AUCsamp|e - AUijank) (Figure 2.7.2.2.). The calculated
data are expressed as Trolox Equivalents (TE) as micro mol of TE per gram or
liter of sample (umol of TE/g OR pmol of TE/L).

ROS (Reactive Oxygen Species)

./ I \

Fluorescent Probe Fluorescent Probe Fluorescent Probe

+ Buffer + Trolox + Sample

Loss of fluorescence Loss of fluorescence Loss of fluorescence
Sum (Blank) Sum (Blank) Sum (Blank)

Antioxidant Capacity = (Sum(Sample) - Sum (Blank)) I (Sum(Standard) — Sum(Blank))

 

Figure 2.7.2.2. Total antioxidant properties calculations using ORAch
(Source: Davalos and others 2004).

2.7.3. Spectrophotometery for Lycopene Determination

Spectrophotometry is a rapid, practical, and economical technique for
lycopene content determination in food products that is widely used in the current

6

research. Other possible methods for lycopene analysis include High

28

Performance Liquid Chromatography (HPLC) and color evaluation (Schoefs
2002; Anthon and Barrett 2007; Sandei and others 2009). In general,
spectrophotometer consists of a spectrometer for producing light of any specific
wavelength (color), and a photometer for measuring the light intensity. The
cuvette filled with sample liquid containing compound of interest is placed

between the spectrometer beam and the photometer for analysis.

The absorbance (AA ) is defined as follows:
AA = - '0910 (I Ila)

where I is the intensity of light at a selected wavelength (A) that has passed

(transmitted) through a sample, which intensity is measured by the photometer.

The photometer delivers a voltage signal to a galvanometer. This signal changes

as the amount of light absorbed by the sample changes. While I0 is the light

intensity before it enters the sample or so called incident light intensity If the
color development is associated to the concentration of a compound of interest
present in a solution, then that concentration can be measured by determining
the extent of light absorption at the appropriate wavelength (Gore 2000).

This device has been used for lycopene content determination since
1980s when this technique was initially introduced due to its short-time analysis,
good accuracy and reproducibility (Barrie and Soderstrom 1989). The identity of

carotenoids can be confirmed by their UV-vis absorption spectra, which usually

29

carried out by UV-visible (A) spectrophotometric detector at around 450 nm for
general subclasses of carotenoids generally, and at 475 nm specifically for

lycopene as shown in Figure 2.7.3.1. (Minguez-Mosquera and others 2002).

1.0 '1,
0.8 4

I

P
a:
1

 

P
.5
l

;0.2 -

mozmcqomu>

 

P
O
I

 

VTfirrlIWTI"T1"'T‘I“1FTT'TV‘Fj

350 400 450 500 550 600
Wavelength (nm)

Figure 2.7.3.1. UV-vis light absorption spectrum of selected carotenoids
(...) B-carotene, (--) capsanthin, and (—) lycopene
(source: Minguez-Mosquera and others 2002).

30

CHAPTER 3: MATERIALS AND METHODS

3.1. Plant Material
3.1.1. Autumnberry Collection

Three batches of whole autumnberries were manually harvested during
the same harvest time (October 2007) from different locations of a farm owned
by Paul Siers in Mount Pleasant, Michigan. The berries were separated from
leaves and twigs using a mechanical harvester (BEI, Inc., South Haven, MI).
They were then spray-washed with water. The weights of the three batches of
autumnberries after sorting were 15.92 kg, 2.93 kg, and 1.70 kg. The first batch
was used for bread-making as well as for physicochemical analyses. The

second and third batches were only used for physicochemical analyses.

3.1.2. Autumnberry Puree

Washed whole berries were mechanically mashed into pureed using a
fruit-pureeding machine (Sterling Electric, Inc., Indianapolis, Indiana), which
removed the seeds. The sorting and pureeing were done on-site immediately
after the harvesting. The final weight of pureed for batches 1, 2, and 3 were
14.51 kg, 2.67 kg, and 1.55 kg, respectively. Citric acid solution 0.2% (w/w) was
added into the puree to retard the post-harvest enzymatic degradation (Hui,
2006). The puree was transferred to individual 500-mL capacity wide-mouth
French clear square glass bottles. The bottles were loosely closed with vinyl-

lined screw caps, and wrapped in parafilm, and kept inside a closed cardboard

31

box to minimize exposure to light. These pureed samples were stored in a

freeZer at —20°C until further processing.

3.2. Freeze Drying
3.2.1. Sample Preparation and Freezing

The glass bottles containing frozen pureed autumnberry were thawed in a
refrigerator at 4°C for 2 to 3 d. The thawed pureed was transferred to square
plastic molds to form 20 x 20 cm slabs, which were returned to the freezer for 2 d.
Prior to the freeze-drying process, the frozen slabs were further frozen in a

styrofoam box filled with dry ice to approximately —20 to -40°C.

3.2.2. Operation
The freeze-dryer used in this experiment was a research-scale Freeze
Mobile 12 with Unitop 6OOSL chamber consisting of three heating plates (The

Virtis Company, Gardiner, NY).

Refrigeration

Mesh trays (51 x 25 cm), made out of 316—stainless steel, were put on
each heating plates inside of the chamber. Pieces of dry ice were placed on top
of the trays to completely cool down the chamber for about 30 to 45 min. The
remaining pieces of dry ice were taken out of the chamber. The frozen

autumnberry slabs were placed on the mesh trays (one slab per tray).

32

Sublimation

The vacuum vents were closed prior to the beginning of sublimation
process. The mechanical oil in the vacuum pump was changed prior to each run,
and the oil level was also checked. Fisherbrand 19 mechanical pump oil (Fisher
Scientific, Pittsburgh, PA), with specifications of ultimate pressure at 25°C, 1.13 x
10'2 Pa; pour point of —15°C; and flash point of 212.78°C, was used for the
continuous high vacuum pump throughout the freeze-drying process. Boekel
hyvac flushing oil (Boekel Ind. Inc., Philadelphia, PA) was used to wash out any
sediments and contaminants from the vacuum pump for 1 h between runs. Once
the chamber was completely sealed, the pressure of the unit decreased gradually
until around 4 to 5 Pa. Each freeze-drying run took approximately 3 to 4 d to

obtain samples at the desired moisture content of 2% to 3% (wet basis).

Storage

The freeze-dried slabs were transferred to zip-lock bags, placed in a
desiccator with drierite, and transported to a dark-dry room where these slabs
were crushed manually using mortar and pestle into powder. The samples were
repeatedly crushed until the particles passed a USA Standard Mesh Sieve ASTM
Specifications Number 50 (300 pm) (Central Scientific Co., Chicago, IL). The
freeze-dried powder was transferred to dark-brown narrow-necked 500 mL glass
bottle, leaving a minimum headspace to prevent oxidation, and then sealed with

parafilm. The samples were stored in a dark freezer at —20°C until further

analysis.

33

3.3. Moisture Content Analysis

Moisture content of the pureed, freeze-dried, and the bread samples were
determined using the vacuum oven drying method (Nielsen, 2003). Each batch
of freeze-dried autumnberry powder was measured for its moisture content in
triplicates. Approximately 2 to 2.5 g of freeze-dried sample was added to the
oven-dried aluminum-weighing dish. The weight was recorded to the nearest
0.0001 g. The samples were dried at 70°C for 5 h in a bench-top vacuum dryer
model 1430 (VWR Scientific, San Dimas, CA) equipped with a vacuum pump
(Gast Manufacturing Corporation, Benton Harbor, MI, USA). After drying, the
samples were cooled down in a desiccator and weighed to the nearest 0.0001 g.
The weight loss was determined to calculate the moisture content of the samples.
Moisture content (MC) was expressed in percentage of wet basis:

[( Weight of initial sample — weight of dry sample)/weight of initial sample] x 100

3.4. Water Activity (Aw)

Water activity (Aw) of the pureed, freeze-dried samples, and the bread

samples at all levels were measured using Aqua Lab Water Activity Meter

(Decagon Devices, Pullman, WA) in triplicate readings.

3.5. Sugar Content Analysis
Sucrose content and total sugar content of both autumnberry pureed and
freeze-dried autumnberry were estimated using the adaptation of Lane-Eynon

titration method (AOAC Method 923.09, 920.183b).

34

3.5.1. Standard Solution Preparation
A stock solution was prepared in 1-L volumetric flask by dissolving 9.5 9
sucrose crystal (Mallinckrodt Baker, Inc., Phillipsburg NJ) with 100 mL distilled
water. Five milliliters of concentrated hydrochloric acid (HCI) (EMD Chemicals
Inc., Gibbstown, NJ) was added to the solution, which was equilibrated at room
temperature for 3 d. The volume of the solution was then brought up to 1 L with
distilled water.
‘ The invert sugar standard solution was made by neutralizing 50 mL of the
stock solution with sodium hydroxide (NaOH) solution 0.1 to 1 N to pH 7.0. The

volume of this pH-adjusted solution was brought up to 250 mL with distilled water.

The standard solution contained 2 mg of invert sugar/mL solution.

3.5.2. Determining the Titration Factor

In 150-mL Erlenmeyer flasks, 5 mL each of Fehling’s reagent A (cupric
sulfate standard) and Fehling’s reagent B (potassium sodium tartrate solution
alkaline) (Fluka Chemika/Sigma-Aldrich, St. Louis, MO), 25 mL of distilled water,
15 mL of the standard solution, and glass beads were added. The solutiongwas
kept boiling throughout the titration. Two minutes after boiling, 2 to 3 drops of
methylene blue solution (C16H180lN3S - 3H20) were added as a color indicator.
The blue solution was titrated against the standard solution in a burette until the
solution turned a clear maroon color. The amount of standard solution used for
titration was recorded. The titration factor, F, was determined using the following

formula:

35

F (mg) = V1 (mL) x 2 mg/L

where, V1 = 15 mL + volume of standard solution used in titration.

3.5.3. Sample Preparation for Invert Sugar Determination
Approximately 25 g of the sample was dissolved with 225 mL of distilled
water, stirred constantly for 30 to 45 min, and equilibrated at room temperature

for 15 min. The clear upper part of the solution was used in titration.

3.5.4. Sample Preparation for Total Sugar Determination

Fifty milliliters of the dissolved sample solution (1 :9) was mixed with 5 mL
of concentrated HCI, and then put in a water bath at 65-57°C for 5 min. After
cooling, the pH of the solution was adjusted to 7.0 using 0.1 to 1 N of NaOH and
the volume was brought to 100 mL.
3.5.5. Determining the Invert Sugar and Total Sugar Content

The procedure mentioned in section 3.5.2 was followed in estimating the
invert sugar and total sugar content of the samples with some changes: 15 mL
standard solution was added; and instead of filling the burette with the standard
solution, the sample solution was filled into it. The invert sugar and total sugar

was calculated using the following formulas:

Invert sugar (g/100 mL of diluted sample) = F (g) x dilution factor x 100

 

V2 (ML)

36

where V2 = volume of sample solution used in titration. The sucrose content can
also be calculated:

Sucrose = (Total Sugar - Invert Sugar) x 0.95

3.6. Oxygen Radical Absorbance Capacity (ORACFL)
3.6.1. Sample Preparation
Approximately 1 g each of autumnberry pureed, freeze-dried autumnberry,

and autumnberry bread were analyzed for their antioxidant capacity.

3.6.2. Reagent and Standard Preparation

Fluorescein sodium salt, 2,2’-Azobis (2-amidionopropane) dihydrochloride
(AAPH) and 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (T rolox)
were purchased from Sigma-Aldrich (St. Louis, MO). Black polystyrene, round
bottom, assay plates with 8 x 12 wells (part# 3792) were obtained from Corning
Incorporated (Corning, NY).

The ORAC assay was performed as described by Huang and others
(2002) where 0.414 g AAPH was dissolved in 10 mL of 75 mM phosphate buffer
(pH,7.4) to obtain a final concentration of 153 mM. The AAPH was prepared
fresh during the experiment. The fluorescein stock solution of 4 x 10'3 mM was
prepared in 75 mM buffer (pH 7.4) and wrapped in foil and placed in refrigerator.
The fluorescein stock solution was prepared every three months. Prior to
analysis, a fluorescein working solution was made daily by diluting the

fluorescein stock solution 121000 with 75 mM phospahate buffer (pH 7.4). The

37

Trolox standards were prepared by dissolving 0.25 g trolox to 500 mL of the 75
mM phosphate buffer (pH 7.4) to give a 1.89 x 10-3 M stock solution. The stock
solution was diluted prior to each analysis with the same phosphate buffer to

6.25.125, 25, 50, and 100 pM working solutions.

3.6.3. Experimental Setup for ORACFL

The outer wells of the plates were filled with 300 IIL of water, while the
interior wells were used for experimental analyses. Into all experimental walls,
150 pL of working sodium fluorescein solution was added; to the blank wells, 25
IIL of 75 mM phosphate buffer (pH 7.4) was added; to the standard wells, 25 uL
of trolox dilutions were added; and to the sample wells, 25 pL of appropriate
dilution were added. The plate was incubated for 30 min at 37°C in the FLx800
Multi-Detection Microplate Reader (Biotek Instruments, Winooski, VT) after which
reactions were initiated by the addition of 25 IIL of AAPH solution that was freshly
prepared. The microplate reader was controlled by the Biotek Gen5 software
where it was programmed to shake the microplate automatically for 10 3 prior to
each reading. Detection parameters were set at 485 nm, 20 nm bandpass,
excitation filter and a 528 nm, 20 nm bandpass, emission filter. The fluorescence

was monitored over time and recorded every 90 s.

38

3.6.4. Data Analysis of ORACFL
ORAC values were computed according to Cao and Prior (1999). The net
area under the curve (AUC) of the standards and samples were calculated using
the trapezoidal rule as shown below:
AUC= [fl+R2+R3+... +Rn-1 +fl] At
2 2
Where R1 is the fluorescence reading at the initial time of the reaction and Rn is

the final measurement of fluorescence. IM'Iile At is the time difference between

each reading.
1 The net AUC is determined by AUCsamp|e - AUCbIanKThe standard curve

was obtained by plotting the trolox concentrations against the net AUC of
different trolox concentrations. The ORAC values of the samples could then be
calculated automatically using the Biotek Gen5 software by interpolating the
sample’s net AUC against the trolox standard curve, with the dilution factor taken
into account. Results are generally expressed as trolox equivalents (TE) as

micromol of TE per gram or per liter of sample (pmol of TE/g OR pmol of TE/L).

3.7. Total Phenolics by Folin—Ciocalteau Calorimetry
- The total phenolics assay used in this study followed the protocol written in

Handbook of Food Analytical Chemistry by Wrolstad and others (2005).

3.7.1. Preparation of saturated sodium carbonate solution
Anhydrous sodium carbonate, 200 g, was dissolved in 800 mL distilled

water and then boiled. After cooling, a few crystals of sodium carbonate were

39

added into the solution, and equilibrated for 24 h at room temperature. The
solution was passed through Whatman no. 1 filter paper, brought up to 1 L using

distilled water, and stored at room temperature until further use.

3.7.2. Preparation of Gallic acid standard solution

Gallic acid, 0.5 g, was dissolved in 10 mL ethanol and diluted to 100 mL
with distilled water to make a 5-g/L-concentration solution. Standard solutions
with concentrations of 12.5, 25, 50, 75, 100, 125, 250, and 500 mg/L, were made
by diluting 0.25, 0.5, 1, 1.5, 2, 2.5, 5, and 10 mL to 100 mL with distilled water,
respectively. These solutions could be kept for further use approximately for two

weeks at 4°C with 98% potency retention.

3.7.3. Sample Preparation and Analysis of Total Phenolics

Approximately 0.5 g of the sample in the form of filtered pureed or freeze-
dried solid was extracted using 50 mL of 80% methanol. The samples were put
in an ultrasonic bath for 20 min, and then centrifuged (Sorvall RD-SB
Refrigerated Superspeed Centrifuge by DuPont Instruments for 15 min at 78269.
One mL of sample aliquot or the standard solution was added to 25 mL
volumetric flask containing 9 mL distilled water, and then one mL of Folin-
Ciocalteau’s phenol reagent 2 N was added into all mixture, then shaken, and
equilibrated at room temperature for 5 min. Saturated sodium carbonate, 10 mL,
was added to the mixture, diluted to 25 mL with distilled water, and incubated for

90 min at room temperature. After the incubation, the blue color developed.

40

The absorbance readings were measured using UV-visible
spectrophotometer model Spectronic 21D (Milton Roy, lvyland, PA) at 750 nm
wavelength. The standard curve was constructed using the absorbance data of
the gallic acid standards versus gallic acid concentration. The linear regression
equation form the standard curve was then used to calculate the concentration of
total phenolics in the samples with taking into account the dilution factor used.

The results were expressed as gallic acid equivalents (GAE) mg / mL sample.

3.8. Color Analysis

; Color analysis on pureed and freeze-dried autumnberry, as well as on the
fortified bread at all levels (control, 3%, 6%, 9% flour basis) was done using
LabScan XE colorimeter which includes the EasyMatch QC software an
electronic recordkeeping version that is 21 CFR 11 compliant (Hunter Lab,
Reston, VA). The type of color test chosen was reflectance using Hunter Lab
and CIE D65/10 (day light at 10° angle). The pureed and freeze-dried samples
were analyzed using 1.75” glass sample cup with 1.75” port opening, while the

bread crust and crumb samples were directly put on top of the 1.75” post.

3.9. Titratable Acidity

IAOAC Official Method 942.15 (2000) in fruit products was used to measure
the titratable acidity of the pureed and freeze-dried autumnberry with some
adjustments. Ten mL of the juice from pureed or the extracted juice from 10 g of

freeze-dried sample was combined with 190 mL distilled water. The aliquot of 50

41

mL was titrated with 0.1 N NaOH to the end point of pH of 8.1 — 8.2 using
phenolphthalein indicator. The acid content was expressed as percentage (w/w,

wet weight).

3.10. Spectrophotometric Method for Lycopene Measurement

The low volume hexane extraction method (LVHEM) was used to measure
the lycopene content of pureed and freeze—dried autumberry samples, and bread
samples. Fish (2002) method on LVHEM was performed. Approximately 1 9
(determined to the nearest 0.05 g) triplicate samples were weighed into the 50
mL - polypropylene tubes that contained 5 mL of 0.05% (v/v) butylated
hydroxytoluene (BHT) in acetone, 5 mL of 95% HPLC grade ethanol, and 10 mL
of hexane. The tubes were put in the sonicator, Bransonic 2510, (Branson
Ultrasonic Corporation, Danbury, CT) for 20 minutes for homogenization.
Samples were extracted on a gyratory water bath orbital shaker G76 (New
Brunswick Scientific, Edison, NJ) at 180 rpm for 15 min on ice. After shaking, 3
mL of distilled water were added into each tube, and shaken for additional 5 min.
The tubes were left on a stable surface at room temperature for 5 min to allow
the phase separation. The upper hexane layer was transferred to the
spectrophotometer cuvettes. The absorbance readings were measured using
UV-visible spectrophotometer model Spectronic 21D (Milton Roy, lvyland, PA) at

503 nm wavelength blanked with hexane. The lycopene standard curve was

obtained using pure lycopene from Wako Chemicals USA, Inc. (Richmond, VA).

3.11. Bread-making

A fifty-pound bag of Bread flour (Seal of Minnesota-Bakers Flour AD)
containing bleached wheat flour, malted barley flour, and potassium bromate was
obtained from ConAgra, Omaha, NE. The flour composition information provided
by the manufacturer includes 14.83% protein, 82.37% carbohydrates, and 2.81%
fat. Method 10-1OB: optimized straight-dough bread-making method of American
Association of Cereal Chemists lntemational (AACCI) was used in making the
autumnberry-fortified bread at different levels: control, 3%, 6%, and 9%

fortification (flour basis).

Fan'nograph

A farinograph (C. W. Brabender Instrument, Inc., South Hackensack, NJ)
was used to estimate the water absorption of the flour and measure dough
characteristics of flour, i.e., development time, dough stability and softening.
This information is important in optimizing the bread-making process.

The moisture content of the bread flour was determined using IR-200
Moisture Analyzer (Denver Instrument Company, Arvada, CO). The amount of
flour: used for the farinograph was determined using the following formula (for
flour with 14% of moisture content or 86% of dry matter, 50 g of flour is needed
for farinograph readings):

Weight of flour for farinograph readings = 50 x 86
100 — MC of flour

43

The amount of the freeze-dried autumnberry incorporated with the flour at
3%, 6%, and 9% levels were also adjusted to 14% moisture content using the
following formula:

Weight of freeze-dried sample incorporated = % level of fortification x 86
100 — MC of freeze-dried sample

 

The weight of flour used for bread-baking (100 g flour basis) is two times
the weight of flour used for farinograph. Based on the farinogram, two important
pieces of information were obtained: (1) the water absorption of the flour (water
needed for optimum dough forming) was estimated plus 2 mL of water, and (2)
the mixing time of the dough to reach optimal stability. Distilled water was used

throughout the process.

Baking Process
‘ Method 10-1 OB: optimized straight-dough bread-making method of
American Association of Cereal Chemists International (AACCI) was used to
make the bread samples, with some modification. The formulation for the bread
includes salt-sugar solution, ascorbic acid solution, and yeast suspension, while

other ingredients listed in the method were not used in this particular experiment.

The baking time was adjusted to 20 minutes for 400 — 425°F.

3.11.1. Bread Texture
The bread firmness was measured according to AACC Method 74-09

(AACC 2000) using a TA-XT2i Texture Analyser that includes Texture Expert-

0

44

Stable Micro Systems version 1.22 software (Texture Technologies Corp,
Scarsdale, NY). A 50 mm diameter cylindrical probes, for 25% of compression;
at a test speed of 1.0 mm/s was used in this firmness test. At least triplicates
slices of bread (25 mm thickness each) cut from the center of the bread loaf were

tested.

3.11.2. Bread Volume and Density
The volume and density of each bread samples were estimated using a
voluLmeter filled with rape seeds. The density (p) was then calculated as mass

over volume (m/v).

3.12. Statistical Analysis for the Physicochemical Properties

The physicochemical properties of autumnberry in two presentations
(pureed and freeze-dried) were measured in three batches of product. The
differences in physicochemical properties between the two presentations of
autumnberry were the point of interest. Each batch of each presentation was
analyzed repeatedly three times. For each physicochemical property of interest,
a general linear mixed mode! was fitted using the MIXED procedure of SAS (SAS
Institute Inc, Cary, NC). The statistical model included the fixed effect of
presentation (pureed versus freeze-dried) and the random effect of batch. In
addition, a random interaction between presentation and batch was fitted to the
model to account for sub-sampling (technical replication) in the experimental

design. Least square mean estimates (with standard errors) at each

45

presentation were also shown. Pertinent painrvise comparisons were performed
using Tukey’s adjustment to avoid inflation of Type I error rate. A likelihood ratio
test (also to determine homogenous or heterogeneous variances applied) was
estimated for each treatment to improve the model fit. Model assumptions were
evaluated using residual plots and assumptions were considered to be

appropriately met.

3.13. Sensory Tests and Evaluations

A preliminary consumer acceptability testing (n=52) using a 9-point
hedonic scale (1-dislike extremely, 2-dislike very much, 3-dislike moderately, 4-
dislike slightly, 5-neither like nor dislike, 6-Iike slightly, 7-Iike moderately, 8-Iike
very much, 9-like extremely) was conducted. The samples presented were
bread containing 10% and 20% autumnberry pureed. This test was done to
acquire consumer perception and acceptance on the attributes of autumnberry-
containing bread such as aroma, color, appearance, flavor, body/texture, and
overall acceptance. Some general survey-typed questions: “How do you like the
idea of bread containing antioxidant lycopene?” “Would you purchase bread with
health benefits over regular white bread?” “Please rank the three samples in
order of preference for each attributes” were also asked during the test. The
resulting data was analyzed using an ANOVA and Tukey’s test.

A trained panel (n=12) was further conducted to evaluate the final samples
of fortified bread (using freeze-dried autumnbeny) at different levels (control, 3%,

6%, and 9%). Sensory characteristics of interest were crust color, crumb color,

46

firmness, crumb cell structure uniformity, intensity of yeasty flavor, and intensity
of autumnberry flavor. The duration of the training session was eight weeks with
additional three weeks for conducting triplicates descriptive tests using
randomized samples, unstructured line scaling method (0-15, less to more). The
trained panelists were asked to assess the samples’ sensory characteristics, as
trained. This test was repeated three times. Sensory scores were recorded for
each treatment assessed by each panelist at each run, utilizing SIMS 2000
sensory software (Sensory Computer System, Morristown, NJ). The resulting
data was further studied utilizing SAS statistical software (SAS Institute Inc, Cary,
NC):

The trained panel utilized commercial breads as the references since there
are no universal standard for bread characteristics. For each sensory attribute of
interest, the references were set up in a 5-point increment. For the firmness
attribute, the panelists were trained to bite the bread (excluding the crust) using
their front teeth as their first judgment and then bring it back to the wisdom teeth
for further evaluation. The yeasty flavor references were prepared using a bread
mix with increased active dry yeast content: 0%, 5%, 10%, 15%, and 20% (w/w).
Lastly, the references for autumnberry flavor were prepared using the

autumnberry pureed and water mixture at 0%, 3%, 6%, and 9% (WV).

47

3.14. Statistical Analysis for Sensory

An ANOVA and Tukey’s test were applied to the preliminary consumer
acceptability, or so called discrimination/acceptance test results. This type of test
is simple, requiring untrained panelists, and commonly used method with results
proVen to be reliable. ANOVA was used to analyze interaction effects between
variables, therefore, to test more complex hypotheses. Furthermore, this test
was conducted to examine the significant difference between the sensory
attributes of each sample. Some additional questions were also asked to the
panelists to gain consumers’ perception on the product idea, their preference
among samples, as well as their purchasing habit.

Furthermore, for each sensory characteristic of interest, a general linear
mixed model was fitted using the MIXED procedure of SAS. The model
integrated the fixed effect of treatment at four levels: 0%, 3%, 6%, and 9%
fortification, and the random effect of panelist. In addition, a random interaction
between panelist and treatment was fitted to the model to account for sub-
sampling in the experimental design. A likelihood ratio test (either homogenous
or heterogeneous variances) was estimated for each treatment to improving the
model fit. Residual plot and model assumptions for each characteristics of
interest were evaluated and considered appropriately met. Least square mean
estimates for levels of the fixed effects are provided. Pertinent pairwise

comparisons were performed using Tukey’s adjustment to avoid inflation of Type

I error rate.

48

‘ Additionally, Principal Component Analysis (PCA) on the trained panel
sensory results was performed using XLStat (XLStat, New York, NY). PCA
basically transforms a certain number of data points (variables) into a smaller
number of principal components. PCA reveals the internal structure of the data

in a way which best explains the relationship between data points with sensory

attributes.

49

CHAPTER 4: RESULTS AND DISCUSSIONS

4.1. Statistical Analysis of the Physicochemical Properties of Pureed and

Freeze-dried Autumnberry

The mixed model of SAS was considered to be valid if the residual plots
for each physicochemical characteristic of interest were well distributed (cloud of
points) without depicting a certain trend or shape, such as a fan-shape. In this
case, the statistical analyses of all physicochemical attribute of interest were
valid. In addition, there was no significant difference among the three batches of
pureed autumnberry in term of all physicochemical properties of interest. There
was also no significant difference among the three batches of freeze-dried

autumnberry examined.

4.1.1. CIE L*a*b* Color

‘ The CIE L*a*b* is 3-dimensional color space specified by the lntemational
Commission on Illumination (Commission Internationale d’EcIairage—CIE). CIE
L*a*b* characterizes all color visible to the human eye and was created to Serve
as a tool independent model to be used as a reference. It is the color scale used
as a universal standard and uniform color scale so that the color values or
measurements could be easily compared. L* = 100 represents a perfect
reflecting diffuser, thus, the color appears to be white, while L* = 0 represents

black. The a* and b* axes have no precise numerical limits whether the value is

negative or positive. The negative 3* and positive a*correspond to green and

50

red, respectively, while the negative b* is blue and positive b* is yellow

(HunterLab 2008).

A significant effect of presentation of autumnberry was identified on color
L* (P<0.0001), such that color L* was greater in freeze-dried samples compared
to pureed. This means the color of the freeze-dried samples was perceived to be
lighter than that of the pureed ones. On the CIE color a* attribute, a significant
effect of presentation of autumnberry was identified on color a* (P<0.0001), such
that color a* was greater in pureed sample compared to freeze-dried. In this
case, the pureed samples had more red color perception than the freeze-dried
counterpart. A significant effect of presentation of autumnberry was identified on
colo‘r b* (P<0.0001), such that color b* was greater in pureed compared to
freeze-dried. This means that the pureed impacts more yellow color perception

than the freeze-dried sample (Figure 4.1.1.1.).

Although freeze-drying is considered to be the best drying method to
preserve natural color of fruits, the significant loss of moisture in the freeze-dried
sample changes the surface characteristics of the fruit (smooth and porous) and
alters its light reflectivity, hence, it is perceived to be lighter in color. During
storage, the open porous texture of the freeze-dried sample also allows oxygen
to enter and cause oxidative deterioration of lipids, which overtime can gradually
diminish the color of freeze-dried fruits (Fellows 2000). In addition, based on the
Observation, sugar in autumnberry tended to crystallize after freeze-drying,

forming a thin white layer on the surface of the autumnberry slab. This also

51

contributed to the perceived lighter color of the freeze-dried autumnberry

compared to the pureed sample.

 

Color CIE L*a*b*
I Pureed vs. Freeze-dried Autumnberry

; 60.00 . ”*ng —

 

50.00 ‘ 7

38.66 i 0.09

 

 

. 40.00 ~

29.54 i 0.13

28.82 :t 0.39
30.00 i 2,“ - , .

‘ 20.21 :t 0.10 18.62 i 0.32
20.00 . rut ,- , ,

10.00 ~ ,_ ,_

 

 

 

 

0.00~ —— .~—— -,
Pureed L* Freeze- Pureed a* Freeze- Pureed b“ Freeze-
dn‘ed L* dn’ed a* dried b* I

#0134115. 635.6": L:a;b*:fpureedivs. freeze-dried autumnberry.

4.1 .2. Acid Content

I

The contents of four major acids commonly found in berries: citric, lactic,
acetic, and malic acids were analyzed in both pureed and freeze-dried
autumnberry. A significant effect of presentation of autumnberry was identified
for the citric, lactic, and acetic acid content (P<0.0003), such that pureed
samples had greater concentration of citric acid than freeze-dried counterpart. A

significant effect of presentation of autumnberry was also identified (P<0.0001)

52

such that the pureed autumnberry had greater concentration of malic acid than

freeze-dried autumnberry (Figure 4.1.2.1.).

The predominant organic acids in berries such as citric and malic acid,
with the compliment of phenolic acids, are responsible for the titratable acidity of
fruits. Titratable acidity is considered as a better overall indicator of fruit quality,
whereas the pH is often a poor marker of fruit quality characteristics (Nielsen
2003). In case of autumnberry, its acidity is comparable to blueberries and
blackberries, but somewhat more astringent, with slightly sweet notes (Tanaka
1976; Parmar and Kaushal 1982). The titratable acidity for citric acid (the most
predominant organic acid in berry fruits) of fresh blueberries comparing to the
experimental pureed autumnberries from this study is ranging from 0.54 to 1.13
and‘0.41 to 0.44, respectively (Zhao 2007). The concentration of organic acids
present in fruits is also crucial for fruits preservation, for example, maintaining a
low pH in processed fruits such as in jams and jellies. Additionally, different

acids own various levels of effectiveness in lowering heat resistant of

microorganisms: lactic acid > citric acid > acetic acid (Ranganna 1986).

The air containing free-radicals could easily penetrate the smooth and
porous texture of freeze-dried autumnberry during the grinding process from slab
into powder. The porous structure and the larger surface area of freeze-dried
autumnberry powder further facilitated oxidation during experiment. When the
oxidation took place, these organic acids acted as free-radical quenchers, thus

they degraded overtime. Organic acid degradation might also have occurred

53

during storage especially due to storage temperature fluctuation and/or improper
packaging materials (Fellows 2000). These factors may have caused the
significant differences in organic acid contents between the pureed and freeze-
dried autumnberry with freeze-dried autumnberry having lower organic acids

compared to the pureed counterpart.

 

Acid Content I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. Pureed vs. Freeze-dried Autumnberry 5
0.7 -. _.__. _ ____,_
0.6 _,--- 0.59 1 0.00__ .-, __ j
7% 05 ___; ,___ g ”0.37 10.00 i __ ,
.3 04 ff” 1 0'00 . ______ -, 0.391000 -_ 0.4410.00‘mfl
1% . 0‘33 i 0'00 0.31 :l: 0.00 0'35 {0'00 '
I ”E; 0.3 - — 2. ~ -— _.,-, - .-# _ _.__. --__.. p.-.“
‘ g 0.2 -. ~~ —- « “___ .- . _-_.. ..-.-._ - ,- we .2
0.1 . ~—» —— . e.— L———_-_—« 22 .._. _.__-... ___ *1
0 r ' l i d— I I I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Pureed Freeze- Pureed Freeze- Pureed Freeze- Pureed Freeze-
Citric dried Lactic dried Acetic dried Malic dried
9 Acid Citric Acid Lactic Acid Acetic Acid Malic
1 Acid Acid Acid Acid

 

 

 

Figure 4.1.2.1. Acid contents of pureed vs. freeze-dried autumnberry in
, dried weight basis.

4.1 .3. Sugar Content

High sugars and high acids are essential for desirable berry flavor. The
acidity from predominant organic acids in berry fruits is counterbalanced by the
sugar content (Kader 1991). Invert sugar, total sugar, and sucrose contents of

pureed and freeze-dried autumnberry were analyzed. Invert sugar is a mixture of

54

equal parts of glucose and fructose resulting from the hydrolysis of sucrose,
which is achieved through the action of invertase or a concentrated acid. It is
found naturally in fruits and honey. Invert sugar is also produced artificially for
use in the food industry because it is sweeter than sucrose and it also has lower

tendency to crystallize (Damodaran and others 2008).

According to sugar content analysis on autumnberry in this study, there
were no significant differences between the pureed and freeze-dried
autumnberry in term of invert sugar (P = 0.36), total sugar (P = 0.64), and
sucrose (P = 0.13) (Figure 4.1.3.1.). Thus, the freeze-drying process did not
change the sugar content of autumnberry. The sugar content of autumnberry is
higher but still comparable to that of the other berries claimed to have similar
sweetness/soumess. The sucrose and total sugar contents of autumnberry,
blueberry, and blackberry (g/1009 of wet weight) were as follows: 1 and 15, 0.11
and 9.96, and 0.07 and 4.88 (USDA National Nutrient Database for Standard

Reference 2006).

55

Sugar Content
Pureed vs. F reeze-dried Autumnberry

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0'8 , 066—1003 0.71 $0.03 0.691003 7
0 0'7 ' . if . 0.611003 L E
E 0.6 I
8 0.5
8
'E 04
33
o 0.3
2’
a? 0.2
0 1 00510.01 00810.01
0 I’ T I I f I
Pureed Freeze- Pureed Freeze- Pureed Freeze-
hvert dried Invert Sucrose dried Total Sugar dried Total
Sugar Sugar Sucrose Sugar

Figure 4.1.3.1. Sugar contents of pureed vs. freeze-dried autumnberry in
dried weight basis.

4.1.4. Moisture Content

On the moisture content attribute, a significant effect of presentation of
autumnberry was identified on moisture content (P<0.0001), such that pureed
samples (80.30%) had greater moisture content than freeze-dried samples
(2.32%). Freeze-drying decreased 3 great deal of water content in autumnbery
puree which is a favorable condition to inhibit any potential action(s) of
microorganisms and enzyme that would spoil or degrade the fruit faster (Welti-

Chanes and Hui 2007).

56

4.1.5. Water Activity (Aw)

Water activity of the pureed and freeze-dried autumnberry samples from

each batch was also measured using Water Activity Meter: AquaLab Series 3

(Decagon, Pullman, WA). There was a statistical significant difference between
the pureed autumnberry (0.97 i 0.00) and freeze-dried autumnberry (0.13 :I:
0.01 ). The water activity of the pureed presentation ranged from 0.964 — 0.991 :I:
0.009, which showed that the pureed form, provided an environment for bacteria
and certain types of yeast to grow. Freeze-drying was able to lower the water

activity to Aw ranging from 0.088 — 0.164 :I: 0.033 (Table 4.1.5.1) (Damodaran

and others 2008).

Table 4.1.5.1. Water activity of autumnberry pureed and freeze-dried

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Presentation Water Activity (Aw) Average Standard Deviation
Pureed Batch 1 0.983
Pureed Batch 1 0.975
Pureed Batch 1 0.971
Pureed Batch 2 0.965
Pureed Batch 2 0.966 0975 0-009
Pureed Batch 2 0.964
Pureed Batch 3 0.991
Pureed Batch 3 0.981
Pureed Batch 3 0.975
Freeze-dried Batch 1 0.091
Freeze-dried Batch 1 0.089
Freeze-dried Batch 1 0.088
Freeze-dried Batch 2 0.142
Freeze-dried Batch 2 0.163 (1133 0-033
Freeze-dried Batch 2 0.145
Freeze-dried Batch 3 0.156
Fre‘eze-dried Batch 3 0.157
Freeze-dried Batch 3 0.164

 

57

 

4.1.6. Total Phenolics

On the total phenolics content, no significant difference was detected
between the freeze-dried samples (6.73 :l: 0.34 mg GAE/g of dried sample) and
the pureed (7.32 i 0.34 mg GAE/g of dried sample) counterpart on the total
phenolics attribute (P = 0.31 ). Total phenolic acids can impart bitter or astringent
flavors in most of berry fruits. Together with other predominant organic acids,
phenolic acids contribute to the basic taste components of most of berries (Zhao
2007). The total phenolics content of blueberry and blackberry were 5.31 and
6.60 mg GAE/g of wet weight, respectively (Zheng and Wang 2003; Wu and
others 2004). The average total phenolics content of the experimental
autumnberry in this study was found to be 1.44 mg GAE/g of wet weight, which

was less compared to that of blueberry and blackberry.

4.1.7. ORAC (Total Antioxidant)

For total antioxidant (ORAC) content, a significant effect of presentation of
autumnberry was identified on total antioxidant content (P<0.0001), such that the
freeze-dried sample (102.36 i 3.27 pmol TE/g dried sample) had lower ORAC
values than those of pureed sample (144.14 :I: 4.86 pmol TE/g dried sample). In
this experiment, the freeze-drying process notably degraded the total antioxidant
in autumnberry.

Literature reported the ORAC value of blueberry, blackberry, cranberry,

cherry, raspberry, and strawberry as follows: 61.84, 52.45, 92.56, 33.44, 47.65,

58

and 35.41 umol TE/g wet weight, respectively (Zheng and Wang 2003; Wu and
others 2003). While the average ORAC value of the experimental autumnberry
was 28.43 pmol TE/g wet weight. According to this data, autumnberry is
relatively comparable to cherry and strawberry in term of total antioxidant
capacity.

Antioxidants in general are sensitive to heat and oxidation. Although most
berries contain only small amounts of lipids, the oxidation (incorporation of air
into the porous structure of freeze-dried fruits) of unsaturated fatty acids in the
fruits produces hydroperoxides, which react further by oxidation to produce
aldehydes, ketones, and acids, and eventually cause rancidity and bad odor.
Antioxidants in the berries undergo auto-oxidation to slow down this process
(Fellows 2000). The highly unsaturated structure makes it easier for antioxidant
lycopene to be oxidized. This explains why the total antioxidant capacity after
freeze-drying (including storage time) was lower than that of the original pureed

autumnberry.

4.1.8. Lycopene Content

A standard curve of lycopene was constructed as a reference for
determining the lycopene content in the pureed and freeze-dried autumnberry,
and bread samples (Figure 4.1.8.1.). Lycopene has UV-visible light absorption
spectrum characteristics due to the presence of conjugated double bonds of its
hydrocarbon chain (polyene). This means the positions of the bands of

maximum light absorption (Amax) area are a function of the number of conjugated

59

double bonds in the molecule. The lipophilic lycopene has UV-vis absorption
maxima ranging from 446 to 503 nm in low polarity solvents such as hexane.
Thus, for lycopene content determination, the samples absorbance readings

were taken at 503 nm UV-vis (Minguez-Mosquera 2002).

 

1 Absorbance versus Lycopene Concentration in Hexane

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.0 ——s
2 3'2 ,
. 1F ‘ " fi'” -222- ‘
-_ // -- ~
‘6' A06 +- -—* -—--—-—-— --
we: -_.- , _..._ 1._
g 253': I /' 4y = 0.3306x + 000144
CD . 4"**—"‘ a—' “"“ ’ ' "‘ " 2 I““
R = 0.9999
3 0.2 -_ ——~— ,2 — ---— _
< 0.1 — W —*‘M ‘
0.0 - _ _ . , I .
0.0 0.5 1.0 1.5 2.0 2.5 3.0
; Concentration (mg/100mL)
Figure 4.1.8.1. Lycopene standard curve using the spectrophotometric

method.

For lycopene content, a significant effect of presentation of autumnberry
was identified on lycopene content (P<0.0001), such that pureed sample (2.90 i
0.04 mg/g dried sample) contained higher lycopene than those of freeze-dried
(0.70 :I: 0.00 mg/g dried sample) (Table 4.1.8.1.). The average degradation of
lycopene after freeze-drying in this study was 75.86%, which was considerably
high compared to the degradation of lycopene from tomato after freeze-drying:
20-40%. In addition, the tomato’s lycopene degradation was higher in freeze-

dried tomato samples compared with oven-dried samples between 25 and 75°C.

60

This loss also increased with the exposure of tomato solids to air, light, and high
temperature during storage (Sharma and Maguer 1996; Nguyen and Schwartz
1998). According to this experiment, the lycopene in autumnberry is less stable
compared to lycopene in tomato after freeze-drying.

0

Table 4.1.8.1. Summary of lycopene content of pureed and freeze-dried
autumnberry (P = pureed, FD = Freeze-dried, number in the
sample ID re resents the batch number)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

S I Lycopene Lchpene
am 9 Ab orban n n r
lop Ru" 503 nm (:0? (3379:1113; (".9613 at: 3:1... Avmg" 3:11:70:
weight) sample)
P1 1 1.710 0.5168 2.627
P2 1 1 .888 0.5707 2.901
P3 1 1 .900 0.5743 2.890
P1 2 1.889 0.5710 2.897
P2 2 1.998 0.6039 3.072 2.90 01287
P3 2 1 .885 0.5698 2.876
P1 3 1.880 0.5682 2.857
P2 3 1 .880 0.5682 2.889
P3 3 1.980 0.5985 3.061
F D1 1 2.301 0.6956 0.711
FD2 1 2.222 0.6716 0.687
FD3 1 2.301 0.6956 0.713
FD1 2 2.222 0.6717 0.688
FD2 2 2.301 0.6956 0.711 0.70 00133
FD3 2 2.301 0.6956 0.713
FD1 3 2.301 0.6956 0.713
FD2 3 2.222 0.6717 0.688
FD3 3 2.222 0.6717 0.687

 

 

 

 

 

 

 

There are insufficient and inconsistent (no clear trend) data on the effect
of freeze-drying on carotenoids, including lycopene. Some of the examples:
ethanol extracts of tomato skins contained more lycopene than freeze—dried skins

(lnakuma and others 1998); frozen or boiled soybeans had a higher Iutein and

O

61

6

beta-carotene content than freeze-dried beans (Simonne and others 2000);
freeze-drying preserved more carotenoids from daylily (Hemerocallis disticha)
flowers than air-drying (Tai and Chen 2000); freeze-drying also preserved more
carotenoids in eight Malaysian medicinal plants than oven-drying at 50°C for 9 h
or at 70°C for 1 h. No solid conclusions can be drawn from these varied studies.
It seems that freeze-drying can have a negative effect on carotenoid preservation
(Jones 1979). In the case for autumnberry in this study, freeze-drying retained
only 24.14% of lycopene from its original pureed form.

The comparisons of average lycopene content of autumnberry vs. other
fruits and tomato products are shown in Figure 4.1.8.2. (Bramley 2000 and
Boileau 2002). The average lycopene content of the experimental autumnberry
was 57.13 :I: 2.45 mg/100g wet weight. The literature based lycopene content of
autumnberry ranged from 30 to 70 mg/1009 wet weight. Compared to the
lycopene content of other selected fruits and tomato products, experimental
autumnberry had the highest lycopene content. The spectrophotometric method,
however, is not useful to differentiate the trans and cis lycopene, thus, the
lycopene content estimated in this study was the overall lycopene including both

trans and cis isomers.

62

 

r Average lycopene content of selected fruits and tomato products

 

 

 

 

  

 

 

 
  
 
       
       
 

 

 

 

 

    

 

 

 

 

60.00 ~~~~fi57.13—-~~-— . . _ _ 1
' I
' 50.00 :1. m_-_ _ __
a t I
g
g 40.00 3 z
:3:
H gr:
2. 30.001 ;:;. _
a 8:8.
8 I 53%
= 29001 — k —~— .
CE” 313‘; 1020 11.67 12.71
10.00 +Hf’t‘ 2:: ’33 l
0.00 -
‘3 ° 0 '{F e e ‘9 0 ¢
“é 9‘6 6“? °\° 0'59 “95° 7‘ e°° 0" ‘90 59$ 49" 5
0v {9 ‘0 6° Q Q0 6 09 ‘o .5. 9 -
I ‘Ef f 6“ 4‘69 éé‘ $g¢ 6‘69 63‘ 0&9 ‘9‘. y i
l V’ 7.3. 2‘0 e «o «O & «000 Q i
I

 

 

Figure 4.1.82. Average lycopenevcontent of selected fruits and tomato
products (Bramley 2000 and Boileau 2002).
- Autumnberry: average lycopene content - literature based.
- Autumnberry*: average lycopene content - experimental
based.
4.1.9 Conclusions
, In term of color, the freeze-dried format had lighter color, less intense red
color, and about the same blue and green color perception compared to pureed
format. The pureed autumnberry had more citric, lactic, acetic, and malic acid
content compared to freeze-dried counterpart. In term of sugar content, both
pureed and freeze-dried autumnberry contained the same level of invert sugar,
total sugar, and sucrose as compared to the pureed format. The freeze-drying
brought down the high moisture content in the pureed to a level where the water

activity was very low. Thus, freeze drying preserved the autumnberry from

enzymatic activity as well as from mold, yeast, and bacteria growth, and also

made the sample more dry, concentrated, and convenient for storage and future
applications.

Total phenolics content for both pureed and freeze-dried presentations
were not significantly different, meaning that the freeze-drying process had no or
a little effect on total phenolics of autumnberry. On the other hand, total
antioxidant capacity and lycopene content of the pureed autumnberry were
higher than the freeze-dried counterpart. According to the statistical analyses,
there were no significant differences between batches of pureed and freeze-dried
samples for all physicochemical properties examined. Thus, the freeze-dried
sample from all batches could be used for bread-making. The summary table of

the physicochemical properties raw data is provided in Table A2.

4.2. Bread Analyses
4.2.1. Bread Physical Analysis

For the bread physical analysis, the samples were analyzed for their
weight/loaf, loaf volume, density, and firmness. Table 4.2.1.1. provides the
results summary of the bread physical analysis. Figure 4.2.1.1., 4.2.1.2.,
4.2.1.3., and 4.2.1.4. represent the typical results of the bread firmness (texture
analysis) for the bread control, 3% fortified bread, 6% fortified bread, and 9%

fortified bread samples, respectively.

64

 

 

40.0
2 30.0
0
2
,2 20.0

10.04

at , 2 . .
2.0 4.0 6.0 8.0
Time (s)

Figure 4.2.1.1. Typical results of the control bread texture analysis.

 

 

40.0
g 30.0
, e
B
o
"- 20.0
100
/r ’t
(LL—é"... , , 1
2.0 4.0 6.0 8.0
Time (s)

Figure 4.2.1.2. Typical results of the 3% fortified-bread texture analysis.

65

40,01.

30.0I-

Force (N)

20.0"

100-

 

    

 

210 410 610 8'0

Time (s)

Figure 4.2.1.3. Typical results of the 6% fortified-bread texture analysis.

400

Force (N)

20.0-

10.0-

30.01

 

O-I

 

 

 

Time (s)

Figure 4.2.1 .4. Typical results of the 9% fortified-bread texture analysis.

66

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8.8 88 888 883 .8
8: 8.8 8.8 88 88 88 888 888 888 88 883 8.3; .8
8.8 88 8.88 883 .8
8.: 88 888 883 .8
88 8.3 5.3 88 88 88 88.» 8:8 888 28 8.5; 8.E .8
8.9 88 8.88 8.3; .8
8.... : 88 88 E 8.8. .8
88 8.8. 2.9 88 88 28 8.8 88:. 883. 88 889 S82 .8
8.9 88 88k 882 .8
8.: 8.8 88k 882 .8
88 88. 88. 88 28 28 :8. 85k 888 88 882 8.5 .8
88 88 888 8.8. .8
28
2038380 c2550 A023 co=£>eo A03 :o_uu_>oo A3 0355
28:98 8222 “w“. 28:98 89.22 8888 28:98 8292 85:; 2888 8285. 88.88.25 828

 

 

 

 

 

 

 

 

 

 

 

 

 

 

moi—Emu tween. 6.8902828 _no_m>:n 2: Co 935 meEam ._..._...~.¢ 038...

67

The average weight of a loaf of control bread, 3%-fortified, 6%-fortified,
9%-fortified was 138.49, 139.29, 141.99, and 145.29 respectively. The weight of
the bread increased as the bread was fortified with higher level of freeze-dried
autumnberry. The same trend applied to the bread density (from 0.1793 9/cc to
0.2667 g/cc). The peak force reflected the firmness of the bread samples. The
larger the peak force meant that the more force was needed to compress the
bread, thus, the firmer texture. In this case, the higher the level of autumnberry
fortification, the firmer the bread texture (10.64 to 27.22 N). The volume of the
bread loaf, however, was decreasing at higher level of fortification from 771.7 cc
(control) to 545.0 cc (9%-fortified). The 9%-fortified bread had the heaviest
weight, smaller volume, higher density, hence, firmest texture. The 3%-fortified

bread was the closest to the control in term of its physical attributes.

The bread dough fermentation was facilitated by active dry yeast. The pH
of the bread dough went down due to the addition of acidic freeze-dried
autumnberry. This elevated acidity disturbed the protein matrix in the flour, which
lessened the ability of the protein for trapping the C02 produced by the yeast
fermentation that responsible for leavening the bread. In addition, berries also
contain high in fiber which also changed the protein network/structure, and as a
result decreased the 002 entrapment by the protein to leaven the bread (Zhao
2007). The additional sugar from the freeze-dried autumnberry retarded the
fermentation process. In ideal condition, low levels of sugar act as food for yeast
and sugar normally speeds up the fermentation. However, at the elevated sugar

levels, the osmotic pressure exerted by the sugar slows down the yeast

68

fermentation. (Hui, Corke, and others 2006). Thus, less C02 was produced to
leaven the bread, which resulted in lower bread volume in higher fortification of
freeze-dried autumnberry. This also explained the big pocket holes in the bread

control (less dense) and smaller pocket holes in the 9% fortified bread (denser).
4.2.2. Bread Physicochemical Analyses
4.2.2.1. Bread Moisture Content and Water Activity

Table 4.2.2.1.1. Summary Table of Bread Moisture Content and Water

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Activity
Bread Water Standard Moisture Standard
Sample Activitx(Aw) Average Deviation Content (%) Average Deviation
0% 0.94 4.13
0% 0.91 0.92 0.02 4.17 4.10 0.09
0% 0.91 4.00
3% 0.95 4.08
3% 0.94 0.94 0.01 4.17 4.12 0.05
3% 0.93 4.11
6% 0.95 3.97
6% 0.93 0.93 0.01 4.12 4.04 0.08
6% 0.93 4.01
9% 0.95 4.02
9% 0.95 0.95 0.00 4.11 4.04 0.05
9% 0.95 4.01

 

 

 

 

 

 

 

 

The water activity of the bread with freeze-dried autumnberry fortification

 

did not change significantly compared to the bread control. The fortified bread
had slightly elevated water activity. The moisture content of the fortified bread
samples, however, was slightly lower than the moisture content compared to the
control. In conclusion, the water activity and the moisture content of the fortified

bread were comparable to the control bread.

69

4.2.2.2. Bread ORAC-value (Total Antioxidant Capacity)

Table 4.2.2.2.1. Summary of ORAC-values of the bread samples in dried

 

 

 

 

 

 

 

 

 

 

 

 

 

weight basis (after baking)
Bread ORAC-value in
Sample Run dry matter Average Siflgtfi:
ID (umol TElg)
Control 1 0.16
Control 2 0.21 0.19 0.03
Control 3 0.19
3% 1 1.43
3% 2 1.46 1.36 0.14
3% 3 1.19
6% 1 2.17
6% 2 1 .97 2.13 0.14
6% 3 2.24
9% 1 2.62
9% 2 4.05 3.45 0.74
9% 3 3.68

 

 

 

 

 

 

 

On average, the bread samples (average of 1409/loaf) contained
increasing total antioxidant capacity (in dried weight) of 190.40 pmol TE, 298.20
pmol TE, and 483.00 pmol TE of for 3%, 6%, and 9% fortified bread samples,
respectively. The bread control contained very low amount of total antioxidant of

26.6 pmol TE/1409 of bread (Table 4.2.2.2.1.).

Initially, a loaf of 3%, 6%, and 9% fortified-bread (average of 1409/Ioaf)
consisted of 2.64 g, 5.28 g, and 7.92 g of freeze-dried autumnberry, respectively.
Since on average, the freeze-dried autumnberry contained 102.36 pmol TE/g of
freeze-dried autumnberry in dried basis (Section 4.1.7.), thus, before the baking

process, these loaves of bread had approximately 270.23, 540.46, and 810.69

70

pmol TE of antioxidant capacity for 3%, 6%, and 9% fortified bread. Comparing
the total antioxidant content before and after baking process, the fortified bread
samples 3%, 6%, and 9% only retained 70.46%, 55.18%, and 59.58% of total

antioxidant capacity.

4.2.2.3. Bread Lycopene Content

Table 4.2.2.3.1. Summary of lycopene content of the bread samples

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lycopene
Content
Sample Run (mg/g of Average 332:3:
dried
sample)
B Control 1 0.000
B Control 2 0.000 0.00000 0.0000
B Control 3 0.000
B 3% 1 0.001451
B 3% 2 0.001452 0.0013 0.0002
B 3% 3 0.001136
B 6% 1 0.002079
B 6% 2 0.002082 0.0022 0.0002
B 6% 3 0.002395
B 9% 1 0.003025
B 9% 2 0.002713 0.0030 0.0003
B 9% 3 0.003340

 

The average weight of a loaf of bread was 140 g. The bread samples
contained 0.18 mg lycopene/1409 bread, 0.31 mg lycopene/1409 bread, and
0.42 mg lycopene/1409 bread for 3%, 6%, and 9% fortified bread samples
respectively (Table 4.22.3.1.) The bread control, however, did not contain any

lycopene.

A loaf of 3%, 6%, and 9% fortified-bread (average of 1409/loaf) consisted

of 2.64 g, 5.28 g, and 7.92 9 of freeze-dried autumnberry, respectively, before

71

baking. Since in average, the freeze-dried autumnberry contained 0.7 mg of
lycopene/g of freeze-dried autumnberry in dried basis, thus, before the baking
process, these loaves of bread had approximately 1.85 mg, 3.70 mg, and 5.54
mg of lycopene. Comparing the lycopene content before and after baking
process, the fortified bread samples 3%, 6%, and 9% fortified bread only retained

9.73%, 8.38%, and 7.58% of lycopene.

0

4.2.3. Conclusions

The fortified bread samples were comparable to the control in term of
water activity and moisture content attributes. The total antioxidant and the
lycopene content of bread sample with higher level of fortification were higher,
thus, had higher nutraceutical value. There was also a similar pattern that the
lower the degree of fortification, the better the retention of both total antioxidant

capacity and lycopene.

4.33 Sensory Results and Analyses
4.3.1. Consumer Acceptability Panel

The raw data of the consumer acceptability test was attached in the Table
A.1.. Refer to Table A.1.1 to Table A.1.6. for the ANOVA test summary of each

sensory attribute. The Tukey’s test summary is presented in table 4.3.1.1 below.

72

Table 4.3.1.1. Tukey’s test summary for consumer acceptability test

 

 

 

 

 

 

 

 

 

Body Overall
. Aroma Color Appearance Texture Flavor Acceptance

Standard Error of

Mean (SEM) 0.12 0.20 0.16 0.15 0.17 0.15
Least Significant

Difference (LSD) 0.35 0.55 0.46 0.41 0.47 0.43

Mean Differences

Control vs. 10% 0.23 0.52 0.79 0.12 0.19 0.25

Control vs. 20% 0.70 1.33 1.19 0.21 0.27 0.79

10% vs. 20% 0.47 0.81 0.40 0.10 0.46 0.54

 

 

 

 

 

 

 

 

Significant differences were perceived between control and 20% samples,
and also between 10 and 20% samples in term of aroma, color, and overall
acceptance, while no significant differences were detected between control and
10% samples. Significant differences in term of aroma, color, and overall
acceptance were detected in 10 and 20% fortification samples. On the other
hand, no significant differences were detected in body/texture and flavor among
all three treatments. In terms of appearance, the control appeared to be
significantly different compared to the other’two samples, while 10 and 20%

samples were not significantly different from each other (Figure 4.3.1.1.).

73

Summary of Consumer Acceptability Testing
(significant differences among samples)

aab

aab aaa

 

9.00
8.00
7.00
6.00
5.00
4.00
3.00
l 2.00
‘ 1.00
0.00

240820!

 

O O U 0
2020202020

0.020203.

9-point Hedonic Scale

 

 

 

 

 

 

 

Figarie 43;. Sir;mafl of consjumer aciceptabilityitest for control, 10%,
and 20% fortification (flour basis) using autumnberry pureed
for each sensory attribute.

As of the results of this preliminary sensory study show, panelists had
higher acceptance toward the 10°/o over the 20% bread sample. The aroma for
20% bread was significantly low compared to the 10% and control samples. The
10% bread sample was the most preferable sample in terms of flavor compared
to other samples. Ninety percent of the panelists liked the idea of bread
containing antioxidants/functional ingredient. Seventy-seven percent of them
would buy breads with health benefits versus regular white bread if they were
available in the market. They liked the appearance, body texture, and overall

acceptance of the autumnberry bread compared to the control. In conclusion,

74

panelists liked the idea of autumnberry-fortified bread (high lycopene bread).
They also liked the bread sample that had subtle, moderate levels of
autumnberry fortification, which they thought had closer sensory profile to regular

white bread that they are used to consume on a regular basis.

4.3.2. Statistical Analysis for Trained Panel I Descriptive Analysis

Residual plots for each sensory characteristics of interest were examined
for the outliers and shape. The respond values distribution on the residual plot
should be well distributed (cloud of points) without depicting a certain trend or

shape, Le. a fan-shaped, to be valid.

4.3.2.1 . Crust Color

On the crust color attribute, heterogeneous variances were estimated for
each treatment. A significant effect of freeze-dried autumnberry fortification on
the perception of crust color of bread samples was identified (P<0.0001), such
that as the levels of freeze-dried autumnberry fortification increased from 0% to
6%, the greater the crust color scorings were recorded (Figure 4.3.2.1.), This
means that as the level of fortification increased, the darker the crust color of the
bread samples were (light golden brown to dark brown). However, no difference
in crust color was identified between 6% and 9% bread samples fortification. The
effect of supplementation on cmst color scoring was additive and linear

(P<0.0001).

75

 

Crust Color

 

 

 

 

 

 

 

 

o—smw.:s.mo>~ioo
.. . T... 'l-
l
l

 

 

.
I

 

l 0% Fortification

l

 

 

 

 

3% Fortification

A_T

 

 

 

 

 

 

 

 

 

 

 

 

_T_____.

6% Fortification 9% Fortification

Figure 4.3.2.1. Trained panel crust color perception scores (increasing
score indicates perceived darker color).

4.3.2.2. Crumb Color

For the crumb color, homogenous variances were estimated for each

treatment. A significant effect of autumnberry fortification on crumb color

perception of bread samples was identified (P<0.0001), such that greater levels

of autumnberry fortification yielded greater crust color scoring at all levels. Each

0

3% increase in autumnberry fortification yielded a significant increment on crumb

color perception. The greater the crust coloring recorded, the color perception

was more off from the white color.

In addition, a cubic trend (P = 0.05) was

identified between autumnberry fortification and crumb color perception.

76

 

 

 

 

Crumb Color

 

 

l

 

E3 ('3

|
I

l

l
|
A
i
0’
.h
a

 

 

 

—L_L
o-A
l
l
l
l
l

 

l
l
l
l

 

 

 

 

 

 

 

 

 

 

o-xmcmhcnmxrco
’ |
N
_s
N
N

 

 

 

 

 
   

 

 

0% Fortification 3% Fortification

_ ___r__—

6% Fortification 9% Fortification

“*4,
l

l

Figure 4.3.2.2. Trained panel crumb color perception (increasing score
indicates perceived darker color).

4.3.2.3 Crumb Cell Uniformity

On the crumb cell uniformity, homogenous variances were estimated for
each treatment. A significant effect of autumnberry fortification on crumb cell
uniformity perception was identified (P<0.0001) as crumb cell uniformity scoring
for 9% fortification was greater than that for 0% bread sample (Figure 4.3.2.3.).
This means that the crumb cell was more uniform on the 9% fortification sample
than the 0% sample. No significant differences were identified between any of
the other levels of fortification. Additionally, the relationship between fortification

level and crumb cell uniformity scoring was linear (P=0.0002).

77

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

O—th-hU'lCDNm-CD

0% Fortification 3% Fortification ' 6% Fortification 9% Fortification

 

 

Figure 4.3.2.3. Trained panel crumb cell uniformity perception (increasing
score indicates more uniform crumb cells).

4.3.2.4. Firmness

On the firmness attribute, homogenous variances were estimated for each
treatment. At P<0.0001, a significant effect of autumnberry fortification on
firmness perception of the bread samples was identified. Fortification at 6% and
9% levels resulted in greater firmness than 0% fortification (Figure 4.3.2.4.). Also,
9%, but not 6%, fortification yielded greater firmness than 3%. On the other hand,
no significant difference was identified between 6% and 9% fortification samples
and between 0% and 3%. Additionally, the relationship between fortification level

and firmness scoring was linear (P<0.0001).

15—

Firmness

 

 

14.

 

 

 

12

 

 

 

11-

 

 

 

 

 

 

 

 

 

 

(O
.1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F—____.—_ .

.P_ _—1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

O—‘NJJOAUIODVCD
—'|

 

0% Fortification

3% Fortification

6% Fortification

9% Fortification

 

l
|
l
l
t
Figure 4.3.2.4. Trained panel firmness perception (increasing score

indicates more firmer crumb).

4.3.2.5. Yeasty Flavor

For the yeasty flavor attribute, homogenous variances were estimated for

each treatment.

A linear effect was identified between level of autumnberry

fortification and yeasty flavor of bread samples (P=0.0142), such that yeasty

flavor scoring of the bread tended to decrease linearly with greater fortification

levels.

significant (P=0.0870) (Figure 4.3.2.5.).

79

No pairwise comparison between fortification levels turned out to be

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[ ‘ Yeasty Flavor
15 ~—~ ——— “—7 Wfi —— m--- - fl —
14 + ——— -——... -- -——
l 13 i» fi — W7 ~w—— AAAAA -
12 ,_ 8 ~ , —— ,, "—8—w ————
11 l » _.. .— * - ~ ~— -—- -
10 i__. __ “___ __ __ _.. "_.- ___-
9 j 7.06a __ __ __e _ “w--- W
8 g 5.943
7' T _ _ 5.33a ___ 500a _
6 ___ .__ ___ ___ .
7 I
5 1 —- _.- —- - ——.- - —
4 8% __ 8 _ _‘fl __ _ , __ __ _..
3 («w —~— — ——8 fi.._ —« —~
2‘+— — — 8 ————~ ____- —
1 . __ ._ ____ _- _u‘ Q
0 {e -- ——T -— . -.— -——. é
‘ . 0% Fortification 3% Fortification 6% Fortification 9% Fortification

 

Figure 4.3.2.5. Trained panel yeasty flavor perception (increasing score
indicates more yeasty flavor).

4.3.2.6 Autumnberry Flavor

The significant effect of autumnberry fortification on the autumnberry flavor
scoring was identified (P<0.0001). The intensity of autumnberry flavor increased
linearly with increased level of fortification (P<0.0001). The 9% fortification
samples yielded the greatest intensity of autumnberry flavor scoring whereas 0%
fortification yielded the least autumnberry flavor scoring (Figure 4.3.2.6).
Estimates of residual variation varied with supplementation treatment and
appeared to increase with level of supplementation. There are significant

differences in the autumnberry flavor among all treatments.

80

 

 

Aummnberry Flavor

8
l

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1o .. -
9 8 _ ___
8 _-__ _. _
7 -____, _
6 _ ___ __
5 , __ 3.61b e
4 8.__ _ _
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2 .— 0.19a _.--_—_.-...
‘l -.
o —-— L—J—a -
0% Fortification 3% Fortification 6% Fortification 9% Fortification

 

 

 

 

Figure {3.2.6. Trained panel autumnberry flavor perception (increasing
score indicates more yeasty flavor).
4.3.3. Principal Component Analysis (PCA)

According to the PCA results (Figure 4.5.3.1.), the yeasty flavor was a
distinct characteristic to the control bread sample. While the crumb cell
uniformity was closely related to 9% bread samples. The 6% bread samples
were closely related to crust color, crumb color, and autumnberry flavor attributes,
so was the firmness. In this case, the 3% bread samples were not closely
related to any of the attributes. It is also important to note that the relationship

between yeasty flavor and crumb cell uniformity were opposite to each other.

81

 

Variables (axes F1 and F2: 68.74 %)

 

 

 

 
      
   
   

 

 

 

 

 

_/ \
0.75 ,/ Yeasty Flavor \
,, ‘ F “
/ Control mnesg\
0'5 / (0% fortification) \
r//
9/ _ . Autumnberry
A 0.25 / 6% fortrficatlon Flavor
8\°
h Crust Color 1
O
05 0 L“ 7* 1 7 i
x l, : Cmmb Color
u. \ l 1
c.25 - /
“x o . . . . /
\\ 3 /° fortification 1 9% fortification /
“0.5 \V\\ ‘T /
\\ L Uniformity /
° p.75 \\ //
\\‘~\ . ,-/‘///
-1 \..\_\_M __J #149,, .--/—
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
F1 (50.67 %)

 

 

 

 

 

 

 

 

 

Figure 4.3.3.1. Principal Component Analysis (PCA) graph for the sensory;
trained panel.

4.3.4. Conclusions

Autumnberry is a low-cost underutilized fruit with potential for highly
valuable antioxidants or nutraceuticals applications. Overall, autumnberry
fortified bread had high consumers acceptability. The bread fortified with lower
level of autumnberry was preferred compared to the higher level of fortification.

The flavor of the fortified bread was comparable to that of sourdough bread.

82

FUTURE STUDIES AND RECOMMENDATIONS
The following topics are recommended for future research:
1) To study the bioavailability of lycopene extracted from autumnberry.
2) To study different applications of autumnberry in food products.
3) To investigate different drying methods that may be more efficient and

cheaper than freeze-drying.

83

APPENDICES

Table A.1. Raw Data of Consumer Acceptability Panel

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Panellst Sample AROMA COLOR APPEARANCE 1.38.325 FLAVOR Ac%"E'f§AA';:EE
Auouvmousom Control 7 6 6 7 7 7
ANONYMOUSOOZ Control 7 5 7 6 6 6
ANONYMOUSOOS Control 6 6 6 9 9 6
ANONYMOUS004 Control 9 9 9 9 9 9
ANONYMOUSOOS Control 5 4 5 6 6 6
ANONYMOUSOOG Control 6 6 6 6 7 6
ANONYMOUSOO7 Control 6 6 7 7 7 7
ANONYMOUSOO8 Control 6 6 6 6 7 7
ANONYMOUSOOQ Control 7 6 6 7 7 7
ANONYMOUS010 Control 6 6 6 6 6 6
ANONYMOUSO1 1 Control 6 6 6 6 7 6
ANONYMOUS012 Control 5 7 6 3 4 5
ANONYMOUS013 Control 6 9 6 6 7 6
ANONYMOUSO14 Control 6 6 6 6 6 6
ANONYMOUSO15 Control 6 7 7 7 7 7
ANONYMOUSO16 Control 7 6 6 7 7 6
ANONYMOUSO17 Control 6 6 6 7 7 6
ANONYMOUS018 Control 7 6 6 7 6 7
ANONYMOUSO19 Control 7 4 4 4 5 4
ANONYMOUSOZO Control 7 6 7 7 7 7
ANONYMOUSOZ1 Control 7 5 6 7 6 7
ANONYMOUSOZZ Control 9 9 9 9 9 9
ANONYMOU8023 Control 7 6 6 7 7 7
ANONYMOUSOZ4 Control 9 9 9 6 6 6
ANONYMOUSOZS Control 7 6 ' 7 6 7 6
ANONYMOUSOZG Control 9 6 6 6 6 6
ANONYMOU8027 Control 5 6 6 6 6 6 '
ANONYMOUSOZB Control 6 6 4 7 5 6
ANONYMOUS029 Control 7 7 7 7 7 7
ANONYMOUS030 Control 6 9 9 9 6 6
ANONYMOU3031 Control 4 5 6 7 4 5
ANONYMOUSO32 Control 7 6 6 6 6 7
ANONYMOUSO33 Control 7 7 7 7 7 7
ANONYMOUSO34 Control 7 6 6 6 7 6
ANONYMOUSO35 Control 9 9 6 6 5 6
ANONYMOUSO36 Control 4 5 5 6 5 5
ANONYMous037 Control 6 6 6 6 9 9
ANONYMOUSO38 Control 6 7 7 7 7 7
ANONYMOUSO39 Control 7 6 6 7 6 7
ANONYMOUSO40 Control 7 6 9 7 7 6

 

 

 

 

 

 

 

 

84

 

Table A.1. Continued

 

ANONYMOUSO41

Control

 

ANONYMOUSO42

Control

 

ANONYMOUSO43

Control

 

ANONYMOUSO44

Control

 

ANONYMOUSO45

Control

 

ANONYMOUSO46

Control

 

ANONYMOUSO47

Control

 

ANONYMOUSO48

Control

 

ANONYMOUSO49

Control

 

ANONYMOUSO5O

 

ANONYMOUSOS1

 

ANONYMOUS052

 

ANONYMOUSOO1

 

ANONYMOUSOOZ

 

ANONYMOUSOO3

 

ANONYMOUSOO4

 

ANONYMOUS005

 

ANONYMOUSOOG

 

ANONYMOUSOO7

 

ANONYMOUSOOB

 

ANONYMOUSOOQ

 

ANONYMOUSO10

 

ANONYMOUSO1 1

 

ANONYMOUSO12

 

ANONYMOUSO13

 

ANONYMOUSO14

 

ANONYMOUSOI 5

 

ANONYMOUSO16

 

ANONYMOUSO17

 

ANONYMOUSO18

 

ANONYMOUSO19

 

ANONYMOUSOZO

 

ANONYMOUSOZ1

 

ANONYMOUSOZZ

 

ANONYMOU8023

 

ANONYMOUS024

 

ANONYMOU8025

 

ANONYMOUSOZG

 

ANONYMOU8027

 

ANONYMOUSOZB

 

ANONYMOUS029

 

ANONYMOUSO30

 

ANONYMOUSO31

 

ANONYMOUSO32

 

ANONYMOU8033

 

ANONYMOU3034

 

 

ANONYMOUSO35

 

 

muwuwmmwmmwmNoummwumuwmmbmmmmmwwmwwummmwahwwmmm

 

mmbcowtocomoooocnmotocnwmmalhummmmmmmmmwAcostastcocooocnolmcocoalbco

 

\INQOG‘DGNQVNGQQO’N-kNQ-ANQOUI#NNWGOGUGQGQGGQQNNQOQNN

 

awhwuouwmmwwwmwwmmwRmmmmmmmmmwmhoocncncomtocowmucnmcomoo

 

@m-bNmGNGGCD‘lNG‘DNGGGNGGQQNVGGQGQGNONGGVOQNC’IQGGU’IWG

 

mflnbflNONO’QONNO‘DNQO’QO’UOGQQQQOQQNGNm‘lmmmmmmmflmflmh‘l

 

85

 

Table A.1. Continued

 

ANONYMOUSO36

10%

 

ANONYMOUSO37

10%

 

ANONYMOUSO38

10%

 

ANONYMOUSO39

10%

 

ANONYMOUSO40

1 0%

 

ANONYMOUSO41

1 0%

 

ANONYMOUSO42

10%

 

ANONYMOUS043

10°/o

 

ANONYMOUSO44

1 0%

 

ANONYMOUSO45

10°/o

 

ANONYMOUSO46

10°/o

 

ANONYMOUSO47

1 0%

 

ANONYMOUSO48

10°/o

 

ANONYMOUSO49

10°/o

 

ANONYMOUSO50

1 0%

 

ANONYMOU8051

1 0%

 

ANONYMOUS052

1 0%

 

ANONYMOUSOO1

20%

 

ANONYMOUSOOZ

20%

 

ANONYMOUSOO3

20%

 

ANONYMOUSOO4

20%

 

ANONYMOUS005

20%

 

ANONYMOUSOOG

20%

 

ANONYMOUSOO7

20%

 

ANONYMOUSOOS

20%

 

ANONYMOUSOOQ

20%

 

ANONYMOUSO1O

20%

 

ANONYMOUSO11

20%

 

ANONYMOUSO12

20%

 

ANONYMOUSO13

20%

 

ANONYMOUSO14

20%

 

ANONYMOUSO15

20%

 

ANONYMOUSO16

20%

 

ANONYMOUSO1?

20%

 

ANONYMOUSO18

20%

 

ANONYMOUSO19

20%

 

ANONYMOUSOZO

20%

 

ANONYMOUSOZI

20%

 

ANONYMOUSOZZ

20%

 

ANONYMOUS023

20%

 

ANONYMOU8024

20%

 

ANONYMOUSOZS

20%

 

ANONYMOUSOZB

20%

 

ANONYMOUS027

20%

 

ANONYMOUSOZB

20%

 

ANONYMOU8029

20%

 

 

ANONYMOU8030

 

20%

 

wwwwwwwww9666wawwwwuwwwuwwwwwwwmmmmmhnatalbot\latmwona-

 

QQUNNU’IO)030GmbGVNmmemQGMNOGQNQONVG#NDGGGVOGANNO)

 

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ONNQNU’IGQCDOCDNGO’UIQVONVONNNNONN‘OhVGQQNNmQVO}NQNNVm-h

 

mflmbhwflmmmmmmm#NN#VO¢ONQNGOGNQANNGQO’NO-bflolVNNUIN‘Dh

 

\INQOIUIO’Nwamwmmwflfl#mmmNGNQGO’NOAONQGGNQQVC’INQNOINQ&

 

86

 

Table A.1. Continued

 

ANONYMOU8031

20%

 

ANONYMOU8032

20%

 

ANONYMOUSO33

20%

 

ANONYMOUSO34

20%

 

ANONYMOUSO35

2 0%

 

ANONYMOUSO36

20%

 

ANONYMOUSO37

20%

 

ANONYMOUSO38

20%

 

ANONYMOU8039

20%

 

ANONYMOUSO40

20%

 

ANONYMOUSO41

20%

 

ANONYMOUSO42

20%

 

ANONYMOUSO43

20%

 

ANONYMOUSO44

20%

 

ANONYMOUSO45

20%

 

ANONYMOUSO46

20%

 

ANONYMOUSO47

20%

 

ANONYMOUSO48

20%

 

ANONYMOUSO49

20%

 

ANONYMOUSOSO

20%

 

ANONYMOU8051

20%

 

 

ANONYMOU8052

 

20%

 

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GANNUG@OWNGNQO}QV#AbUNN

 

MGNNhO’GNr‘NOQO’QQVU‘IO’UUVQ

 

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#OJNGGOJCDANQNNGQQOQOCDNé‘Jh

 

mNNGAO’thMNQNNONhNVQNm

 

87

 

 

Table A.1.1. Summary and ANOVA table for aroma

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SUMMARY for AROMA
Groups/1' reatments Count Sum Average Variance
Control 52 368 7.08 1.64
10% 52 356 6.85 1.54
‘ 20% 52 332 6.38 2.48
ANOVA
Source of Variation SS df MS F P-value F crit
Treatments 12.92 2 6.46 3.42 0.04 3.06
Panelists 288.77 153 1.89
Total 301.69 155
Table A.1.2. Summary and ANOVA table for color
SUMMARY for COLOR
Groups/Treatments Count Sum Average Variance
Control 52 378 7.27 2.00
10% 52 351 6.75 2.23
20% 52 309 5.94 3.58
ANOVA
Source of Variation SS df MS F P-value F crit
Treatments 46.5 2 23.25 8.92 0.001 3.06
Panelists 398.81 153 2.61
Total 445.31 155
Table A.1.3. Summary and ANOVA table for appearance
SUMMARY for APPEARANCE
Groups/Treatments Count Sum Average Variance
Control 52 389 7.48 1.27
10% 52 348 6.69 2.41
20% 52 327 6.29 2.60
ANOVA
Source of Variation SS df MS F P-value F crit
Treatments 38.24 2 19.12 9.12 0.001 3.06
Panelists 320.73 153 2.10
Total 358.97 155

 

 

 

 

 

 

 

 

88

 

 

 

Table A.1.4. Summary and ANOVA table for body/texture

 

SUMMARY for BODY/TEXTURE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Groups/1' reatments Count Sum Average Variance
Control 52 374 7.19 1.41
10% 52 368 7.08 1.56
20% 52 363 6.98 1.78
ANOVA 7
Source of Variation SS df MS F P-value F crit
Treatments 1.17 2 0.58 0.37 0.69 3.06
Panelists 242.75 153 1.59
Total 243.92 155
Table A.1.5. Summary and ANOVA table for flavor
SUMMARY for FLAVOR
Groups/Treatments Count Sum Avegge Variance
Control 52 357 6.87 1.81
10% 52 367 7.06 1.78
20% 52 343 6.60 2.17
ANOVA
Source of Variation SS df MS F P-value F crit
Treatments 5.59 2 2.79 1.46 0.24 3.06
Panelists 293.40 153 1.92
Total 298.99 155

 

 

 

 

 

 

 

 

Table A.1.6. Summary and ANOVA table for overall acceptability

 

SUMMARY for OVERALL ACCEPTANCE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Groups/Treatments Count Sum Average Variance
Control 52 375 7.21 1 .31
‘ 10% 52 362 6.96 1 .49
20% 52 334 6.42 2.33
ANOVA
Source of Variation SS df MS F P-value F crit
Treatments 16.88 2 8.44 4.94 0.01 3.06
Panelists 261.29 153 1.71
Total 278.17 155

 

 

89

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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90

Appendix 3. SAS Output for Physicochemical Properties

 

 

A. 3.1. CIE Color L*
Covariance Parameter Estimates
Standard 2
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation*Batch 0 . . .
Residual Presentation FD 0.6436 0.3218 2.00 0.0228
Residual Presentation Pure 0.01687 0.008437 2.00 0.0227
Fit Statistics
-2 Res Log Likelihood 13.6
AIC (smaller is better) 17.6
AICC (smaller is better) 18.5
BIC (smaller is better) 15.8
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 8.42 9117.66 <.0001
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > |t|
Presentation FD 55.4033 0.2674 8 207.18 (.0001
Presentation Pure 29.5367 0.04330 8 682.12 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value Pr > It]
Presentation FD Pure 25.8667 0.2709 8.42 95.49 <.0001
Differences of Least Squares Means
Effect Presentation Presentation Adjustment Adj P
Presentation FD Pure Tukey-Kramer <.0001

 

91

 

 

A.3.2. Color a*

 

 

Covariance Parameter Estimates

Standard 2
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation*Batch 0 . . .
Residual Presentation FD 0.1548 0.07740 2.00 0.0227
Residual Presentation Pure 0.007461 0.003731 2.00 0.0228
t
Fit Statistics

-2 Res Log Likelihood -4.3

AIC (smaller is better) -0.3

AICC (smaller is better) 0.6

BIC (smaller is better) -2.1

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr > F
Presentation 1 8.77 5373.98 <.0001

Least Squares Means

Standard
Effect Presentation Estimate Error DF t Value Pr > |t|
Presentation FD 28.8178 0.1312 8 219.73 <.0001
Presentation Pure 38.6611 0.02879 8 1342.75 <.0001

Differences of Least Squares Means

Standard
Effect Presentation Presentation Estimate Error DF t Value Pr > Itl
Presentation FD Pure -9.8433 0.1343 8.77 -73.31 <.0001
. Differences of Least Squares Means
Effect Presentation Presentation Adjustment Adj P
Presentation FD Pure Tukey-Kramer <.0001

 

92

 

 

A.3.‘3. Color b*

 

 

Covariance Parameter Estimates

Standard Z
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation*Batch 0 . . .
Residual Presentation FD 0.1051 0.05256 2.00 0.0228
Residual Presentation Pure 0.009450 0.004725 2.00 0.0228
Fit Statistics
-2 Res Log Likelihood -5.S
AIC (smaller is better) -1.5
AICC (smaller is better) -0.6
BIC (smaller is better) -3.3
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 9.43 198.89 <.0001
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > ltl
Presentation FD 18.6189 0.1081 8 172.29 <.0001
Presentation Pure 20.2100 0.03240 8 623.69 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value Pr > ltl
Presentation FD Pure -1.5911 0.1128 9.43 -14.10 <.0001
Differences of Least Squares Means
Effect Presentation Presentation Adjustment Adj P
Presentation FD Pure Tukey-Kramer <.0001

 

93

A.3.4. Citric Acid

 

 

Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 1 306E-6 3 146E-6 0.42 0.3390
Presentation*Batch 1 185E-6 3 033E-6 0.39 0.3480
Residual S 286E-6 2 158E-6 2.45 0.0072
. Fit Statistics
-2 Res Log Likelihood -141.3
AIC (smaller is better) -135.3
AICC (smaller is better) -133.3
BIC (smaller is better) -138.0
Type 3 Tests of Fixed Effects
Num Den
Effect OF DE F Value Pr > F
Presentation 1 2 3444.16 0.0003
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > It!
Presentation FD 0.3346 0.001191 3.66 281.00 <.0001
Presentation Pure 0.4168 0.001191 3.66 350.08 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
0
Presentation FD Pure -0.08226 0.001402 2 -58.69
Differences of Least Squares Means
Effect Presentation Presentation Pr > ItI Adjustment Adj P
Presentation FD Pure 0.0003 Tukey-Kramer 0.0003

 

94

 

A.3.‘5. Lactic Acid

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 2.585E-6 6.225E-6 0.42 0.3390
Presentation*Batch 2.344E-6 6.002E-6 0.39 0.3480
Residual 0.000010 4.27E-6 2.45 0.0072
Fit Statistics
-2 Res Log Likelihood -130.4
AIC (smaller is better) -124.4
AICC (smaller is better) -122.4
BIC (smaller is better) -127.1
Type 3 Tests of Fixed Effects
Num Den
Effect DE DE F Value Pr > F
Presentation 1 2 3444.16 0.0003
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > ltl
Presentation FD 0.4706 0.001675 3.66 281.00 <.0001
Presentation Pure 0.5863 0.001675 3.66 350.08 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure -0.1157 0.001972 2 -58.69
Differences of Least Squares Means
Effect Presentation Presentation Pr > ItI Adjustment Adj P
Presentation FD Pure 0.0003 Tukey-Kramer 0.0003

 

95

 

 

A.3.6. Acetic Acid

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 1.149E-6 2 766E-6 0.42 0.3390
Presentation*Batch 1.042E-6 2.667E-6 0.39 0.3480
Residual 4.648E 6 1.897E 6 2.45 0.0072
Fit Statistics
. -2 Res Log Likelihood -143.3
AIC (smaller is better) -137.3
AICC (smaller is better) -135.3
BIC (smaller is better) -140.0
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 2 3444.16 0.0003
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > |t|
Presentation FD 0.3137 0.001116 3.66 281.00 <.0001
Presentation Pure 0.3909 0.001116 3.66 350.08 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure -0.07713 0.001314 2 -58.69
6
Differences of Least Squares Means
Effect Presentation Presentation Pr > |t| Adjustment Adj P
Presentation FD Pure 0.0003 Tukey-Kramer 0.0003

 

96

 

 

A.3.7. Malic Acid

 

The Mixed Procedure

Covariance Parameter Estimates

 

 

Standard 2
Cov Parm Group Estimate Error Value Pr Z
Batch 0
Presentation*Batch 0 .

Residual Presentation FD 7.991E-7 0 . .
Residual Presentation Pure 0.000015 7.443E-6 2.00 0.0228
Fit Statistics
-2 Res Log Likelihood -151.4
AIC (smaller is better) -147.4
AICC (smaller is better) -146.5
BIC (smaller is better) -149.2
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 8.86 4255.88 <.0001

‘ Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > |t|
Presentation FD 0.3503 0.000298 8 1175.60 <.0001
Presentation Pure 0.4364 0.001286 8 339.34 <.0001

Differences of Least Squares Means

Standard

Effect Presentation Presentation Estimate Error DF t Value

Presentation FD

Pure -0.08612 0.001320 8.86 -65.24

Differences of Least Squares Means

Effect Presentation Presentation Pr > Itl Adjustment Adj P

Presentation FD

Pure <.0001 Tukey-Kramer <.0001

 

97

 

A.3.8. Invert Sugar

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 2.28E-22
Presentation*Batch 0 . . .
Residual 0.01085 0.003838 2.83 0.0023
Fit Statistics
-2 Res Log Likelihood -22.6
. AIC (smaller is better) 820.6
AICC (smaller is better) -20.3
BIC (smaller is better) -21.5
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 16 0.89 0.3600
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 0.6103 0.03473 16 17.57 <.0001
Presentation Pure 0.6566 0.03473 16 18.91 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure -0.04628 0.04911 16 -0.94
0 Differences of Least Squares Means
Effect Presentation Presentation Pr > Itl Adjustment Adj P
Presentation FD Pure 0.3600 Tukey 0.3600

 

98

 

A.3.9. Total Sugar

 

The Mixed Procedure

 

‘ Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 9
Presentation*Batch 0 . . .
Residual 0.006336 0.002240 2.83 0.0023
Fit Statistics
-2 Res Log Likelihood -31.2
AIC (smaller is better) -29.2
AICC (smaller is better) -28.9
BIC (smaller is better) -30.1
Type 3 Tests of Fixed Effects
Num Den
Effect OF DE F Value Pr > F
Presentation 1 16 0.23 0.6395
Least Squares Means
Standard
‘ Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 0.6909 0.02653 16 26.04 <.0001
Presentation Pure 0.7088 0.02653 16 26.71 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure -0.01791 0.03752 16 -0.48
Differences of Least Squares Means
Effect Presentation Presentation Pr > Itl Adjustment Adj P
Presentation FD Pure 0.6395 Tukey 0.6395

 

99

 

A.3.10. Sucrose

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 9
Presentation‘Batch 0 . . .
Residual 0.001404 0.000497 2.83 0.0023
Fit Statistics
-2 Res Log Likelihood -55.3
AIC (smaller is better) -53.3
AICC (smaller is better) -53.0
. BIC (smaller is better) -54.2
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 16 2.62 0.1250
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 0.07812 0.01249 16 6.25 <.0001
Presentation Pure 0.04952 0.01249 16 3.96 0.0011
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure 0.02860 0.01767 16 1.62
Differences of Least Squares Means
6 Effect Presentation Presentation Pr > Itl Adjustment Adj P
Presentation FD Pure 0.1250 Tukey 0.1250

 

100

 

 

A.3.11. Moisture Content

 

 

The Mixed Procedure

Covariance Parameter Estimates

° Standard 2
Cov Parm Estimate Error Value Pr 2
Batch 0 . . .
Presentation*Batch 0.000959 0.005355 0.18 0.4289
Residual 0.01748 0.007135 2.45 0.0072
Fit Statistics
-2 Res Log Likelihood -14.3
AIC (smaller is better) -10.3
AICC (smaller is better) -9.4
BIC (smaller is better) -12.1
Solution for Fixed Effects
Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Intercept 80.3033 0.04756 4 1688.55 <.0001
Presentation FD -77.9811 0.06726 4 -1159.5 <.0001
Presentation Pure 0
Type 3 Tests of Fixed Effects
‘ Num Den
Effect DF DF F Value Pr > F
Presentation 1 4 1344343 <.0001
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > |t|
Presentation FD 2.3222 0.04756 4 48.83 <.0001
Presentation Pure 80.3033 0.04756 4 1688.55 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value Pr > |t|
Presentation FD Pure -77.9811 0.06726 4 -1159.5 <.0001
Differences of Least Squares Means
Effect Presentation Presentation Adjustment Adj P
Presentation FD Pure Tukey <.0001

 

101

 

 

A.3.12. Water Activity (Aw)

 

 

 

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation*Batch 0 . . .
Residual Presentation FD 0.001114 0.000557 2.00 0.0228
Residual Presentation Pure 0.000084 0.000042 2.00 0.0227

Fit Statistics

-2 Res Log Likelihood -79.7
AIC (smaller is better) -75.7
AICC (smaller is better) -74.7
BIC (smaller is better) -77.5

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr > F
Presentation 1 9.2 $321.27 <.0001

Least Squares Means

Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 0.1328 0.01113 8 11.93 <.0001
Presentation Pure 0.9746 0.003055 8 318.97 <.0001

Differences of Least Squares Means

Standard
Effect Presentation Presentation Estimate Error DF t Value

Presentation FD Pure -0.8418 0.01154 9.2 -72.95

Differences of Least Squares Means
Effect Presentation Presentation Pr > Itl Adjustment Adj P

Presentation FD Pure <.0001 Tukey-Kramer <.0001

102

 

A.3.13. Total Phenolics

 

 

The Mixed Procedure

Covariance Parameter Estimates

DF

Standard 2
Cov Parm Group Estimate Error Value
Batch 0.05790 0.2455 0.24
Presentation*Batch 0.2771 0.2839 0.98
Residual Presentation FD 0.02194 0.01258 1.74
Residual Presentation Pure 0.01887 0.01096 1.72
Fit Statistics
-2 Res Log Likelihood 3.1
AIC (smaller is better) 11.1
AICC (smaller is better) 14.7
BIC (smaller is better) 7.5
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Presentation 1 2 1.85 0.3065
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value
Presentation FD 6.7275 0.3378 3.91 19.92
Presentation Pure 7.3197 0.3373 3.89 21.70
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error
Presentation FD Pure -0.5922 0.4350

Differences of Least Squares Means

Effect Presentation Presentation Pr > Itl Adjustment

Presentation FD Pure 0.3065 Tukey-Kramer

Pr Z
0.4068
0.1646

0.0406
0.0426

Pr > |t|

<.0001
<.0001

t Value

-1.36

Adj P

0.3065

 

 

103

 

A.3.14. ORAC value

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation*Batch 0 . . .
Residual Presentation FD 96.1967 48.0983 2.00 0.0228
Residual Presentation Pure 212.32 106.16 2.00 0.0228

Fit Statistics

-2 Res Log Likelihood 129.2
AIC (smaller is better) 133.2
AICC (smaller is better) 134.1
BIC (smaller is better) 131.4
. Type 3 Tests of Fixed Effects
Num Den
Effect OF DE F Value Pr > F
Presentation 1 14 50.92 <.0001

Least Squares Means

Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 102.36 3.2693 8 31.31 <.0001
Presentation Pure 144.14 4.8571 8 29.68 <.0001

Differences of Least Squares Means

Standard
Effect Presentation Presentation Estimate Error DF t Value

Presentation FD Pure -41.7796 5.8549 14 -7.14

Differences of Least Squares Means
Effect Presentation Presentation Pr > Itl Adjustment Adj P

‘ Presentation FD Pure <.0001 Tukey-Kramer <.0001

 

104

 

 

A.3.15. Lycopene

 

 

The Mixed Procedure

Covariance Parameter Estimates

Standard 2
Cov Parm Group Estimate Error Value Pr 2
Batch 0
Presentation‘Batch 0 . . .
Residual Presentation FD 0.000178 0.000089 2.00 0.0228
Residual Presentation Pure 0.01657 0.008284 2.00 0.0228
Fit Statistics
-2 Res Log Likelihood -52.1
AIC (smaller is better) -48.1
AICC (smaller is better) -47.2
BIC (smaller is better) -49.9
Type 3 Tests of Fixed Effects
Num Den
Effect OF DE F Value Pr > F
Presentation 1 8.17 2590.61 <.0001
Least Squares Means
Standard
Effect Presentation Estimate Error DF t Value Pr > Itl
Presentation FD 0.7013 0.004443 8 157.83 <.0001
Presentation Pure 2.8967 0.04290 8 67.51 <.0001
Differences of Least Squares Means
Standard
Effect Presentation Presentation Estimate Error DF t Value
Presentation FD Pure -2.1954 0.04313 8.17 -50.90
Differences of Least Squares Means
Effect Presentation Presentation Pr > It] Adjustment Adj P
Presentation FD Pure <.0001 Tukey-Kramer <.0001

 

105

 

 

6

Appendix 4. Consumer Testing Consent Form

 

 

Consent Form - Consumer Panel Acceptance Test
Evaluation of Bread containing Autumnberry

Dear Participant:

Michigan State University graduate student is investigating consumer
perceptions of autumn berry-containing bread. We would like you to take about
15 minutes (including the time you spend reading this letter) to help us evaluate
autumnberry bread samples. We are asking for volunteers, 18 years or older,
who consume bread in regular basis. If you have a known food allergy to the
ingredients of the bread: wheat flour, autumn olive berry pureed, sugar, yeast,
partially hydrogenated shortening, and salt, please do not volunteer for this
study.

If you meet the above requirements, we would like you to look at and taste the
bread samples. You will be given 5 samples to look at, smell, taste, and answer
questions related to the product quality. If you agree to taste these and provide
your evaluation based on the survey questionnaire, please sign the consent form
below. You will be given an ice cream cup for your evaluation and completion of
the survey.

If you believe there is a potential of an allergic reaction upon sniffing and tasting,
notify the on-site sensory evaluation coordinator and/or principle investigator
immediately. You will be released from participating in this study. Please note if
you are injured as a result of your participation in this research project, Michigan
State University will assist you in obtaining emergency care, if necessary, for
your research related injuries. If you have insurance for medical care, your
insurance carrier will be billed in the ordinary manner. As with any medical
insurance, any costs that are not covered or in excess of whatever are paid by
your insurance, including deductibles, will be your responsibility. Financial
compensation for lost wages; disability, pain or discomfort is not available. This
does not mean that you are giving up any legal rights you may have.

Your responses are collected anonymously. We have no way to connect you, as
an individual, to the completed survey form. You are free to not answer any
question you choose, but please try to answer every question. We are not able
to use incomplete responses nor are we able to provide the incentive for
incomplete responses.

If you have any questions during your reading this consent form, or during or
after your participation, please do not hesitate to contact the on-site sensory
evaluation leader and/or the principle investigator. Feel free to contact Dr. Janice

 

 

106

J‘

 

 

Appendix 4. Continued

Harte, the principle investigator, via phone at 517 355 8474 x105 or
harteia@rrfl1.edu for any inquiry you might have due to your participation in our
study.

PLEASE NOTE UPON YOUR SIGNING THIS CONSENT FORM, YOU
VOLUNTARILY AGREE TO PARTICIPATE IN OUR STUDY. YOUR
SIGNATURE INDICATES YOU HAVE READ THE INFORMATION PROVIDED
ABOVE AND THAT YOU HAVE HAD AN ADEQUATE OPPORTUNITY TO
DISCUSS THIS STUDY WITH THE PRINCIPLE INVESTIGATOR AND HAVE
HAD ALL YOUR QUESTIONS ANSWERED TO YOUR SATISFACTION. YOU
WILL BE GIVEN A COPY OF THIS CONSENT FORM WITH YOUR
SIGNATURE FOR YOUR RECORDS UPON YOUR REQUEST.

SIGNED DATE

 

 

107

 

 

Appendix 5. Trained Sensory Evaluation Consent Forms

 

 

 

Trained Panel Consent Form
Evaluations of Freeze-dried Autumnberry Fortified Bread
Samples: Yeast-raised bread fortified with freeze-dried autumnberry
(AACC Method 10-10)

Before you decide to sign this consent form and continue to participate in our
study, please read carefully and thoroughly the reverse side of this form for the
sample ingredients and preparation information, purpose and procedure of this
study, potential risks and benefits from your participation, our assurance of your
privacy, your rights as a human subject in our study, etc.

We are asking that panelists participate in the physicochemical and sensory
properties evaluation of autumnberry bread that will be conducted for 8-10 weeks
period. Training will consist of approximately 8—10 sessions of 30 - 45 minutes.
After training, evaluations will be scheduled for 3 times over a 2-week period.
Evaluations should last about 15-30 minutes. It is important for this research that
we have the same panelists participate for each evaluation when ever possible.
We will make every effort to accommodate your schedule and time needs.
However, if you cannot attend any evaluation please inform the researchers
when contacted each month. Your signing this consent form will indicate your
agreeing to participate when possible.

If you have any question during your reading this consent form, or during or after
your participation, please do not hesitate to contact the on-site sensory
evaluation leader and/or the principle investigator. Feel free to contact Dr. Janice
Harte, the principle investigator of this study, via phone at 517-355-8474, ext.
105 or write her at 114 Trout Food Science and Human Nutrition Building,
Michigan State University, East Lansing, MI 48823. You also can reach us via
email at harteia@msu.edu for any inquiry you might have due to your
participation in our study. ‘

If you have read all the information we offer to you in this consent form and
decide to participate in our study and give us your valuable response to our
questionnaire, you can go ahead and sign this form now. Otherwise, you can
stop here and feel free to discontinue participation in our study without any

penalty.

PLEASE NOTE UPON YOUR SIGNING THIS CONSENT FORM, YOU
VOLUNTARILY AGREE TO PARTICIPATE IN OUR STUDY. YOUR
SIGNATURE INDICATES YOU HAVE READ THE INFORMATION PROVIDED
ABOVE AND THAT YOU HAVE HAD AN ADEQUATE OPPORTUNITY TO
DISCUSS THIS STUDY WITH THE PRINCIPLE INVESTIGATOR AND HAVE

 

108

 

 

 

Appgndix 5. Continued

HAD ALL YOUR QUESTIONS ANSWERED TO YOUR SATISFACTION. YOU
WILL BE GIVEN A COPY OF THIS CONSENT FORM WITH YOUR
SIGNATURE FOR YOUR RECORDS UPON YOUR REQUEST.

SIGNED: DATE:

6

 

 

 

 

 

 

TRAINED PANEL CONSENT FORM
Participant Copy
Evaluations of Yeast-raised Bread Fortified with Freeze-dried Autumnberry
(AACC Method 10-10 for bread-making)

Invitation to participate: You are invited to participate in the study that
assesses some physicochemical and sensory properties of yeast-raised bread
fortified with freeze-dried autumnberry.

Purpose of the study: We are investigating the effect of the fortification of
freeze-dried autumnberry at different levels on in yeast-raised bread in terms of
crust and crumb color, firmness, cell uniformity, and the intensity of the yeast
and/or autumnberry flavor.

Procedure of the study: Each panelist would be served different variations of
slice of bread at room temperature condition. Each sample will be coded with a
random 3-digit code. We are asking that panelists participate in this study at
which last for 8-10 weeks period. Training will consists of approximately 8-10
sessions of 30-45 minutes. Instructions to the test would be provided on a given
sheet. Participants will be asked to rate the samples based on Universal Scale in
which consists of 15 points spectrum scale on color and texture attributes.

Samples preparation: Breads were prepared in MSU Baking Lab/Cereal Lab
124 G of Trout FSHN Building using AACC approved baking equipment.

Potential risks: The breads are consisted of bread flour (wheat gluten), freeze-
dried autumnberry, salt, sugar, vegetable shortening, bread machine yeast,
ascorbic acid, water. If you have any known allergic reaction(s) to these listed
ingredients, please do not participate in this trained panel. These cookies pose
no adverse health risk. Though none is anticipated, if you have a problem upon
consuming these samples, please notify the on-site sensory evaluation

 

109

 

 

 

 

Appendix 5. Continued

coordinator and/or principle investigator immediately. You will be released from
participating in this study. Please note if you are injured as a result of your
participation in this research project, Michigan State University will assist you in
obtaining emergency care, if necessary, for your research related injuries. If you
have insurance for medical care, your insurance carrier will be billed in the
ordinary manner. As with any medical insurance, any costs that are not covered
or in excess of whatever are paid by your insurance, including deductibles, will
be your responsibility. Financial compensation for lost wages; disability, pain or
discomfort is not available. This does not mean that you are giving up any legal
rights you may have. Your response is confidential and we will protect your
confidentiality to the full extent of the law.

Expected benefits: This study will enable the researchers to establish the
relationship between sensory evaluation and experimental (mechanical) data on
physicochemical properties of autumnberry fortified breads.

Assurance of confidentiality: Any information obtained in connection with this
study that could be identified with you will be kept confidential by ensuring that all
consent forms and response sheets are securely stored. All data collected and
analyzed will be reported in an aggregate format that will not permit associating
subjects with specifics response or findings. Your privacy will be protected to
the maximum extent allowable by law.

Withdrawal from the study: Participation in this study is in voluntary basis. You
may refuse to grade any of the cookies without penalty, and your decision to
refuse participation or discontinue participation during this study will be fulfilled
promptly and unconditionally.

 

110

 

 

 

Appendix 6. Descriptive Analysis Questionnaire

 

 

Descriptive Analysis of Autumnberry Bread
Trained Panel Test Session #

 

 

 

 

 

 

 

 

 

 

 

 

Name: Date:

1) Crust Color:

I . I I I
I . I I I
0 ‘ 5 10 15
light dark
Comments:

2) Crumb Color:

I I . I I
I I I I
0 5 1O 15
light dark
Comments:

3) Firmness:

I ' I I A
I . I I I
0 5 10 15
soft hard
Comments:

4) Crumb Structure Uniformity:

I I I J
I I I I
0 5 1O 15
less uniform more uniform

 

111

 

 

 

 

Appendix 6. Continued

Comments:

 

 

5) Yeasty Flavor:

0
weak

Comments:

10

1 5
strong

 

 

6) Autumnberry Flavor:

I
I

0
weak

6

Comments:

1 5
strong

 

 

 

112

 

 

 

Table A7. Bread Attributes References for Sensory Trained Panel

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ICh arggeagsti cs Food Products Score
Sunbeam whole grain white bread 1
Firmn e s s Stone-ground 100% whole wheat Pepperldge
_ Farm bread 8
'Mini Bagel pre-sliced Pepperidge Farm 15
Great Value bread mix without added yeast
Yeast Flavor suspension 1
Great Value bread mix with added yeast (doubled) 8
‘ Great Value bread mix with added yeast (tripled) 15
Distilled water 0
Autumnbeny Autumnberry juice 5% 5
EM Autumnberry juice 10% 1O
Autumnberry juice 15% 15

 

 

113

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

10

 

 

 

 

Figure A.7.1. References for Bread Crumb Cell Uniformi

Table A.8. Raw Data for Descriptive Analysis Testing

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2:2: .222. 22.”: “2222“”
niformrty
0% 1 1 4 1 5 1o 6 o
0% 2 1 7 1 5 3 10 0
0% 3 1 7 0 12 1 5 0
0% 4 1 8 5 4 5 10 o
0% 5 1 6 1 8 7 10 0
0% 6 1 2 2 6 1 7 1
0% 7 1 7 1 7 O 9 0
0% 8 1 2 9 4 2 3 0
0% 9 1 6 0 3 3 6 0
0% 10 1 8 3 9 3 5 0
0% 11 1 2 2 11 7 6 0
0% 12 1 7 3 9 7 4 0
0% 1 2 5 2 3 4 9 0
0% 2 2 8 1 5 3 13 0
0% 3 2 8 o 5 1 5 o
0% 4 2 7 7 6 6 4 0
0% 5 2 5 2 2 1o 10 0
0% 5 2 7 3 4 6 2 0
0% 7 2 8 1 1o 6 9 0
0% 8 2 7 3 7 3 7 0
0% 9 2 5 1 5 4 9 1
0% 1o 2 8 3 6 2 9 0
0% 11 2 3 3 6 7 5 4
0% 12 2 7 3 5 3 4 0
0% 1 3 6 1 3 4 11 o
0% 2 3 4 1 6 3 11 0
0% 3 3 8 0 9 1 5 0
0% 4 3 7 5 7 3 2 0
0% 5 3 7 1 3 1o 12 0
0% 6 3 7 2 3 1 7 0
0% 7 3 7 2 7 4 12 0
0% 8 3 5 2 9 2 9 0
0% 9 3 5 1 4 2 4 0
0% 1o 3 7 1 6 2 7 0
0% 11 3 2 3 1o 5 3 1

 

 

 

 

 

 

 

 

 

 

115

 

 

Table A.8. Continued

1O

1O
1O

 

10

11

10

 

10

12

 

12

1O

11
11

1O

1O

 

1O

10
1O

 

10

1O

1O

13
1O
10

10

1O

 

 

12

1

2
3
4

5
6
7
8
9

1O

11

12
1

2
3
4

5
6
7
8
9

1O

11

12

1

2
3
4

5
6

7
8
9

10

11

12

1

2
3

 

0%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
3%
‘ 3%

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3%
3%
3%
3%
3%
3%
6%
6%
6%

 

 

 

 

 

 

 

 

 

 

116

Table A8. Continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-I
I
u 1“:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6% 4 1 9 10 9 5 7 10
6% 5 1 7 7 5 5 5 6
6% 6 1 12 12 7 5 5 5
6% 7 1 13 11 12 3 3 8
6% 8 1 10 9 10 8 5 6
6% 9 1 12 10 4 6 5 9
6% 10 1 8 1o 6 5 7 5
6% 11 1 13 10 12 1o 9 11
6% 12 1 10 9 8 8 7 8
6% 1 2 8 9 5 8 2 4
6% 2 2 9 9 8 5 5 9
6% 3 2 9 9 1o 7 3 7
6% 4 2 9 10 5 5 10 12
6% 5 2 12 11 7 12 1o 11
6% 6 2 11 9 10 5 5 7
6% 7 2 10 10 3 4 2 7
6% 8 2 9 8 7 3 5 5
6% 9 2 14 9 14 3 2 12
6% 1o 2 9 10 7 5 5 4
6% 11 2 1o 9 5 9 7 8
6% 12 2 11 1o 6 1o 6 8
6% 1 3 1o 8 5 5 1 10
6% 2 3 10 9 6 7 6 9
6% 3 3 9 10 7 7 4 7
6% 4 3 8 11 6 8 9 9
6% 5 3 1o 6 5 9 1o 10
6% 6 3 9 9 7 3 7 7
6% 7 3 11 10 4 4 4 6
6% 8 3 10 9 5 6 6 8
6% 9 3 13 9 14 5 5 7
6% 1o 3 9 1o 10 6 6 7
6% 11 3 1o 10 12 5 5 7
6% 12 3 12 10 6 10 4 5
9% 1 1 10 10 5 5 1 13
9% 2 1 11 12 8 9 1 14
9% 3 1 10 12 10 8 6 10
9% 4 1 9 11 5 3 9 9
9% 5 1 11 10 5 6 2 10
9% 6 1 12 12 9 10 8 10
9% 7 1 14 13 5 7 0 11

 

 

 

Table A.8. Continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

118

9% 8 1 11 11 8 4 5 8
9% 9 1 13 14 9 10 8 13
9% 10 1 10 10 7 6 5 1O
9% 11 1 13 13 12 5 6 11
9% 12 1 12 11 8 9 8 13
9% 1 2 10 10 5 5 1 13
9% 2 2 10 1O 10 6 4 13
9% 3 2 1O 1O 15 8 3 12
9% 4 2 10 12 7 5 8 13
9% 5 2 14 13 3 13 9 13
9% 6 2 1O 12 7 8 5 8
9% 7 2 11 13 1O 5 5 11
9% 8 2 11 9 9 5 9 8
9% 9 2 14 13 8 2 5 14
9% 1O 2 1O 11 8 7 5 7
9% 11 2 12 12 8 7 6 11
9% 12 2 13 12 5 11 3 13
9% 1 3 13 11 9 7 O 14
9% 2 3 11 11 14 1O 1 15
9% 3 3 9 12 11 A 9 4 1O
9% 4 3 8 12 6 8 8 11
9% 5 3 11 12 5 13 5 14
9% 6 3 8 11 4 8 7 1O
9% 7 3 11 13 13 9 1 12
9% 8 3 11 11 6 9 5 11
9% 9 3 14 14 12 6 8 13
9% 10 3 11 11 5 4 6 9
9% 11 3 1O 13 11 10 11 1O
9% 12 3 11 12 9 14 2 13

 

 

Appendix 9. SAS Output for Sensory Analysis

 

 

 

A.9.1. Crust Color
Class Level Information
6
Class Levels Values
Run 3 1 2 3
Panelist 12 1 2 3 4 S 6 7 8 9 10 11 12
Trt 4 0 0.03 0.06 0.09
Number of Observations
Number of Observations Read 144
Number of Observations Used 144
Number of Observations Not Used 0
Covariance Parameter Estimates
Cov Parm Group Estimate
Panelist 0.2570
Panelist*Trt 0.8463
Residual Trt 0 2.2190
Residual Trt 0.03 3.2221
Residual Trt 0.06 1.5183
Residual Trt 0.09 1.5348
Fit Statistics
-2 Res Log Likelihood 552.1
AIC (smaller is better) 564.1
1 AICC (smaller is better) 564.7
BIC (smaller is better) 567.0
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Trt 3 27.9 40.47 <.0001
Least Squares Means
Standard
Effect Trt Estimate Error DF t Value Pr > Itl
Trt 0 5.9722 0.3919 40.3 15.24 <.0001
Trt 0.03 8.3333 0.4260 39.2 19.56 <.0001
Trt 0.06 10.1111 0.3662 30.6 27.61 <.0001
Trt 0.09 11.0833 0.3668 30.5 30.21 <.0001
Differences of Least Squares Means
Standard
Effect Trt Trt Estimate Error DF t Value Pr > Itl Adjustment Adj P
Trt 0 0.03 -2.3611 0.5405 36.5 -4.37 <.0001 Tukey-Kramer 0.0009
Trt 0 0.06 -4.1389 0.4948 29.9 -8.36 <.0001 Tukey-Kramer <.0001
Trt 0 0.09 -5.1111 0.4953 29.9 -10.32 <.0001 Tukey-Kramer <.0001

 

 

119

 

 

A.9.1. Continued

 

 

 

Trt 0.03 0.06 -1.7778 0.5222 27.2 -3.40 0.0021 Tukey-Kramer 0.0103
Trt 0.03 0.09 ~2.7500 0.5227 27.3 -5.26 <.0001 Tukey-Kramer <.0001
Trt 0.06 0.09 -0.9722 0.4752 21.4 -2.05 0.0533 Tukey-Kramer 0.1959
Coefficients for
Linear on Trt
Effect Trt Row1
Intercept
Trt 0 -3
Trt 0.03 -1
Trt 0.06 1
Trt 0.09 3
Contrasts
Num Den
Label DF DF F Value Pr > F
‘ Linear on Trt 1 30.4 118.03 <.0001
Quadratic on Trt 1 30.3 3.72 0.0631
Cubic on Trt 1 28 0.02 0.8934

 

 

120

 

A.9.2. Crumb Color

 

Class Level Information
Class Levels Values
Run 3

Panelist 12
Trt 4

c>hshs
6:633:
c>uaua

4 S 6 7 8
3 6. 6.09

Dimensions
Covariance Parameters
Columns in X
Columns in 2
Subjects
Max Obs Per Subject
Number of Observations
Number of Observations Read
Number of Observations Used
Number of Observations Not Used

The Mixed Procedure

Iteration History

66

144

Iteration Evaluations -2 Res Log Like

0 1 568.42724685
1 1 482.24237824

Convergence criteria met.

Covariance Parameter
Estimates

Cov Parm Estimate
Panelist 0.06075
Panelist*Trt 0.8709
Residual 1.1181
Fit Statistics
-2 Res Log Likelihood
AIC (smaller is better)

AICC (smaller is better)
BIC (smaller is better)

Solution for Fixed Effects

Standard
Effect Trt Estimate Error DF
Intercept 11.6389 0.3297 43.7
Trt 0 -9.4722 0.4553 33
Trt 0.03 -4.3611 0.4553 33

482.2
488.2
488.4
489.7

t

9 10 11 12

144
144

Criterion

6.00066600

Value

35.36
-20.81
-9.58

Pr > |t|

(.6061
(.6061
(.0661

 

121

 

 

A.9.2. Continued

 

 

Trt 0.06 -2.1667 0.4553 33 -4.76 <.0001
Trt 0.09 0
Contrasts
Num Den
Label DF DF F Value Pr > F
Linear on Trt 1 33 452.11 (.0001
Quadratic on Trt 1 33 20.92 <.0001
Cubic on Trt 1 33 4.03 0.0530
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Trt 3 33 159.02 <.0001
Least Squares Means
‘ Standard
Effect Trt Estimate Error DF t Value Pr > Itl
Trt 0 2.1667 0.3297 43.7 6.57 <.0001
Trt 0.03 7.2778 0.3297 43.7 22.08 <.0001
Trt 0.06 9.4722 0.3297 43.7 28.73 <.0001
Trt 0.09 11.6389 0.3297 43.7 35.30 <.0001
Differences of Least Squares Means
Standard
Effect Trt Trt Estimate Error DF t Value Pr > Itl Adjustment Adj P
Trt 0 0.03 -5.1111 0.4553 33 -11.23 <.0001 Tukey-Kramer <.0001
Trt 0 0.06 -7.3056 0.4553 33 -16.05 <.0001 Tukey-Kramer <.0001
Trt 0 0.09 -9.4722 0.4553 33 -20.81 <.0001 Tukey-Kramer <.0001
Trt 0.03 0.06 -2.1944 0.4553 33 -4.82 <.0001 Tukey-Kramer 0.0002
Trt 0.03 0.09 -4.3611 0.4553 33 -9.58 <.0001 Tukey-Kramer <.0001
Trt 0.06 0.09 -2.1667 0.4553 33 -4.76 <.0001 Tukey-Kramer 0.0002

 

122

 

 

 

 

A.9.3. Crumb Cell Uniformity

 

Class

Run

Trt

1 2
Panelist 12 1 2
0 0

Class Level Information

Levels Values

3
9
4

Dimensions

Covariance Parameters
Columns in X

Columns in 2

Subjects

Max Obs Per Subject

Number of Observations

Number of Observations Read
Number of Observations Used
Number of Observations Not Used

Iteration

Effect

Intercept
Trt
Trt
Trt
Trt

0
1

Trt

The Mixed Procedure
Iteration History

Evaluations

-2 Res Log Like

10 11 12

60

144

144
144

Criterion

1 687.61026712

2 669.09489588

Convergence criteria met.

Covariance Parameter
Estimates

Cov Parm Estimate
Panelist 1.7038

Panelist*Trt 0
Residual 5.5729

Fit Statistics
-2 Res Log Likelihood
AIC (smaller is better)
AICC (smaller is better)
BIC (smaller is better)

Solution for Fixed Effects

Standard
Estimate Error DF

8.0833
-2.6000
-1.3333
-0.6389

0

0.5448
6.5564
0.5564
6.5564

28.7
129
129
129

0.00000060

669.1
673.1
673.2
674.1

t Value Pr > Itl

14.84
-3.59
-2.40
-1.15

<.0001
6.0605
6.0180
6.2530

 

 

123

IL_

 

A.9.;3. Continued

 

 

The Mixed Procedure

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr > F
Trt 3 129 4.83 0.0032
Coefficients for
Cubic on Trt
Effect Trt Row1
Trt 0.09 1
Contrasts
Num Den
Label DF DF F Value Pr > F
Linear on Trt 1 129 14.48 0.0002
‘ Quadratic on Trt 1 129 0.00 0.9719
Cubic on Trt 1 129 0.00 0.9623
Least Squares Means
Standard
Effect Trt Estimate Error DF t Value Pr > Itl
Trt 0 6.0833 0.5448 28.7 11.17 <.0001
Trt 0.03 6.7500 0.5448 28.7 12.39 <.0001
Trt 0.06 7.4444 0.5448 28.7 13.67 <.0001
Trt 0.09 8.0833 0.5448 28.7 14.84 <.0001
Differences of Least Squares Means
Standard
Effect Trt Trt Estimate Error DF t Value Pr > Itl Adjustment
Trt 0 0.03 -0.6667 0.5564 129 -1.20 0.2331 Tukey-Kramer
Trt 0 0.06 -1.3611 0.5564 129 -2.45 0.0158 Tukey-Kramer
Trt 0 0.09 -2.0000 0.5564 129 -3.59 0.0005 Tukey-Kramer
Trt 0.03 0.06 -0.6944 0.5564 129 -1.25 0.2143 Tukey-Kramer
Trt 0.03 0.09 -1.3333 0.5564 129 -2.40 0.0180 Tukey-Kramer
Trt 0.06 0.09 -0.6389 0.5564 129 -1.15 0.2530 Tukey-Kramer

Adj P

0.6291
0.6736
0.6026
0.5976
6.0829
0.6664

 

124

 

 

 

A.9.4. Firmness

 

 

Class Level Information

Class Levels Values

Run 3
Panelist 12
Trt 4

4 5 6 7 8
3 0.66 6.69

GDFJDJ
QNN
0WD)

0

Dimensions

Covariance Parameters
Columns in X

Columns in 2

Subjects

Max Obs Per Subject

Number of Observations
Number of Observations Read
Number of Observations Used
Number of Observations Not Used
The Mixed Procedure

Iteration History

9 10 11 12

60

144

144
144

Iteration Evaluations -2 Res Log Like Criterion

Effect

Intercept
Trt
Trt

6 1 672.12490773
1 1 639.72197978 0.06660066

Convergence criteria met.

Covariance Parameter
Estimates

Cov Parm Estimate
Panelist 2.0272
Panelist*Trt 0.4961
Residual 4.0486
Fit Statistics
-2 Res Log Likelihood
AIC (smaller is better)

AICC (smaller is better)
BIC (smaller is better)

Solution for Fixed Effects

Standard
Trt Estimate Error OF
7.5278 0.5681 24.1
0 -3.5556 0.5546 33
0.03 -2.6667 0.5546 33

639.7
645.7
645.9
647.2

t Value Pr > |t|

13.25 <.0001
-6.41 <.0001
-4.81 <.0001\

 

125

 

 

 

A.9.4. Continued

 

 

Trt 0.06 -1.2222 0.5546 33 -2.20 0.0346
Trt 0.09 0
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Trt 3 33 16.02 <.0001
Coefficients for
Cubic on Trt
Effect Trt Row1
Trt 0.09 1
Contrasts
Num Den
Label DF DF F Value Pr > F
Linear on Trt 1 33 47.68 <.0001
Quadratic on Trt 1 33 0.18 0.6736
Cubic on Trt 1 33 0.20 0.6603
‘ Least Squares Means
Standard
Effect Trt Estimate Error DF t Value Pr > Itl
Trt 0 3.9722 0.5681 24.1 6.99 <.0001
Trt 0.03 4.8611 0.5681 24.1 8.56 <.0001
Trt 0.06 6.3056 0.5681 24.1 11.10 <.0001
Trt 0.09 7.5278 0.5681 24.1 13.25 <.0001
Differences of Least Squares Means
Standard
Effect Trt Trt Estimate Error DF t Value Pr > |t| Adjustment Adj P
Trt 0 0.03 -0.8889 0.5546 33 -1.60 0.1185 Tukey-Kramer 0.3911
Trt 0 0.06 -2.3333 0.5546 33 -4.21 0.0002 Tukey-Kramer 0.0010
Trt 0 0.09 -3.5556 0.5546 33 -6.41 <.0001 Tukey-Kramer <.0001
Trt 0.03 0.06 -1.4444 0.5546 33 -2.60 0.0137 Tukey-Kramer 0.0626
Trt 0.03 0.09 -2.6667 0.5546 33 -4.81 <.0001 Tukey-Kramer 0.0002
Trt 0.06 0.09 -1.2222 0.5546 33 -2.20 0.0346 Tukey-Kramer 0.1433

 

126

 

A.9.5. Yeasty Flavor

 

 

Effect

Intercept
Trt
Trt
Trt
Trt

Class

Run
Panelist
Trt

Class Level Information

Levels Values

3 1 2
12 1 2
4 6 0

Dimensions

Covariance Parameters
Columns in X

Columns in 2

Subjects

Max Obs Per Subject

Number of Observations

Number of Observations Read
Number of Observations Used
Number of Observations Not Used

Iteration

0
1

Iteration History

Convergence criteria met.

Covariance Parameter

Estimates
Cov Parm Estimate
Panelist 0.6538
Panelist*Trt 2.6863
Residual 4.2778

Fit Statistics

-2 Res Log Likelihood

AIC (smaller is better)
AICC (smaller is better)
BIC (smaller is better)

Trt

Solution for Fixed Effects

9 16 11 12

60

144

144
144

Evaluations -2 Res Log Like Criterion
1 692.35364706
1 667.13419336 0.00000000

667.1
673.1
673.3
674.6

Standard
Estimate Error DF t Value
5.0000 0.6302 41.6 7.93
2.0556 0.8279 33 2.48
0.9444 0.8279 33 1.14
0.3333 0.8279 33 0.40
0

Pr > Itl

<.0001
0.0183
0.2622
0.6898

 

127

 

A.9.5. Continued

 

 

 

The Mixed Procedure

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr > F
Trt 3 33 2.38 0.0870
Contrasts
Num Den
Label DF DF F Value Pr > F
Linear on Trt 1 33 6.70 0.0142
Quadratic on Trt 1 33 0.44 0.5111
Cubic on Trt 1 33 0.01 0.9329
Least Squares Means
Standard
Effect Trt Estimate Error DF t Value Pr > |t|
Trt 0 7.0556 0.6302 41.6 11.20 <.0001
Trt 0.03 5.9444 0.6302 41.6 9.43 <.0001
Trt 0.06 5.3333 0.6302 41.6 8.46 <.0001
. Trt 0.09 5.0000 0.6302 41.6 7 93 <.0001
Differences of Least Squares Means
Standard
Effect Trt Trt Estimate Error DF t Value Pr > Itl Adjustment Adj P
Trt 0 0.03 1.1111 0.8279 33 1.34 0.1887 Tukey-Kramer 0.5435
Trt 0 0.06 1.7222 0.8279 33 2.08 0.0453 Tukey-Kramer 0.1806
Trt 0 0 09 2.0556 0.8279 33 2.48 0.0183 Tukey-Kramer 0.0814
Trt 0.03 0.06 0.6111 0.8279 33 0.74 0.4656 Tukey-Kramer 0.8810
Trt 0.03 0.09 0.9444 0.8279 33 1.14 0.2622 Tukey-Kramer 0.6674
Trt 0.06 0.09 0.3333 0.8279 33 0.40 0.6898 Tukey-Kramer 0.9776

 

128

 

A.9.6. Autumnberry Flavor

 

 

Class Level Information

Class Levels Values

Run 3 1 2 3

Panelist 12 1 2 3 4 S 6 7 8 9

Trt 4 0 0.03 0.06 0.09
Dimensions

Covariance Parameters
Columns in X

Columns in 2

Subjects

Max Obs Per Subject

Number of Observations
Number of Observations Read

Number of Observations Used
Number of Observations Not Used

The Mixed Procedure
Iteration History
Evaluations

Iteration -2 Res Log Like

0
1

1
1

572.26438983
548.24935278

Convergence criteria met.

Covariance Parameter
Estimates

Cov Parm Estimate
0.6094

0.6395
1.9722

Panelist
Panelist‘Trt
Residual

Fit Statistics

-2 Res Log Likelihood
AIC (smaller is better)
AICC (smaller is better)
BIC (smaller is better)

Solution for Fixed Effects

Standard

Effect Trt Estimate Error DF

11.3889
-11.1944

0.3986
0.4649

33.7
33

Intercept
Trt

10 11 12

144
144

Criterion

0.00000000

548.2
554.2
554.4
555.7

't Value Pr > Itl

28.57
-24.08

<.0001
<.0001

 

129

 

 

 

A.9.6. Continued

 

 

 

Effect

Trt
Trt
Trt
Trt
Trt
Trt

Trt
Trt
Trt

Effect

Trt
Trt
Trt
Trt

Trt

¢,o>o>
&,c>o>
c‘u3u16>6>d>

0.03 -7.7778 0.4649 33 -16.73 <.0001
0.06 -3.6111 0.4649 33 -7.77 (.0001
0.09 0
The Mixed Procedure
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF F Value Pr > F
Trt 3 33 220.05 <.0001
The Mixed Procedure
Coefficients for
Cubic on Trt
Effect Trt Rowl
Trt 0.09 1
Contrasts
Num Den
Label DF DF F Value Pr > F
Linear on Trt 1 33 659.28 <.0001
Quadratic on Trt 1 33 0.09 0.7693
Cubic on Trt 1 33 0.79 0.3810
Least Squares Means
Standard
Trt Estimate Error DF t Value Pr > Itl
0 0.1944 0.3986 33.7 0.49 0.6288
0.03 3.6111 0.3986 33.7 9.06 <.0001
0.06 7.7778 0.3986 33.7 19.51 <.0001
0.09 11.3889 0.3986 33.7 28.57 <.0001
Differences of Least Squares Means
Standard
Trt Estimate Error DF t Value Pr > Itl Adjustment
0.03 -3.4167 0.4649 33 -7.35 <.0001 Tukey-Kramer
0.06 -7.5833 0.4649 33 -16.31 <.0001 Tukey-Kramer
0.09 -11.1944 0.4649 33 -24.08 <.0001 Tukey-Kramer
0.06 -4.1667 0.4649 33 -8.96 <.0001 Tukey-Kramer
0.09 —7.7778 0.4649 33 -16.73 <.0001 Tukey-Kramer
0.09 -3.6111 0.4649 33 -7.77 <.0001 Tukey-Kramer

Adj P

.0001
.0001
.0001
.0001
.0001

AAAAA

 

130

 

 

 

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