physicochemical and sensory properties of …

PHYSICOCHEMICAL AND SENSORY PROPERTIES OF

SOYMILK FROM FIVE SOYBEAN LINES

_______________________________________

A Thesis

presented to

the Faculty of the Graduate School

at the University of Missouri-Columbia

_______________________________________________________

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

_____________________________________________________

by

JING TANG

Dr. Fu-hung Hsieh, Thesis Supervisor

DECMEBER 2013

The undersigned, appointed by the dean of the Graduate School, have examined the

thesis entitled



presented by Jing Tang,

a candidate for the degree of or Master of Science,

and hereby certify that, in their opinion, it is worthy of acceptance.

Dr. Fu-hung Hsieh, Food Science

Dr. Ingolf Gruen, Food Science

Dr. Mengshi Lin, Food Science

Dr. Mark Ellersieck, Statistics

ii

ACKNOWLEDGEMENTS

I would like to give my sincere thank to Dr. Fu-hung Hsieh for being my advisor.

Especially for his patience guiding me through my research problems, he has always been

a good mentor. He always opens my mind for any kind of research conversation, and

always being a good listener. Thank you Dr. Hsieh. Without your warm heart and support,

I don’t think I can make it here.

I am also grateful to my committee members, Dr. Ingolf Gruen, Dr. Mengshi Lin,

and Dr. Mark Ellersieck, for their guidance on many research problems and giving me

very valuable comments.

I would like to express my thanks to Dr. Kristin Bilyeu and her lab members for

their kindness to provide soybeans and details of soybeans to support my research.

I want to say thank you to Harold Huff, Lakdas Fernando, and Rick Linhardt for

their patience and kindness to help me working in the laboratory and solving the

problems of making soy ice creams.

I would like to express my thanks to Linda Little and JoAnn Lewis. Thank you for

your patience and always giving me correct answers for all kinds of questions.

I am very thankful to the valuable friendships with all my friends. Thank you for

sharing your life with me and encouraging me when I had problems.

iii

Last but not least, I would like to express my thanks to my parents and my

grandparents. I could not finish my degree without your support and love. Your loves

always make me have a faith in me. I love you.

iv

TABLE OF CONTENT

ACKNOWLEDGEMENTS ................................................................................................ ii

LIST OF TABLES ............................................................................................................ vii

LIST OF FIGURES ......................................................................................................... viii

ABSTRACT ....................................................................................................................... ix

CHAPTER

1. INTRODUCTION .......................................................................................................... 1

1.1 Background .................................................................................................................. 1

1.2 Objectives .................................................................................................................... 3

2. LITERATURE REVIEW ............................................................................................... 4

2.1 Soy Products ................................................................................................................. 4

2.2 Soy Foods and Health Promotion ................................................................................. 5

2.2.1 Soy Foods and Coronary Heart Disease .............................................................. 6

2.2.2 Soy Foods and Cancer ......................................................................................... 7

2.2.3 Soy Foods and Women’s Health ......................................................................... 9

2.2.3.1 Soy Foods and Menopausal Symptoms ..................................................... 9

2.2.3.2 Soy Foods and Calcium Binding Properties ............................................ 10

2.2.4 Soy Foods and Chronic Kidney Disease ........................................................... 11

2.2.5 Soy Foods and Cognitive Function ................................................................... 12

2.3 Disadvantages of Soy Foods ....................................................................................... 14

2.3.1 Soy Foods Allergy ............................................................................................. 14

2.3.2 Soy Foods Intolerance ....................................................................................... 15

2.3.3 Beany Flavor ..................................................................................................... 15

2.4 New Soybean Lines .................................................................................................... 16

v

2.5 Sensory Evaluation ..................................................................................................... 19

2.5.1 Descriptive Sensory Evaluation ........................................................................ 19

2.5.2 Discrimination Test ........................................................................................... 23

2.5.3 Consumer Sensory Evaluation .......................................................................... 23

3. MATERIAL AND METHODS .................................................................................... 25

3.1 Sample Preparation ..................................................................................................... 25

3.1.1 Soy Milk Samples Preparation .......................................................................... 25

3.1.2 Soy Ice Cream Samples Preparation ................................................................. 26

3.2 Analytical Methods ..................................................................................................... 27

3.2.1 pH Value ........................................................................................................... 27

3.2.2 Color .................................................................................................................. 28

3.2.3 Viscosity ............................................................................................................ 28

3.2.3.1 Viscosity for Soymilk .............................................................................. 28

3.2.3.2 Viscosity for Soy Ice Cream Mix ............................................................ 28

3.2.4 Overrun .............................................................................................................. 29

3.2.5 Melting Rate ...................................................................................................... 29

3.2.6 Hardness ............................................................................................................ 29

3.2.7 Total Solids Content .......................................................................................... 30

3.2.8 Fat Content ........................................................................................................ 30

3.2.9 Protein Content .................................................................................................. 31

3.3 Descriptive Sensory Evaluation for Soymilk .............................................................. 33

3.3.1 Training of Panelists .......................................................................................... 33

3.3.2 ABX Test ........................................................................................................... 33

3.3.3 Descriptive Sensory Evaluation ........................................................................ 34

vi

3.3.4 References Preparation ...................................................................................... 34

3.4 Consumer Acceptance Test for Soymilk .................................................................... 37

3.5 Statistical Analysis ...................................................................................................... 37

4. RESULTS AND DISCUSSION ................................................................................... 43

4.1 Soymilk Fat, Protein, and Total Solids Contents ........................................................ 43

4.2 Soymilk Color ............................................................................................................. 45

4.3 Soymilk Viscosity ....................................................................................................... 46

4.4 Descriptive Sensory Evaluation for Soymilk .............................................................. 48

4.5 Consumer Acceptance Test for Soymilk .................................................................... 55

4.6 Soy Ice Cream Color ................................................................................................... 56

4.7 Soy Ice Cream Texture Properties .............................................................................. 57

5. CONCLUSION ............................................................................................................. 61

APPENDICES .................................................................................................................. 63

BIBLIOGRAOHY ............................................................................................................ 83

vii

LIST OF TABLES

Table Page

2.1 Sensory studies of different degree of lipoxygenase for beany flavor in soymilk.16

3.1 Initial list of attributes of soy milk samples……………...…..…………………..40

3.2 Final list of attributes of soy milk samples…………..…………………………..42

4.1 Composition of soymilk samples………..……………………………………...44

4.2 Soluble carbohydrate content of soybean lines…………….………….………....45

4.3 pH value and color of soymilk samples……….……………………….………...46

4.4 Viscosity of soymilk samples………….…………………………….…………..47

4.5 Sensory data with significance values from ANOVA and jack-knife estimates...51

4.6 Variance explained by the seven principal directions…………………….……..52

4.7 Consumer acceptance of soymilk samples…………...…………….……………56

4.8 Color of soy ice cream mixes……………………………………………….……57

4.9 Viscosity of soy ice cream mixes……………………………………………..….58

4.10 Overrun value and hardness of soy ice cream samples………………..………..59

viii

LIST OF FIGURES

Figure Page

4.1 Scores on principal direction 1 and 2 from PCA on mean centered data……..…53

4.2 Loadings on principal direction 1 and 2 from PCA on mean centered data……..54

4.3 Percentage of melting down of five soy ice cream samples….…..….........……..60

ix



Jing Tang

Dr. Fu-hung Hsieh, Thesis Supervisor

ABSTRACT

Soymilk may provide health benefits due to its soy isoflavone compounds,

polyunsaturated fatty acid compositions and fibers. The main objective of this study was

to investigate the effects of soybean varieties on descriptive sensory evaluation, consumer

acceptance test, and texture properties of soy ice creams. Plain flavor soymilks made

from five soybean lines were made in the laboratory using a soymilk maker for physical,

chemical, descriptive sensory analysis, and making soy ice creams. Soymilks were placed

at room temperature for 2 h first prior to the sensory evaluations. For the consumer

acceptance test, soymilks were flavored with sugar and vanilla.

Soymilk fat content, protein content, and redness did not show significant

differences among samples. However, high oleic acid soymilk sample and high sucrose

content soymilk sample had significantly higher results on total solids content and

yellowness. Moreover, the high sucrose content soymilk sample was significantly lighter

than others due to its clear hilum skin. For the viscosity, high oleic acid soymilk sample

was significantly more viscous than other samples because of the oleic acid composition

and the molecular conformation of the triacylglyceride molecules. The viscosity also

contributed to the overrun value of soy ice cream. The more viscous the mix, the lower

the overrun of the soy ice cream became. Thus, the low overrun was associated with

harder texture and higher melting rate.

x

The results from descriptive sensory evaluation showed that lipoxygenase free

soymilk was the least beany flavor and most rice-like flavor, and high sucrose content

soymilk was significantly sweeter than others, but all of them did not significantly change

the texture of soymilks. On the other hand, high oleic acid content soymilk was the

thickest one, but was not associated with flavor aspects. Furthermore, consumer

acceptance test showed that soymilk made from sample KB10-5#1137 was the best.

1

CHAPTER 1

INTRODUCTION

1.1 Background

Soy foods are traditional foods from soybeans in Asia, and now become popular

in Western Countries. In 2011, total U. S. soy foods sales exceeded $5 billion, lead by

soy milk beverages, which reached 994 million dollars according to the Soyfoods

Association of North America (SANA) (2011). Soy foods have a high plant protein

content and contain polyphenol components, such as isoflavones. Thus, soy foods are

classified as a functional food. In addition, some papers indicated that soy foods may

decrease the risk of coronary heart disease (Malik and others 2004), have anti-cancer and

anti-inflammation properties (Yang and others 2009; Peng and others 2009), improve

menopausal symptoms and increase the Calcium absorption for women (Charoenphun

and others 2013; Bao and others 2008), provide positive effects for Type 1 or Type 2

diabetes (Zimmermann and others 2012), and maintain or even relieve dementia

symptoms for patients who suffer from Alzheimer’s disease (Duffy and others 2003).

Soy foods, especially soymilk, are considered a good substitution for dairy

products for individuals who have milk intolerance. Milk intolerance including cow’s

milk protein allergy (CMPA), cow’s milk protein intolerance (CMPI), and lactose

intolerance is prevalent in the world, especially in children. CMPA can affect from 2 to 6%

of children. Studies have shown that 80% to 90% of these children will resolve CMPA

within their fifth year (Host and others 2002; Wood 2003; Gopalan 2011). The important

and widely used method used to treat milk intolerance is the exclusion of all forms of

2

animal milk and milk products from the diet. Because of the high plant protein content

and similar texture as milk, soymilk is a healthy protein source for those milk intolerant

individuals. In addition, cow’s milk contains saturated fats that might increase the risk of

cardiovascular diseases (Astrup and others 2011; American Heart Association Nutrition

Committee. 2006; Siri-Tarino and others 2010). Soymilk, which contains much lower

amounts of saturated fat than cow’s milk, could be a good choice for individuals who are

concerned about heart disease.

Although soymilk and soy foods are nutritious and their consumption has many

health benefits, soy milk has some limitations such as beany flavor, possible allergic

reactions and undesirable microbial fermentation after intake by some consumers.

Consequently, scientists have tried to resolve these problems by creating some new lines

of soybeans, for instance, low and ultra-low raffinose family of oligosaccharides

soybeans, lipoxygenase free soybeans, high oleic acid soybeans, low P34 allergen

soybeans, and high sucrose soybeans. It would be of great interest to know if soy milks

made from these new lines of soybeans could address these problems. This has not been

reported in the literature.

Traditional dairy ice creams are popular and sales continue to increase, 4.1%

increase in 2011 reaching 10.7 billion dollars and 4% growth in 2012 (International

Dairy Foods Association 2013). For those people who cannot or do not want to consume

any dairy products, soymilk ice creams might be an acceptable replacement. Therefore,

physical properties of soy ice creams made from new types of soymilks were tested

because texture and mouth feel are the most important characteristics for ice cream.

3

1.2 Objectives

The objectives of this study were

1) To produce soymilks and soy ice creams from five lines of soybeans;

2) To determine the effect of soybean lines on physical and chemical characteristics

of the soymilks, including color, viscosity, pH value, total solids content, fat

content, and protein content;

3) To design a descriptive sensory test and a consumer acceptance test of the

soymilk samples and to study the effect of soybean lines on sensory evaluation;

4) To investigate the effect of soybean lines on texture properties of the soy ice

cream samples, including overrun value, viscosity, hardness, and melting

properties.

4

CHAPTER 2

LITERATURE REVIEW

2.1 Soy Products

Soy based foods were developed in Asian countries and accepted in Western

countries within decades. In 1917, soymilk was produced commercially in the United

States, introduced by Li Yu-ying in 1913 (SoyInfo Center 2001). Soy foods become

popular due to two reasons: the first is that soy foods contain many nutrients, and the

second is that a high percentage of populations in Western countries have allergic

reactions to dairy. The American College of Allergy, Asthma and Immunology (ACAAI)

reported that around 2.5% of children younger than three years old are allergic to milk in

the USA and approximately 80% of these children with milk allergy will not outgrow the

allergic reaction until the age of sixteen (2010). Moreover, thirty to fifty million

Americans, including adults and children, have lactose intolerance reported by the Ohio

State University Wexner Medical Center (2007).

Traditional soy foods are classified into two categories: non-fermented and

fermented. Typical non-fermented soybean products are soy milk, tofu, tofu skin, soy

meat alternatives, etc., while fermented products include soy sauce, fermented bean paste,

natto, sufu, miso, onchom, tempeh, etc. In the United States, the commercial soy based

products include soy protein products (meat analogs), soy milk, soy cheese, soy nut butter,

soy yogurt, soy frozen dessert, and soy based infant formulas (United Soybean Board

2012). In 2011, the soy based food industry sales exceeded $5 billion, the most

5

significant product being soy milk beverages, of which sales reached 994.3 million

dollars, according to the Soyfoods Association of North America (SANA) (2011).

2.2 Soy Foods and Health Promotion

Soybeans are one of the highest sources of plant protein, containing around 40%

protein, in addition to being rich in essential amino acids (Xiao 2008). They also contain

35% carbohydrates, including soluble di- and oligosaccharides, 18% to 22% fat,

including polyunsaturated fatty acids, tocopherols, phytosterols, phospholipids, minerals,

B vitamins, and fiber (Liu 1997). Soy foods are classified as functional foods because

they contain polyphenol components, including isoflavones, glycosides and malonate

conjugates, which contribute to their antioxidant activity (Xiao 2008; Mackinnon and

others 2011). The amount of isoflavones in soy products varies with the type of soybean,

the condition and area of cultivation, and processing. Green soybeans and tempeh are

good sources of isoflavones with more than 48.95 mg isoflavones per 100 g product.

Tofu and soy milk contain approximately 30 to 40 mg isoflavones per gram of protein

(USDA 2008).

However, soybeans contain potentially anti-nutritive compounds, such as lectins,

phytic acid, protease inhibitors, and phenolic acids (Xiao 2008). They may adversely

affect iron and zinc balances in some individuals who do not eat a well-balanced diet.

Despite the potential anti-nutritive effects, a large amount of research papers reported

health benefits of soy foods in the area of disease prevention.

Non-fermented soy foods contain a high amount of soybean isoflavones, which

contribute to lower rates of osteoporosis (Taku and others 2011; Wei and others 2012).

Previous studies showed that non-fermented soy foods may be associated with reducing

6

gastric cancer risk (Wu and others 2000; Messina and others 1994; Kim and others 2011)

and breast cancer risk (Wu and others 1996). On the other hand, fermented soy food may

improve the bioavailability of dietary zinc, iron, and vitamins, since it has much less

phytic acid than raw beans, which may obstruct the absorption of minerals (Hirabayashi

and others 1998; Kasaoka and others 1997; Denter and others 1998; Jiang and others

2011). In addition, after fermented soy foods are digested and adsorbed in the human

body, oligopeptides can be generated, which have a more rapid absorption and higher

biological activity than free amino acids in the small intestine (Gibbs and others 2004;

Maebuchi and others 2007).

2.2.1 Soy Foods and Coronary Heart Disease

Coronary Heart Disease (CHD) is a serious heart disease and a major cause of

death in most developed countries. The high fat and sugar content of the diet particularly

in the United States and many other variable factors, such as smoking and drinking

alcoholic beverages, contribute to an individual’s risk for CHD. Low-density lipoproteins

(LDL) play key roles in the atherosclerotic process. LDL is oxidized by free radicals

along the walls of blood vessels and forms a glue-like material which builds up and

eventually may block the vessel. High-density lipoproteins (HDL) have the protective

effect against LDL oxidation and eliminate the cholesterol along the blood vessel wall

(Malik and others 2004). Soy foods contain a high quantity of soy isoflavone

components that have effects on reducing the risk for CHD because of their antioxidant

(Anderson and others 1998; Tikkanen and others 1998) and anti-inflammatory effects

(Sadowska-Krowicka and others 1998; Gooderham and others 1996). A study with rats

reported that soy isoflavones appear to be incorporated with LDL and protect it from

7

oxidation (Anderson and others 1998). In addition, soy proteins could be another

contributor for reducing risk of CHD; in a clinical study of 38 humans, soy protein intake

significantly decreased serum concentrations of LDL-cholesterol, while increasing the

HDL concentration in 34 of the 38 individuals (Anderson and others 2001).

Furthermore, during the atherosclerotic process, blood vessels could be damaged

by the abnormal spasm or “clamping down” which occurs when the atherosclerotic

plaque is exposed to stress. Soy proteins and soy isoflavones may reduce the blood clot

formation (Anderson and others 2001) and restore damaged blood vessels to protect

blood vessels because of their anti-inflammatory effects (Honore and others 1997). Since

soy proteins and their isoflavones may correct the balance of blood lipid levels, protect

blood vessels, and have antioxidant and anti-inflammatory effects, in 1999 the U.S. Food

and Drug Administration (FDA) approved a health claim that eating soy protein as part of

a low saturated fat and cholesterol diet may decrease an individual’s risk for CHD (FDA

1999). That same year, the U.S. FDA published that 25 g of soy protein per day provided

an appropriate intake to reduce risk for CHD (FDA 1999).

2.2.2 Soy Foods and Cancer

Currently, more than 13 million people in the United States have had some type

of cancer (American Cancer Society 2012). The main types of cancer are lung cancer,

colon cancer, breast cancer, prostate cancer, and skin cancer. The high rate of cancer is

often because of unhealthy lifestyles, such as smoking, high-calorie diets, drinking

alcohol, lack of physical activities, etc.

Results from animal and in vitro studies have suggested that soy foods have a

positive effect against cancer initiation due to its high isoflavone content. The one

8

possible mechanism of soy’s effects against cancer is that the structure of isoflavones is

similar to estrogen’s structure, thus having a weak estrogenic effect. Consequently, the

association of soy foods with cancer risk was first researched for breast cancer and

prostate cancer prevention. Several recently published papers indicated that soy foods or

isoflavones may decrease the risk of breast and prostate cancers. Yan and Spitznagel

(2005) examined the relationship between soy intake and prostate cancer risk by meta-

analysis and demonstrated that consumption of soy food could be related to a lower risk

of prostate cancer in men. In China, 73,223 Chinese women participated in a Shanghai

Women’s Health Study to investigate the association of soy food intake with breast

cancer risk. The result of this prospective cohort study showed that soy food intake

significantly reduced the risk of premenopausal breast cancer (Lee and others 2009).

However, Nechuta and others (2012) demonstrated that soy food intake did not result in a

significant reduction in the risk of breast cancer, yet it had a statistically significant

reductive effect on the risk of cancer recurrence.

The other possible mechanism of soy foods to prevent cancer is similar to the

mechanism of blood vessel protection. Formation of new blood vessels is the key step for

the growth of human cancers and facilitation of tumor cell metastases. Soy isoflavone is

the major protective components, since it can restore the structure of blood vessels

(Honore and others 1997) and inhibit cell transformation and tumor cell proliferation

(Anderson and others 2001). A prospective cohort study provided strong evidence that

soy food consumption may protect against colorectal cancer in women (Yang and others

2009). In addition, in a large case control study, researchers demonstrated that soy food

consumption may reduce the risk of colorectal cancer in men (Budhathoki and others

9

2011). Soy food consumption has a positive effect on lung cancer as well. A meta-

analysis demonstrated that soy isoflavones were associated with a 27% risk reduction in

lung cancer (Yang and others 2011). A recent epidemiologic study which involved 444

nonsmoking women supported this claim with similar conclusions (Yang and others

2013).

2.2.3 Soy Foods and Women’s Health

2.2.3.1 Soy Foods and Menopausal Symptoms

The main menopausal symptoms women experience are vasomotor symptoms,

hot flashes, and night sweats. The prevalence of those symptoms is much more common

among North American and European women than those in China and Japan.

Approximately 20% of Chinese women and less than 25% of Japanese women complain

of hot flashes and night sweats, compared to around 85% of North American and

European women (Anderson and others 2001; Reed and others 2013; Freeman and others

2007). McNagny and others (1997) indicated the undesirable symptoms could be

eliminated or treated with estrogen substitutions, called hormone replacement therapy

(HRT), which is chosen by 47% of postmenopausal women’s physicians. However,

commercial HRT drugs may have some side effects such as heart attack, breast cancer,

and blood clots in the lungs and legs (Björn and others 1999). Furthermore, the

differences in prevalence of menopausal symptoms in various countries are hypothesized

to be partly related to the content of dietary soy consumption. The average soy intake per

day of Chinese women is up to nine times greater than that of North American women

(Reed and others 2013). The high concentration of soy’s estrogen-like isoflavones could

10

make it a good hormone replacement option for postmenopausal women. Consequently,

soy foods may be a healthy alternative to HRT pills for women.

2.2.3.2 Soy Foods and Calcium Binding Properties

Calcium is known to make up about 1.5-2.2% of bodyweight. Bones and teeth

contain approximately 99% of the body’s calcium. The other 1% is dispersed in

intracellular and extracellular fluids (Kitts 2006). Intestinal calcium absorption regresses

in ovarian hormone deficiency and may partially lead to bone loss of women during

menopause (Arjmandi and others 2002). Common sources of calcium-binding peptides

are casein (Kitts 2006), shrimp, carp muscle (Charoenphun and others 2013), and soy

protein (Charoenphun and others 2013; Bao and others 2007).

One possible mechanism of regressive calcium absorption is that estrogen

deficiency leads to a down-regulation of key calcium transport molecules in intestinal

cells (Aloia and others 2013). Previous studies have shown that since soy isoflavones

have a similar structure to estrogen receptors, which could enhances calcium transport in

intestinal cells, so that soy isoflavones may exert partly estrogen activity in tissues and

alter intestinal calcium absorption just like estrogen receptors do (Arjmandi and others

2002). In addition, soybean protein contains a number of asparagine and glutamine,

because the free carboxyl group content of the hydrolysates significantly increased by

treatment with asparaginase or glutaminase (Bao and others 2007). Bao and colleagues

(2008) found that calcium binding increased linearly with the increment of carboxyl

group of acidic amino acids in soy protein hydrolysate.

Though the calcium-binding capacity of soy protein is lower than that of casein

(Pathomrungsiyounggul and others 2010), soy isoflavones may alter intestinal calcium

11

absorption, particularly in women, because of its structural similarities with estrogen

receptors (Arjmandi and others 2002) and soy protein hydrolysates may increase calcium

absorption as well (Bao and others 2008 and Bao and others 2007).

2.2.4 Soy Foods and Chronic Kidney Disease

Dietary protein plays a critical role in the prevention and treatment of chronic

kidney disease. Compared with meat protein, vegetable protein has a temperate effect on

renal glomerular filtration (Kitazato and others 2002). Soy food is one of the most

frequently used plant protein sources in our diet. One hundred fifty years ago, researchers

studied the effects of soy protein on kidney diseases (Anderson and others 2001).

Recently, soy protein and their isoflavones have been demonstrated to provide some

positive effects in individuals with Type 1 or Type 2 diabetes (Anderson and others 2001;

Zimmermann and others 2012; Boué and others 2012), end-stage renal disease (ESRD)

(Ogborn and others 2010), and polycystic kidney disease (Peng and others 2009). The

mechanisms of how soy protein prevents kidney diseases have not been fully explained;

however, from previous studies, researchers know that soy isoflavones, amino acid

composition, and antioxidant and anti-inflammatory properties could be the major

contributors.

Type 2 diabetes mellitus results from insulin resistance, which causes an increase

in hepatic glucose production. The ultimate goal of diabetes treatment is to maintain or

decrease the level of hyperglycemia (Moller 2001). Several animal and human studies

indicated that soy food can play a role in the improvement of glycemic control.

Consumption of soy foods is associated with lower fasting insulin levels, reduced risk of

type 2 diabetes, and glycemic levels (Jang and others 2010; Davis and others 2007; Lee

12

2006 and Zimmermann and others 2012). For instance, consumption of cheonggukjang, a

fermented soybean paste, has been shown to reduce glycemic level and maintain

pancreatic β-cell in diabetic animal models (Kwon and others 2007).

Furthermore, nearly one-third of those with diabetic nephropathy have

exacerbation renal disease, such as ESRD and polycystic kidney disease (Anderson and

others 2001). Renal prostanoids and maintenance of normal renal function are sensitive to

physiological challenges. In pathological conditions, renal prostanoids may rapidly

change in inflammatory and proliferative processes and develop into renal injury (Peng

and others 2009). Consequently, when pressure increases between glomerular capillaries,

capillary dilation will stimulate some intracellular pathways and alter level of prostanoids.

Soy isoflavones may block some of those pathways and thereby inhibit kinase activity

(Hao and others 2008 and Anderson and others 2001). In addition, because of their anti-

inflammatory effects, soy isoflavones may decrease the inflammatory processes which

contribute to the pathogenesis of renal failure.

2.2.5 Soy Foods and Cognitive Function

Alzheimer’s is a chronic and progressive disease, which is the most common form

of dementia. The symptom of Alzheimer’s disease (AD) in the early stages, is memory

loss, and in the late stage, is losing the ability to carry on a conversation and respond to

their environment. AD is the fifth leading cause of death among aged 65 and older in the

United States, and also is the only cause of death among the top 10 without a way to cure

it. In 2013, 5.2 million Americans suffer from AD, including 200,000 individuals

younger than age 65 who have younger-onset Alzheimer’s (Alzheimer’s Association

2013). Plaques and tangles are observed in older individuals and far more in AD patients.

13

These two abnormal structures are prime suspects in damaging and killing nerve cells.

However, researchers and scientists are still working on what role plaques and tangles

play in AD. Recently, soy isoflavones, as partial estrogen agonists, have been proved to

modulate cognitive function, particularly in postmenopausal estrogen-deficient women.

Genistein and daidzein are structurally similar to estrogen and have the ability to

bind to estrogen receptors, hence exerting both agonistic and antagonistic estrogenic

effects (Miksicek 1995). Two types of estrogen receptor have been found in the nucleus:

ERα and ERβ. ERβ is the major subtype of receptor in the brain so that it is hypothesized

that soy isoflavones may exert potentially biological activities in the brain (Lee 2005).

Some human studies have shown an association between soy isoflavones intake

and cognitive function. Duffy and others (2003) indicated a beneficial contribution of soy

isoflavones in the prevention of Alzheimer’s and enhanced cognitive function. They

examined the short term, 12 weeks, effect of soy extract containing isoflavones of thirty

three postmenopausal women (50-65 years) on cognitive tests and regulatory behaviors.

The group receiving isoflavones supplement showed significantly improvements in recall

of pictures and in a sustained attention task. Kritz-Silverstein and others (2002)

demonstrated that category fluency was higher in women receiving soy isoflavones

supplement than in controls. In addition, a case-control study found that soy isoflavones

supplementation improved cognitive function in men (File 2001). However, based on a

double-bind, randomized, placebo-controlled trial of 202 healthy postmenopausal women,

Kreijkamp-Kaspers and colleagues (2004) reported that there were no differences

between the control group receiving milk protein and the experimental group receiving

14

25.6 g of soy protein containing 99 mg of isoflavones in cognitive function of these

women.

2.3 Disadvantages of Soy Foods

2.3.1 Soy Foods Allergy

Soy is a popular vegetarian alternative to meats, and many processed foods

contain soy fiber or soy protein to get a higher nutritional value. In addition, soy food is

also an important source of nutrition for infants with milk allergy (Sicherer and others

2006). However, approximately 0.4% of children are allergic to soy based products,

making soy allergy about half as common as peanut allergy (Savage and others 2010).

Therefore, it is urgent to identify the protein components which elicit allergic

manifestations of soy products. Immunoblotting and enzyme-linked immunosorbent

assay (ELISA) have been widely used to detect and determine the major allergens by

using allergen-specific antibodies (Ogawa 2006). According to immunoblotting patterns,

Herian and others (1990) demonstrated that allergens in soybean could be classified into

three categories. Furthermore, the protein components binding with immunoglobulin E

(IgE) antibodies in the sera of soybean sensitive patients indicated three major allergenic

proteins in soybean, called Gly m Bd 30K, Gly m Bd 28K and Gly m Bd 60K (Ogawa

2006; Ogawa and others 1991). Gly m Bd 30K, which is identified as P34, is a common

allergenic protein in soybean, because two thirds of soybean-sensitive patients examined

have shown IgE antibody binding to Gly m Bd 30K (Ogawa 2006). However, it is hard

to define how many specific IgEs in soybean will cause clinical soy allergy. In a

15

retrospective review, Komata and others (2009) demonstrated that soy-specific IgE levels

of between 20 and 30 kU/L predicted a 50% chance of leading a soy allergy.

There are several methods that could reduce or remove these three major allergens.

They are molecular breeding, genetic modification, physicochemical reduction,

enzymatic digestion, chemical modification, extrusion cooking (Ogawa 2006) and

transgenic suppression (Bilyeu and others 2009).

2.3.2 Soy Foods Intolerance

After consumption of soy foods, some sensitive individuals may generate

undesirable microbial fermentation in the intestines, leading to digestive distress. Some

sensitive individuals cannot fully digest α-galactosides with α-galactosidase completely

in their small intestine. So in their large intestine, non-digestible oligosaccharides are

digested by microorganisms, liberating huge amounts of gas (Slominski 1994). These

nondigestible oligosaccharides, including the stachyose and raffinose families of

oligosaccharides (RFOs), cannot be eliminated by usual processing (Bilyeu and others

2009).

2.3.3 Beany Flavor

Normal mature soybeans contain three lipoxygenase (LOX) isozymes, LOX-1,

LOX-2, and LOX-3 (Torres-Penaranda and others 1998). In oxidation reactions, LOX

isozymes require substrates containing polyunsaturated fatty acids, including linoleic acid

and linolenic acid (King and others 2001). These polyunsaturated fatty acids contribute to

beany or grassy flavor in soy foods. Table 2.1 shows that the differences in LOX may

lead to different results in sensory studies of soymilk.

16

Table 2.1 Sensory studies of different degree of lipoxygenase for beany flavor in

soymilks

No Lipoxygenase Variety Beany Flavor (Comperd with

Normal Soymilk)

References

1 LOX-free Significant difference on beany

flavor

Torres-Penaranda

and others 1998


flavor

Torres-Penaranda

and others 2001


flavor

Kobayashi and

others 1995

4 LOX-1 No significant difference on beany

flavor

Kobayashi and

others 1995

5 LOX-free No significant difference on beany

flavor

King and others

2001

6 LOX-free No significant difference on beany

flavor

Suratman and others

2004

2.4 New Soybean Lines

Soy foods have high amounts of nutrients so that consuming soy foods has a

number of health benefits for humans. However, eating soy foods has been limited by

allergic reactions, soy intolerance of sensitive individuals, undesirable beany flavor and

other limitations. Consequently, scientists developed several varieties of soybeans either

to lower the risk of undesired sensitive reactions, such as anaphylaxis and soy intolerance,

or alleviating unwanted beany flavor. Nowadays, several soybean lines, such as the high

oleic acid soybean lines, the low raffinose soybean lines, lipoxygenase free soybean lines,

17

low allergen content soybean lines, and some other soybean lines, were developed by

scientists to fix these limitations and may potentially improve the health benefits.

Commercial soybeans typically contain an average of 20% oil (dry weight).

Soybean oil is widely used for heat intensive using such as cooking and frying. However,

heat stability and oxidative stability of soybean oil has been terminated due to the

unwanted production of trans fats from hydrogenation of soybean oil. The alteration of

fatty acid compositions in soybean to improve soybean oil quality has been a long-time

goal of soybean researchers. High oleic acid soybean oil can target the recent demand of

trans-fat free soybean oil. Oleic acid is considered the most functional for food uses due

to its oxidative stability. High oleic acid soybeans typically contain at least 75% oleic

acid content and 6% polyunsaturated fatty acids (linoleic acid and linolenic acid), while

commercial soybeans have approximately 20-25% oleic acid content and 60% of

polyunsaturated fatty acids (Pham and others 2012). The high concentration of

polyunsaturated fatty acids contribute to the low oxidative and frying stability of soybean

oil, leading to rancidity, and shortened storage time of food products (Warner and Fehr

2008).

The raffinose family of oligosaccharides (RFOs) included in raffinose and

stachyose is made up of different β-galactosyl derivatives, which can be found in soybean

seeds (Hernandez-Hernandez and others 2011; Martíenz-Villaluenga and others 2007).

Raffinose and stachyose cannot be digested by humans or other monogastric animals

because of the lack of α-galactosidase activity within the gut. As a result, the RFOs are

considered as anti-nutritional factors. The low concentration level of raffinose and

stachyose in soybeans is a result of raffinose and stachyose synthase enzyme activities,

18

which initiate a galactosyl transfer from galatinol to sucrose (Dierking and others 2008).

Generally speaking, removing raffinose and stachyose from soybeans may increase

metabolism energy of a person on a soybean-supplemented diet and may reduce flatulent

productions (Coon and others 1990; Dierking and others 2008; Parsons and others 2000).

The soybean lipoxygenases are capable of producing unsaturated fatty acid

hydroperoxides by oxidizing the polyunsaturated fatty acids, such as linoleic and

linolenic acids. The polyunsaturated fatty acids are associated with the undesirable beany

or grassy flavors for soymilk (Torres-Penaranda and others 1998). In addition, the total

contents of hexanal in soymilk have been significantly associated with the lipoxygenases

contents in soymilk beverages (Wang and others 1998). Hexanal is the critical chemical

compound which provides the beany flavor in soymilk. Yuan and Chang (2007) indicated

that lower beany flavor and beany aroma (Kobayashi and others 1995) were present in

the soymilk produced by lipoxygenases-deficient soybean varieties.

The major allergenic soy protein, P34, has been classified as an immunodominant

allergen (Herman and others 2003). Herman and others (2003) and Helm and co-workers

(2000) reported up to 65% of soy sensitive participants in their studies had an allergic

reaction to this protein. The one mechanism of protein allergic reaction may be the

homology in the amino acid sequences among various allergenic proteins so that the

allergic reactions may occur due to the cross reactivity between similar allergens (Wilson

and others 2005). For instance, the allergy caused by peas and peanuts may be due to the

cross reactivity relationship between IgE antibodies against pea vicilin and peanut vicilin

(Wensing and others 2003). Since P34 contains approximately 70% sequence homology

with peanut’s main allergen (Ara h 1) and more than 50% with the cow’s milk allergen

19

(α-S1-casein), it will cause an allergic reaction when it meets the allergen, IgE antibodies,

just like the peanut vicilin. So reducing the P34 allergen content of soybeans may

decrease the possibility of an allergic reaction to soymilk beverages (Bilyeu and others

2009).

However, not many research studies investigated the sensory properties of these

new beans. Although soybeans have many benefits, it is useless if customers will not

accept them. Therefore, sensory evaluation of the new varieties of soybeans should be

done so as to successfully obtain and understand each single aspect of flavor in soybeans.

Since directly eating soybeans may be unacceptable to the panelists, plain flavor soymilk

could be used as a research project to study these soybeans.

2.5 Sensory Evaluation

Sensory analysis is widely used in food companies to investigate the influences on

ingredients, processing variables, and storage conditions of their products. It provides

developers with a perceptive of product quality, profiles of competing products and

consumer expectations (Stone and others 2004).

Sensory analysis methods consist of three main techniques, Descriptive Sensory

Evaluation, Discrimination Testing and Consumer Sensory Test.

2.5.1 Descriptive Sensory Evaluation

Descriptive Sensory Evaluation has been regularly used to distinguish among

aroma, flavor, appearance, after-taste and texture characteristics and quantify the

perceived intensities of the sensory attributes of a food product (Lawless and others

2010). According to these aspects of the perceived food product, sensory judges’ job is

20

discussing a list of attributes as comprehensive as possible, and then quantifying the

intensities of all the attributes. In addition, descriptive sensory evaluation can provide the

basis for mapping the similarities and differences of products so that the effect of specific

ingredient or process variables can be determined and explained. As a result, the

descriptive sensory analysis can describe both the qualitative and quantitative sensory

components of a food product by well-trained sensory panelists (Stone and others 2004).

Furthermore, descriptive analyses can also distinguish the differences among several

products on specific attributes. It doesn’t like other sensory analyses which provide

overall results (Lawless and others 2010). Consequently, descriptive sensory analysis is

frequently used in product development in food industry to determine how competitive

the new product is and how close this new product target consumer’s demands. However,

the panelists of descriptive analyses should never be consumers, since the sensory judges

should be trained, at least should be reproducible.

Generally speaking, a descriptive sensory analysis has between 8 to 12 panelists

which have been well trained. A brief statement or presentation of the objective before

training is necessary so that panelists won’t misunderstand the purpose of the sensory

study. Many sensory analyses failed because of misconstruing of the objective of the

study (Larmond 1987). During training sessions, reference standards are provided by the

panel leader to help panelists to understand and agree on the meaning of the attributes

used. After they reach agreements on all of the attributes, a quantitative scale for intensity

which allows the data to be statistically analyzed, should be provided by leader. Panelists

won’t be asked for their hedonic responses to the products, though it may bring few

21

influences on the results. However, this few influences can be eliminated by sensory

approaches (Lawless and others 2010 and Larmond 1987).

Descriptive Sensory Analysis including Flavor Profile® method, Texture Profile

®

Method, Quantitative Descriptive Analysis® (QDA), Spectrum

® Method, Quantitative

Flavor Profiling, Free-choice Profiling and Diagnostic descriptive analysis. These first

three methods are frequently used (Caul 1957; Brandt and others 1963 and Stone and

others 1974). The Flavor Profile® method focuses on describing the intensity of flavors

and aromas of food products, including the aftertaste and the overall impression. While,

the Texture Profile® method is the description of textural characteristics and mouth feel

of food products. QDA involves an unstructured scale, which associates with the specific

descriptive attributes about both flavor and textural attributes and indicates the intensity

(Stone and others 1974).

QDA is a developed method from the Flavor Profile® method and Texture

Profile® method. Compared with these two methods, QDA is not a consensus technique.

The data may not reach agreement by all of the sensory judges, though after a consent

discussion, because of the sensitivity of each judge. In addition, panel leader is not

participating in during the discussing session in QDA method (Meilgaard and others 2006;

Murray and others 2001; Stone and others 1980).

During the QDA training sessions, 10-12 panelists are asked to list all the possible

properties of the products and generate a series of terms to describe differences among

the product samples on their own, as completely as possible. After the individual work,

they discuss to develop a standardized definition and terminology for each sensory

22

attribute and to decide the standard references which are appropriately to be used to

anchor the descriptive terms (Murray and others 2001; Drake and others 2005; Drake and

others 2007). When the list of attributes is generated, judges are trained by creating a

consensus vocabulary. The leader’s responsibilities are only directing discussion,

providing materials, standard references and samples which are required by the panelists,

and offering individual training sessions based on the statistical analysis of the panelist’s

performance. As mentioned before that QDA is not a consensus method, during training,

one of the challenges is how to help judges determine the intensity of each characteristic.

Most sensory scientist assume that each penal will use different parts of the intensity

scale to make their decisions so that the isolate scale values are not the essential factor

(Civille and others 1986). Consequently, the QDA data must be regarded as relative

values instead of absolute values. Some statistical analysis methods, such as t-tests and

ANOVA can eliminate the effect of using different parts of the scale (Lawless and others

2010).

Therefore, QDA method has been widely used due to several reasons, including:

(1) It can evaluate all the sensory attributes and properties of product samples, even a

completely new one;

(2) It only need a limited number of sensory judges, usually 10-12 judges, to evaluate

the samples;

(3) It creates a sensory language only by panelists, and has no influence from the

panel leader;

(4) It has capability to estimate multiple products samples in one test;

(5) It can be analyzed by statistical method easily.

23

2.5.2 Discrimination Test

Discrimination Evaluation only allows the sensory panelists to answer questions

that whether the two samples are different or not. Panel leaders cannot determine that two

product samples differ from one another on a specific characteristic. In some cases,

sensory panelists may not distinguish the differences between samples, which can be

used in food industry as well (Meilgaard and others 2006; Stone and others 2004). For

instance, an ice cream food company may want to use the cheaper vanilla extract to

replace the expensive one in their vanilla ice cream. Meanwhile, they may not want the

consumers to perceive a difference in the product as well. In this situation, the

discrimination test can be used. If the result shows that judges cannot tell the difference

between two products, making this substitution will decrease the cost. However, if the

judges can perceive the variance, making this substitution will not be an appropriate way

to reduce the cost (Lawless and others 2010). Paired Comparison Tests, Triangle Tests,

Duo-Trio Test, ABX Discrimination Task, and A-Not-A Test, belong to the

discrimination analysis.

2.5.3 Consumer Sensory Evaluation

Consumer Sensory Testing is usually the last step before a product goes into

market. The panelists participating in this test are usually un-trained sensory judges, most

of them are consumers. There are two main consumer sensory tests for food products, the

preference testing and the acceptance testing. Both of them are called hedonic or affective

tests. In the preference testing, panelists have to make a choice between two products or

among several products. If product samples are only two, it is called a paired preference

test, which is the simplest and most popular sensory test to consumers, because paired

24

preference test mimics the processing of shopping, choosing among alternatives (Lawless

and others 2010). However, it is possible that a product wins in the paired preference test,

but loses in the real market, may be because of the insufficient of sample capacity, or

other reasons. Acceptance testing with a hedonic scale is designed to fix this problem

(Coetzee and others 1996).

In the acceptance or liking testing, panelists are required to mark the acceptability

of products on a hedonic scale without any comparison. The most common scale is 9-

point hedonic scale, which including Like Extremely, Like Very Much, Like Moderately,

Like Slightly, Neither Like or Dislike, Dislike Slightly, Dislike Moderately, Dislike Very

Much, and Dislike Extremely. “9” means Like Extremely, and “1” means Dislike

Extremely (Lawless and others 2010). Sensory scientists supply the samples with

randomly number to panelists one at a time, and then ask panels to indicate their hedonic

response on the 9-point hedonic scale so that the problem mentioned can be fixed. For

instance, sample A got a 5 scale in a liking test, while sample B got a 3 scale in the liking

test, though sample A had a higher score which can be regard as wining in the paired

preference test, but it didn’t indicate sample A will be the first choice for consumers. In

addition, a previous study demonstrated that whether the scale was printed horizontally or

vertically, or whether the scales was started from “Dislike Extremely” or “Like

Extremely”, did not show a significantly difference (Jones and others 1955).

25

CHAPTER 3

MATERIAL AND METHODS

3.1 Sample Preparation

3.1.1 Soy Milk Samples Preparation

Soy milk samples from five soybean lines were used in this study. The

preparation of each soy milk sample was in a food-grade lab. The following describes the

primary contents of interest for each soy milk product:

1) The first line of soybean had an ultra-low concentration of raffinose. The seeds

had a stachyose of content 0.3%, with no detectable raffinose (KB10-23#1681).

2) The second line of soybean had a high level of oleic acid (KB10-5#1137).

3) The third line of soybean not only contained a low level of raffinose, but was also

lipoxygenase free (KB10-25#1235).

4) The fourth line of soybeans had a low concentration of raffinose, a low

concentration of phytic acid, and also a low content of Gly m Bd 30K (P34)

allergen (KB09-5(2) #15).

5) The last line of soybean contained a high level of sucrose and clear hilum

(534545).

All these soybeans were provided by Dr. Bilyeu who is an adjunct professor at the

University of Missouri-Columbia. Moreover, all these beans were not genetically

modified.

26

Soybeans from each treatment were soaked in distilled water for 18 h at room

temperature, and then they were drained and washed with distilled water before making

the soymilk samples. The samples were made with soybeans and distilled water (the ratio

of soybeans and distilled water was 1:9 w/w). The beans and distilled water were added

into a soy milk maker (G3 soy milk maker, Soya Joy, Dandridge, TN). The blender in the

soy milk maker ground the beans with water and the temperature reached 100°C for 5

min to pasteurize these ingredients. Soymilk samples were removed from the soy milk

maker, filtered twice with a 7-inch stainless steel strainer and transferred into a clean

container. After that the samples were stored in a refrigerator at 4°C for 24 h. Before

conducting the sensory evaluation, all the samples were placed at room temperature for 2

h.

Plain soymilk samples were prepared for descriptive sensory evaluation and

physical and chemical properties measurement. Flavored soymilk samples were provided

for consumer acceptance sensory evaluation.

Flavored soymilk samples were prepared at the same ratio as the samples in the

descriptive sensory evaluation. In addition, 5% (w/w) sugar and 0.03% (w/w) vanilla

extract were added into the plain soymilk. The procedure for the flavored soymilk

samples was the same as the plain soymilk samples.

3.1.2 Soy Ice Cream Samples Preparation

Raw materials of soy ice cream were sugar (local grocery store), lecithin powder

(ADM, Decatur, IL), xanthan (Kelco, Chicago, IL), guar gum (Bob’s Red Mill,

Milwaukie, OR), pure vanilla extract (local grocery store), soybean oil (local grocery

27

store) and plain soy milks made from five varieties of beans described in the last

paragraph.

Soy ice cream samples in this study did not contain any dairy ingredient. They

were made by the five soy milk samples separately. All the solid ingredients, including

12% (w/w) sugar, 5% (w/w) lecithin, 0.15% (w/w) xanthan and 0.15% (w/w) guar gum

were mixed together and added into soymilk (74.4% w/w), then followed by adding 0.3%

(w/w) extract vanilla flavor into the system. After these ingredients were mixed well, 8%

(w/w) soybean oil were added and the mix was homogenized by using a lab homogenizer

(T25 digital ULTRA TURRAX®, IKA, Wilmington, NC) for 15 min. The speed of

homogenizer was set at 21,000 rpm. Next step was pasteurization. Each mix was

transferred into a 1.5 quart sauce pan, and heated to ca. 75°C for 20 s. The mixes were

cooled to room temperature, and then stored at 4°C overnight for ageing. Each aged mix

was poured into a homemade ice cream maker (a small freezer, 4 Quart Plastic Bucket

Ice Cream Maker, WESTBEND, West Bend, WI). The soy ice creams were packed in

500 mL plastic containers with lids and stored at -18°C for hardening.

3.2 Analytical Methods

3.2.1 pH Value

The pH value of five soymilk samples and five soy ice cream mixes was

determined with a pH meter (S20 SevenEasy TM

pH, Mettler Toledo, Columbus, OH).

Soymilk samples were placed in room temperature for 2 h before measured. Soy ice

cream mixes were stored in -4°C overnight, and measured at room temperature.

28

3.2.2 Color

The color of five soymilk samples and five soy ice cream mixes was measured

with a colorimeter (Konica Minolta CR-410, Mahwah, NJ) at room temperature. L*, a*,

b* represent lightness, redness and yellowness, respectively. L* ranges from 0 to 100.

Positive readings of “a*” indicate redness, while negative readings indicate greenness.

Positive readings of “b*” mean yellowness of the sample, and negative readings mean

blueness. Three readings were taken from each sample. The second and third readings

were taken after rotating the sample 90 degrees.

3.2.3 Viscosity

3.2.3.1 Viscosity for Soymilk

Physical viscosity of the soymilk samples was measured in triplicate at room

temperature. A HAAKE RS100 RheoStress Viscometer (HAAKE Buchler Instruments,

Paramus, NJ) was used and the rotation speed was 100 s-1

. In addition, the gap between

the two parallel plates was 0.5 mm. The diameter of top plate was 35 mm, and the

diameter of bottom plate was 35 mm.

3.2.3.2 Viscosity for Soy Ice Cream Mix

The viscosity of the aged mixes was measured using a concentric cylinder

viscometer (HAAKE, VT550 ThermoMC, Brookfield, Stoughton, MA), with a

cylindrical cone (60 mm of diameter) and a cylinder (41 mm of diameter). A thermostat

(TC-602, Brookfield, Stoughton, MA) adjusted the temperature of the sample.

Approximately 15 mL of sample were placed at 4 ± 0.3°C for 5 min, then subjected to a

program that increased the shear rate from 2.52 to 19.96 s-1

over 4 min. The power law

29

model was used to determine the flow behavior index and the consistency coefficient.

The power law was calculated as follow.

[τ is the shear stress (Pa), K the consistency index (Pasn), γ the shear rate (s

-1), and n is

the flow behavior index.]

3.2.4 Overrun

An overrun measurement was taken per sample by comparing the weight of ice

cream mix and ice cream in a 100 mL beaker. Overrun (%) was calculated as follows:

3.2.5 Melting Rate

A sample of ice cream was cut from the quarter gallon container, and weighed

100 to 120 g. The sample of ice cream (initially at -18°C) was placed on a wire screen

(around 8 holes per cm) on top of a funnel that was attached to a 50 mL beaker. The ice

cream was placed in a room temperature environment at 25°C. Every 5 min, for up to 90

min, the dripped weight was recorded (Muse and others 2004).

3.2.6 Hardness

Hardness of ice cream after 3 days at -18°C was measured by using a TA-HDi

Texture Profile Analyzer (Texture Tech. Corp., NY). The probe dimensions were 0.3 cm

in diameter and 15 cm in height. Before the tests, the samples were tempered at -14°C

overnight. The conditions for the analysis were as follows: 50 mm penetration distance

30

and probe speeds were 2 mm/s pre-penetration, 3 mm/s during penetration, and 5 mm/s

post-penetration (Pereira and others 2011). The hardness was measured as the peak

compression force (N) during the penetration of the sample. Three measurements were

taken from each sample and the results averaged.

3.2.7 Total Solids Content

The total solids content of soymilk samples was determined by drying 5 g of

samples in an air oven at 105ºC for 12 h. Total solids content was calculated as follows:

3.2.8 Fat Content

Fat content determination was according to the Modified Mojonnier Ether

Extraction Method (AOAC Official Method 989.05. 2007).

Dry weighing dishes were numbered and the weights were recorded.

Approximately 10 g soymilk sample was pipeted into a flask. One and half mL NH4OH

(Fisher Scientific, Fair Lawn, NJ) was added into the flask and the solution was mixed

well to neutralize any acid present. Three drops of phenolphthalein indicator

(phenolphthalein solution with alcoholic 1.0% volumetric indicator, Fisher Scientific,

Fair Lawn, NJ) were added into the solution to help sharpen the visual appearance of

interface between ether and aqueous layers during extraction. Ten mL 95% alcohol

(Fisher Scientific, Fair Lawn, NJ) were added into the flask and the flask was shaken

about 15 s. For the first extraction, 25 mL ethyl ether (Fisher Scientific, Fair Lawn, NJ)

were added and the flask was shaken about 1 min, then 25 mL petroleum ether (Fisher

31

Scientific, Fair Lawn, NJ) were added into the solution, and the flask was centrifuged at

600 rpm for 30 s (Models 5804R, Eppendorf 15 Amp Centrifuge, Hauppauge, NY) to

obtain clean separation of the aqueous and ether phases. Ether solution was decanted into

a weighing dish.

For the second extraction, 5 mL 95% alcohol were added into the aqueous, the

flask was shaken about 15 s. Next, 15 mL ethyl ether were added into the solution, the

flask was shaken about 1 min. Fifteen mL petroleum ether were placed in the solution,

and then the flask was centrifuged at 600 rpm for 30 s. For the third extraction, the steps

of the second extraction were repeated. Next, weighing dishes were placed in a hood

overnight to evaporate solvents. The weights of each weighing dish plus fat were

recorded. Fat content (%) was calculated as follows:

3.2.9 Protein Content

Protein content determination was according to the Mirochemical Determination

of Nitro-Micro Kjeldahl Method (AOAC Official Method 960.52. 2007).

Approximately 100 mg of soymilk sample were weighed and placed into a

digestion flask with about 40 mg CuSO4·10H2O (Fisher Scientific, Fair Lawn, NJ), about

1.9 g K2SO4 (Fisher Scientific, Fair Lawn, NJ), and 4 to 5 boiling chips (No.10 Sieve).

Ten mL 95%-98% H2SO4 (Sigma-Aldrich, Chemical Company, Milwaukee, WI) were

added to rinse any sample on the neck of the flask into the bulb. The digestion flask was

32

placed on a heating device for 2 h, until digestion completely finished (color was shown

as clear with light blue-green color). After the whole system was cooled down to room

temperature, the system was diluted into a 50 mL volumetric flask with distilled water up

to the 50 mL. Next, ten mL of the solution in the volumetric flask were taken out and

prepared for the distillation.

Five mL H3BO3 (2% (w/v) aqueous solution with mixed indicator, HMIS,

Arlington, TX) were placed in a 125 mL Erlenmeyer flask under a condenser with the tip

extending below the surface of the H3BO3 solution. The digestion solution and 10 mL 50%

NaOH (Fisher, Scientific, Fair Lawn, NJ) solution were added to the still, about 20 mL

distillate (indicator color changed from blue to green) were collected by the distillation

apparatus (Labcon North America, Petaluma, CA). The distillate was titrated by 0.05 M

HCl (Fisher, Scientific, Fair Lawn, NJ) to the end point (color changed from green to

purple). The volume of used HCl solution during titration was recorded. Blank was

determined. The test was duplicated for each treatment. Nitrogen content (%) was

calculated as follows:

Nitrogen constitutes 16% of most proteins and, hence, the total nitrogen content

of a sample was multiplied by 6.25 to arrive at the value of the crude protein.

33

3.3 Descriptive Sensory Evaluation for Soymilk

3.3.1 Training of Panelists

Twelve judges (nine female and three male) from the University of Missouri-

Columbia, all with a food science background, were selected for this descriptive sensory

evaluation. In the first training session, the leader gave a brief introduction about the

methodology and the procedure for making the soy milk samples. Meanwhile, five

soymilk samples were given to each of the judges. After the introduction, judges were

asked to taste the samples and then describe the sensory characteristics of these products

using their own language. After completion their individual task, judges shared their first

feeling about the soymilk samples, and decided collectively on an original list of

attributes describing these samples (the initial list of attributes is shown in Table 3.1).

Some references were prepared based on previous studies (Torres-Penaranda and others

1998 and Torres-Penaranda and others 2001 and Chamber and others 2006). From the

second to the fifth training session, the sensory leader provided all the references of each

attribute to help panelists correct the definition and terminology for all the attributes. In

the sixth training session, judges selected a final list of attributes (shown as Table 3.2)

and did a pre-test. According to the results of the pre-test, three panelists needed more

individual training sessions focusing on practicing the ABX test which belongs to the

discrimination test.

3.3.2 ABX Test

Panelists who need to do the ABX test finished the test individually. During the

ABX test, three samples (A, B and X) were given to each panelist. The sensory leader

34

told the panelists that the first two samples (A and B) were different, and asked each

panelist to identify which one was the same as the third one (X). This test was used to

train them to discriminate the specific attribute which they were not familiar with. The

test was replicated three times.

3.3.3 Descriptive Sensory Evaluation

Forty mL of the samples and references were presented in 3-oz white plastic cups

coded with three-digit numbers with lids on at room temperature (KB10-23#1681 was

represent by 177, KB10-5#1137 was represent by 742, KB10-25#1235 was represent by

882, KB09-5(2) #15 was represent by 216, and 534545 was represent by 529). Judges

were requested to record their responses on a 15 cm intensity line scale. They were

required to taste the reference first to perceive the intensity of the reference, and then to

taste and swallow the samples, and put the vertical marker on the 15 cm scale for one

attribute. After finishing one attribute, they were asked to rinse mouth by water (and

crackers, if necessary). After tasting each sample, they were instructed to rinse mouth by

water (and crackers, if necessary) and to take a break for at least 5 min. The order of

samples for each panelist was random. Panelists were asked to do three replicates of each

soymilk sample.

3.3.4 References Preparation

1% sucrose solution: 4 g sugar (local grocery store) was weighed and dissolved in

a 500 mL beaker with 400 mL distilled water.

2% sucrose solution: 8 g sugar was weighed and dissolved in a 500 mL beaker

with 400 mL distilled water.

35

0.02% caffeine solution: 0.08 g caffeine (Sigma, St. Louis, MO) was weighed and

dissolved in a 500 mL beaker with 400 mL distilled water.

0.04% caffeine solution: 0.16 g caffeine was weighed and dissolved in a 500 mL

beaker with 400 mL distilled water.

0.05% alum solution: 0.1 g alum (McCormick, Sparks, MD) was weighed and

dissolved in a 500 mL beaker with 400 mL distilled water, and then slightly

warmed with a stirring hot plate.

0.1% alum solution: 0.4 g alum was weighed and dissolved in a 500 mL beaker

with 400 mL distilled water, and then slightly warmed with a stirring hot plate.

0.2% alum solution: 0.8 g alum was weighed and dissolved in a 500 mL beaker


hexanal in oil (50 ppm): 12.5 mg of hexanal (Sigma-Aldrich Chemical Company,

Milwaukee, WI) was weighed and dissolved in a 20 mL beaker with soybean oil

(local grocery store). The solution was the transferred to a 250 mL volumetric

flask and rinsed 3 times and brought to the volume with soybean oil.

5% rice solution: 50 g of rice (local grocery store) was weighed and placed in a

blender (6640-022, 10 Speed Oster blender, Boca Raton, FL) with 500 mL

distilled water for 5 min. The rotate speed of blender was set at high speed-Grind.

After blending, the solution was transferred to a 1000 mL beaker and rinsed 3

times with 500 mL distilled water and stirred well. Supernatant was used as

reference during sensory evaluation.

10% rice solution: 100 g of rice (local grocery store) was weighed and placed in a

blender with 500 mL distilled water for 5 min. The rotate speed of blender was set

36

at high speed-Grind. After blending, the solution was transferred to a 1000 mL

beaker and rinsed 3 times with 500 mL distilled water and stirred well.

Supernatant was used as reference during sensory evaluation.

1.67% starch solution (unheated): 6.68 g starch (local grocery store) was weighed

and dissolved in a 500 mL beaker with 400 mL distilled water, and then stirred

the solution on a stirring hot plate for 10 min, without heating.

1.67% starch solution (heated): 6.68 g starch was weighed and dissolved in a 500

mL beaker with 400 mL distilled water, and then stirred and warmed with a

stirring hot plate to 90 °C for 10 min.

1% mushroom solution: 5 g dry mushroom (local grocery store) was weighed and

placed in a blender with 500 mL distilled water for 2 min, and the speed of the

blender was set at high speed-Blend. After blending, placed the mushroom

solution in a 600 mL beaker for 2 h. Supernatant was used as reference during

sensory evaluation.

3% mushroom solution: 15 g dry mushroom was weighed and placed in a blender

with 500 mL distilled water for 2 min, and the speed of the blender was set at high

speed-Blend. After blending, placed the mushroom solution in a 600 mL beaker

for 2 h. Supernatant was used as reference during sensory evaluation.

0.3% guar gum: 1.2 g guar gum was weighed and dissolved in a 500 mL beaker


0.4% guar gum: 1.6 g guar gum was weighed and dissolved in a 500 mL beaker


37

0.03% citric acid: 0.12 g citric acid (Sigma-Aldrich Chemical Company,

Milwaukee, WI) was weighed and dissolved in a 500 mL beaker with 400 mL

distilled water.

0.04% citric acid: 0.16 g citric acid was weighed and dissolved in a 500 mL

beaker with 400 mL distilled water.

References were prepared and completed in a food-grade lab, and were stored in a

refrigerator at 4°C for 24 h. Before doing the sensory evaluation, all the references were

placed at room temperature for 2 h.

3.4 Consumer Acceptance Test for Soymilk

For the consumer acceptance test, the flavored soymilk samples were provided.

Seventy five panelists from the University of Missouri-Columbia including students and

staff participated in this consumer acceptance evaluation. They were instructed in a brief

orientation session about the evaluation and characteristics of the samples. They were

asked to indicate their hedonic responses on unstructured 9 points line scales, anchored

by extremely like and extremely dislike on each end. Thirty mL of soymilk were

provided in 3-oz white plastic cups coded with 3-digit numbers at room temperature. The

order of samples for each panelist was random.

3.5 Statistical Analysis

The data was analyzed by Analysis of Variance (ANOVA) and the GLM

procedure in SAS 9.3 software (SAS Institute Inc., NC) to determine significant

parameters factors for the physical and chemical properties.

38

For sensory evaluation, ANOVA method was used to test the significant

difference among the soymilk samples for each attribute. The assessment scores of each

attribute were regressed on soymilks (fixed effect), judges (fixed effect), the interaction

of soymilks by judges (fixed effect), and replicates by judges (random effect). Least

square estimates and p-values were extracted from SAS output.

Furthermore, PCA method was applied to the mean centered data to estimate

loadings of each attribute at each principle direction. In descriptive sensory studies,

panelists are usually asked to evaluate several attributes of the samples. It is very possible

that not only one attribute has significantly influence on the samples, but also several

descriptors describe the same characteristic of the samples. For example, when sensory

panelists evaluate an ice cream sample on both aroma and flavor aspect. The milky aroma

and dairy flavor of the sample might be redundant and affect the same attribute of the ice

cream. In this situation, when some dependent variables are correlated with others, PCA

is the suitable technique to use (Heymann and others 1989). The redundancies, which

have the linear relationships, will be transferred into the new space to form a new set of

dependent variables, called principal components (PCs). Products will have new values

on these new variables (PCs) just as the original attributes which evaluated by judges.

The first principal component treats at the first eigenvalue and explains the largest

possible of the total variability in the data set. The following PCs demonstrate smaller

amounts of the total variance in the data set and have no interrelationships with prior PCs,

which mean that each PC is vertical to others (Lawless and others 2010; Heymann and

others 1989). If all the PCs are reserved, the PCA will act as a method that data are 100%

transferred. However, in fact, the major goal of PCA is amounting for as much of the

39

variability of the original data as possible by using as few of PCs as possible

(Borgognone and others 2001). Consequently, sensory scientists only analyze the first

few PCs to explain the main variance in the data set. In general, when the amount of

variance reaches 80% can be accepted (Kaiser 1960).

PAC is extensively used on sensory evaluation. A few examples are Owusu and

others (2011), Heenan and others (2010), Ghosh and Chattopadhyay (2012), Resano and

others (2010), He and others (2009), and Duart and others (2010). In addition, PCA can

be used to create an external preference mapping, which provides a revelation of the

relationships between the product samples and the individual characteristics (Lovely and

others 2009; Schmidt and others 2010).

40

Table 3.1 Initial list of attributes of soy milk samples

Attributes Reference and intensity

Flavor Sweetness 1% sucrose solution = 4.0

2% sucrose solution = 7.5

Bitter 0.02% caffeine solution = 4.0

0.04% caffeine solution = 7.5

Astringency 0.05% alum solution = 4.0

0.1% alum solution =7.5

Beany fresh bean sprout =7.5

hexanal in oil (50 ppm) = 5.0 (smell)

Rice-like 5% rice solution = 4.0

10% rice solution = 7.5

Oily Wesson vegetable oil =5.0 (smell)

Starchy 1.67% starch solution (heated) = 5.0

1.67% starch solution (unheated) = 7.5

Earthy 5 g ground mushroom soak in 500 mL

distilled water for 2 h = 4.0

15 g ground mushroom soak in 500 mL

distilled water for 2 h = 10.0

Mouth-

feel

Mouth Coating whole milk = 7.5

Teeth-etch 0.2% alum solution =7.5

Thickness 0.3% guar gum = 4.0

0.4% guar gum = 7.5

Silky Betty Crocker whipped butter cream

frosting = 10.0

41

Aftertaste Sourness 0.03% citric acid = 4.0

0.04% citric acid = 7.5


42

Table 3.2 Final list of attributes of soy milk samples

Attributes Reference and intensity

Flavor Sweetness 1% sucrose solution = 4.0

2% sucrose solution = 7.5


0.04% caffeine solution = 7.5

Astringency 0.05% alum solution = 4.0

0.1% alum solution =7.5

Beany fresh bean sprout =7.5

Rice-like 5% rice solution = 4.0

10% rice solution = 7.5

Mouth-feel Thickness whole milk = 7.5

Graininess 0.05% guar gum = 4.0

0.1% guar gum = 7.5

43

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Soymilk Fat, Protein, and Total Solids Contents

Fat content and protein content of each sample did not show significant difference

(Table 4.1). For fat content, the range was from 2.09 to 2.14%. Sample KB10-5#1137

was slightly higher than others because of its high oleic acid amount. The range of

protein content was from 3.55 to 3.96%. Sample 534545 contained the highest amount of

protein compared to others. However, there were significant differences among samples

for total solids content. Sample KB10-5#1137 and sample 534545 contained significantly

more total solids than others. Because sample KB10-23#1681 contained ultra-low

amount of RFOs, which belong to oligosaccharides; sample KB10-25#1235 was made by

low RFOs and lipoxygenase free soybean; sample KB09-5(2)#15 not only contained low

amount of RFOs, but also contained low amount of P34 allergen and phytate. In addition,

Table 4.2 shows that the soluble carbohydrate content of each soybean was significantly

different (data provided by Dr. Bilyeu). The sucrose content and stachyose content of

sample 534545 was significantly higher than others that may contribute to the higher total

solids contents. Since the sucrose content determinations were finished by two batches,

the sample KB10-5#1137 was lower than others and raffinose and stachyose content were

not available. pH value of soymilk samples were shown in Table 4.3. The lowest pH

value was 6.54 from sample KB10-23#1681, and the highest pH value was 6.69 from

sample KB09-5(2)#15. However, there was no significant difference among samples.

44

Table 4.1 Composition of Soymilk Samples

Name of

soymilk

samples

RFOs Lipoxyge

-nase

P34

Allergen

Fatty

acids

Fat

(%)

Protein

(%)

Total

Solids

(%)

KB10-23#1681

Ultra-

low

RFOs

2.11a 3.91

a 4.72

b

KB10-5#1137

High

Oleic

Acid

2.14a 3.61

a 4.80

a

KB10-25#1235 Low

RFOs

l×1 l×2 l×3

null 2.13

a 3.55

a 4.76

ab

KB09-5(2)#15 Low

RFOs

Low

P34

Allergen

2.09a 3.63

a 4.54

b

534545 (Clean

Hilum) 2.11

a 3.96

a 4.92

a

*Means sharing the same letter within the same column do not differ at p < 0.05

45

Table 4.2 Soluble carbohydrate content of soybean lines (% of seed weight)

Soybean Lines Sucrose Raffinose Stachyose

Williams 82 (Commercial

soybeans) 6.7

c 1.0

a 5.6

a

KB10-23#1681 9.5a 0

d 0.3

c

KB10-5#1137 5.3 Not

Available

Not

Available

KB10-25#1235 8.3b 0.1

c 1.8

b

KB09-5(2)#15 8.3b 0.1

c 1.8

b

534545 10.0a 0.9

b 5.5

a


4.2 Soymilk Color

Color is an important property of food products. It relates to the quality of

products and could affect consumer acceptance. Generally speaking, consumers of

soymilk expect the product to have a pale yellow color. Therefore, commercial

manufacturers tend to maintain this natural color of soybean without adding color agent.

In this research, natural soybeans were used and no color agent added. Average of L, a, b

value were shown in Table 4.3.

Lightness, represented as L* value of soymilk samples varied between 86.90 and

96.76. The highest was from the sample 534545, which was significantly different from

others. In sample 534545, there was no hilum on the skin of soybeans, which usually is

black color, so that contributed to lightness of soymilk sample 534545. There was no

significant difference in redness (a* value) among each sample. Yellowness is

represented as positive value of b*. The range of yellowness was from 17.58 to 18.18.

46

There was significant difference between low RFOs group (sample KB10-23#1681,

sample KB10-25#1235, and sample KB09-5(2)#15) and regular RFOs content group

(sample KB10-5#1137 and sample 534545). Since the content of raffinose was low in

these soybeans, the content of raffinose polyester produced by raffinose and long chain

fatty acid may decrease. Raffinose polyester contributes to the yellowness of soymilk and

soybean oil (Akoh and Swanson 1987). As a result, these three soymilks had less yellow

color.

Table 4.3 pH value and color of soymilk samples

Samples pH Value Color

L* a* b*

KB10-23#1681 6.54a 86.90

b -3.27

a 17.58

b

KB10-5#1137 6.59a 87.92

b -3.23

a 18.18

a

KB10-25#1235 6.61a 91.65

b -3.34

a 17.63

b

KB09-5(2)#15 6.69a 87.36

b -3.24

a 17.66

b

534545 6.64a 96.76

a -3.29

a 17.99

a

*Means sharing the same letter within the same column do not differ at p < 0.05.

4.3 Soymilk Viscosity

Viscosity is an essential attribute for beverages. In commercial soymilk,

emulsifiers and stabilizers, such as guar gum, κ-carrageenan, and xanthan gum, may be

added to improve the quality of soymilk by promoting its thickness. The viscosity of

commercial soymilk is usually around 4.7 cP (20°C, 60 rpm). Some studies even

indicated increasing soymilk’s viscosity by 5 cP might significantly lower the astringency

flavor in soymilk (Courregelongue and others 1999). The mean values of viscosity of

47

soymilk samples were shown in Table 4.4. The range of viscosity was from 3.2 to 5.1 cP.

Sample KB10-5#1137 was significantly thicker than others, and sample KB09-5(2)#15

was the least viscous soymilk. Sample KB09-5(2)#15 contains low concentration of

raffinose, phytic acid and P34 allergen, and it also had least total solids content.

Therefore, the low amount of total solids content may contribute to low viscosity. The

thickest texture of sample KB10-5#1137 may be due to the oleic acid composition and

the molecular conformation of the triacylglyceride molecules. A cis double bond, present

in a long chain fatty acyl molecule, may lead to a bend or kink of the chain. Therefore,

the longer the chain of the fatty acyl groups, the more viscous the soymilk. The same

result was proved by Wang and Briggs (2002) in different soybean oils with modified

fatty acid compositions.

Table4.4 Viscosity of soymilk samples

Samples Apparent Viscosity (cP)

KB10-23#1681 4.1b

KB10-5#1137 4.9a

KB10-25#1235 4.3b

KB09-5(2)#15 3.2c

534545 4.0b

*Means sharing the same letter do not differ at p < 0.05

48

4.4 Descriptive Sensory Evaluation for Soymilk

When developing existing food products with new ingredients or new compounds,

it is critical to improve or at least maintain the sensory quality of the new one. If a new

compound or formulation fails to appeal to consumers, the new product will be

considered as an unsuccessful development. Therefore, using sensory evaluation to test or

monitor the potential changes of new products is very important. Descriptive sensory

evaluation, ABX test (for training step), and consumer acceptance test were used in this

study to determine changes in sensory attributes in the soymilk samples.

The ANOVA method helps to identify noticeable difference among soymilks for

each attribute. The ANOVA method yielded least square estimates for each combination

of soymilk and attribute, and they are listed in Table 3.2. The p-values for testing

significance among different soymilks are summarized in row 7 in Table 4.5. P=0.1 was

used as the cutoff for type I errors since it is not critical error to accept an attribute as

significant at this level. ANOVA method identified attributes “Beany” (p<0.0001),

“Rice-like” (p=0.054), “Sweetness” (p=0.078), and “Thickness” (p=0.082) to be

significant. Specifically, soymilk made by soybean KB10-25#1235 is least beany flavor;

soymilk sample KB10-25#1235 and KB10-5#1137 have most rice-like flavor among all;

soymilk samples 534545, KB10-23#1681, and KB09-5(2)#15 are sweeter than soymilk

samples KB10-5#1137 and KB10-25#1235; soymilk samples KB10-5#1137 is thicker

than others, and KB09-5(2)#15 is least thick among all. For other attributes, “Bitter”,

“Astringency”, and “Graininess”, there were no significant differences.

The possible reason for least beany flavor in KB10-12#1235 sample might be that

this sample was lipoxygenase free. The beany flavor components are mainly the

49

oxidation products of unsaturated lipids catalyzed by lipoxygenases (Yuan and others

2007). When no lipoxygenase exist in soybeans, polyunsaturated lipids will not be

catalyzed to produce the undesirable flavor. The result was proved in some other’s

studies (Torres-Penaranda and others 1998; Torres-Penaranda and others 2001;

Kobayashi and others 1995). During the cooking step, temperature reached 100 °C, the

high amount of oleic acid in sample KB10-5#1137 may degrade into octanal, heptanal,

nonanal, and decanal, which may contribute to rice-like flavor (Yang and others 2008).

The soymilk sample KB10-25#1235 has the highest result of rice-like flavor may due to

its least beany flavor. Since no obvious beany flavor cover rice-like flavor, panelists may

easily distinguish rice-like flavor. For the sweetness attribute, soymilk samples 534545

and KB10-23#1681 were the sweetest two samples, which got the same results on

sucrose content (Table 4.2, provided by Dr. Bilyeu). In sample 534545 and sample

KB10-23#1681, the sucrose contents were significantly higher than others including

William82, a commercial soybean lines. Although the sucrose content of KB09-5(2) was

significantly lower than sample 534545 and sample KB10-23#1681, the sensory result on

sweetness did not show significant difference between sample KB09-5(2)#15 and sample

534545 and no significant difference between sample KB09-5(2)#15 and sample KB10-

23#1681 either. This result may be due to sensitivity of panelists. The 1.7% difference of

sucrose content can be determined by apparatus and equipment, but it may not be easily

distinguished by human. Sample KB10-5#1137 is the thickest sample among others. This

may be due to its high oleic acid content. The high molecular weight of oleic acid (C18:0)

may cause the viscosity increase leading to the increase in the thickness of the soymilk

sample. The other possible reason is oleic acid could be considered as an emulsifier.

50

Soymilk is an emulsion system, contains carbohydrate, protein, lipid, and sugar

(Lakshmanan and others 2006). Increasing long-chain fatty acid molecules (oleic acid)

content may have same effect of adding emulsifiers (Matsumiya and others 2011).

The PCA method studies the relationships among attributes to analyze the

descriptive sensory data. The results show that the first four principal directions

explained the majority of the variation (80%) in the observations, PC1 explained 35.72%

of the variance in the data set, PC2 explained 18.40%, PC3 explained 15.84%, and PC4

explained 10.76% (Table 4.6). This suggests that the first four principal directions

explain the majority relationships of the seven attributes in the study.

51

Table 4.5 Sensory data with significance values from ANOVA and jack-knife estimates

Soymilk Sweetness Bitter Astringency Beany Rice-

like Thickness Graininess

KB10-

23#1681 2.813

a 3.434

ab 3.630

a 5.538

a 3.400

b 4.796

a 7.105

a

KB10-5#1137 1.792b 3.700

ab 3.841

a 5.607

a 4.155

ab 4.857

a 6.978

a

KB10-

25#1235 2.173

ab 3.752

a 3.671

a 3.828

b 5.175

a 4.814

a 7.486

a

KB09-5(2)#15 2.807a 2.534

b 2.888

a 5.670

a 4.159

ab 3.377

b 7.160

a

534545 2.830a 3.247

ab 3.449

a 5.594

a 3.762

ab 4.114

ab 6.963

a

S.D. 0.48 0.49 0.37 0.79 0.66 0.65 0.21

p(ANOVA) 0.078 0.138 0.107 <0.0001 0.054 0.082 0.958

p(PC1) 0.048 0.013 0.259 0.021 0.000 0.006 0.070

p(PC2) 0.340 0.067 0.058 0.287 0.405 0.491 0.272

p(PC3) 0.176 0.415 0.455 0.269 0.159 0.136 0.098

p(PC4) 0.281 0.013 0.312 0.077 0.007 0.443 0.407

* Pairwise comparisons use Scheffe’s multiple test adjustment at p-value=0.1.

52

Table 4.6 Variance explained by the seven principal directions

Variance Explained (%) Cumulative Variance (%)

PC1 35.72 35.72

PC2 18.40 54.12

PC3 15.84 69.96

PC4 10.76 80.71

PC5

PC6

PC7

7.44

6.55

5.30

88.15

94.70

100.00

Figure 4.1 is the scatter plot of scores for principal directions 1 and 2. The

principal direction 1 explains 36% of the variation compared to the principal direction 2

that explains 18% of the variation. The square symbol with a cross sign inside represents

scores for soymilk KB10-25#1235, and the plus sign denotes scores for other soymilks.

The arrow denotes the direction of “beany” attribute projecting on the 2-D plane. The

scores from KB10-25#1235 soymilk are at the opposite space compared to the “beany”

direction, which suggests that KB10-25#1235 is less beany than other soymilks. This

result is consistent with the ANOVA test.

53

Figure 4.1 Scores on principal direction 1 and 2 from PCA on mean centered data

Figure 4.2 is the scatter plot of loadings of all seven attributes for the principal

components 1 and 2, i.e. the projection of the directions of the seven attributes to the 2-D

plane spanned by the principal directions 1 and 2. The principal direction 1 was

dominated by “Beany”, “Sweetness”, “Thickness”, and “Rice-like”. The principal

direction 2 was controlled by “Astringency”, “Bitter”, and “Graininess”. By applying the

jack-knife estimation method, the significance of the loadings for each attribute for the

first four principal directions was tested. This is because the first four principal directions

explain the majority of the variation in observations. The p-values from tests are

summarized in Table 4.5. Specifically, the variation at the direction of principal

-4 -2 0 2 4 6

-4-2

02

46

Scores, Prin1 36%

Sco

res,

Prin2

18%

Beany

KB10-25

others

KB10-25

others

KB10-25

others

54

component 1 is explained by (at the significance level 10%) attributes “Sweetness”,

“Bitter”, “Beany”, “Rice-like”, and “Thickness”. Variation for principal direction 2 is

explained by attributes “Bitter” and “Astringency”. Similarly, principal direction 3

identifies attribute “Graininess”, and principal direction 4 identifies attributes “Bitter”,

“Beany”, and “Rice-like” as significant. Although “Beany” and “Rice-like” significantly

influence the same principal components, they are of opposite directions, which mean the

seven attributes in the study each represents a unique taste attribute of soymilk.

Figure 4.2 Loadings on principal direction 1 and 2 from PCA on mean centered data

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Prin1 36%

Prin

2 1

8%

Sweetness

Bitter

Astringency

Beany

RicelikeThickness

Graininess

55

4.5 Consumer Acceptance Test for Soymilk

Soymilks made from five lines of soybeans were evaluated by 75 consumers.

Flavored soymilk samples with 5% (w/w) sugar and 0.03% (w/w) vanilla extract were

provided. The sensory properties of soymilks are shown in Table 4.7. There were no

significant differences of appearance and flavor among soymilk samples. In this study,

the sugar and vanilla extract may cover the beany flavor, rice-like flavor, and other

flavors so that consumers may not be able to distinguish the flavor property among the

soymilk samples. In addition, the light in each sensory booth may be different, so the

appearance characteristic may be influenced. However, there were significant differences

of overall and mouth-feel. Sample KB10-5#1137 had the highest score on mouth-feel

aspect. Combining with the result from descriptive sensory evaluation that sample KB10-

5#1137 was the thickest sample which may be preferred by consumers. As a result, the

overall score of sample KB10-5#1137 was the highest.

56

Table 4.7 Consumer acceptance of soymilk samples (Score as 9 = extremely like, 1 =

extremely dislike).

Samples Overall Appearance Flavor Mouth-Feel

KB10-23#1681 5.60b 6.57

a 5.51

a 5.54

b

KB10-5#1137 6.12a 6.44

a 5.28

a 6.03

a

KB10-25#1235 5.43b 6.53

a 5.32

a 5.46

b

KB09-5(2)#15 5.74b 6.48

a 5.43

a 5.41

b

534545 5.75b 6.81

a 5.28

a 5.60

b


4.6 Soy Ice Cream Color

The soy ice cream made from sample 534545 was significantly higher than others

in lightness, because there was no hilum on the skin of soybeans, which usually is black

color, so that contributed to lightness of the soy ice cream. However, there was no

significant difference in redness and yellowness between each sample. Although there

was significant difference of yellowness among the soymilk samples, adding lecithin,

which is yellow powder, might weaken the influence coming from oleic acid.

57

Table 4.8 Color of soy ice cream mixes

Samples Color

L* a* b*

KB10-23#1681 84.14b -2.50

a 27.96

b

KB10-5#1137 83.61b -2.87

a 28.68

a

KB10-25#1235 82.79b -2.63

a 27.26

b

KB09-5(2)#15 83.27b -2.38

a 27.05

b

534545 88.99a -1.88

a 29.59

a


4.7 Soy Ice Cream Texture Properties

Texture properties relate to the mouth feel of ice cream. The texture

characteristics of soy ice cream samples were determined by measuring the viscosity of

the mixes (Table 4.9), overrun, hardness (Table 4.10), and melting rate (Figure 4.3) of

soy ice creams. Sample KB10-5#1137 was significantly viscous than others. Since all of

the soy ice creams were added 12% (w/w) sugar, 5% (w/w) lecithin, 0.15% (w/w)

xanthan and 0.15% (w/w) guar gum, the only variance was the variety of soybeans, the

main reason that affect the viscosity of soy ice cream mixes may be as same as the

viscosity of soymilks. Though xanthan and guar gum are worked as emulsifier and

stabilizer to increase the viscosity of the mixes, sample KB10-5#1137 contained high

amount of oleic acid so that the double bond from oleic acid, present in a long chain fatty

acyl molecule, may cause a chain bending or chain kinking. Therefore, the viscosity of

soy ice cream mix from sample KB10-5#1137 was higher than other mixes.

58

Table 4.9 Viscosity of soy ice cream mixes

Samples Apparent Viscosity (cP)

KB10-23#1681 894.87b

KB10-5#1137 994.54a

KB10-25#1235 932.78b

KB09-5(2)#15 886.24b

534545 922.52b

*Means sharing the same letter do not differ at p < 0.05

Air is a critical component in ice cream affecting the physical and sensory

properties as well as the storage stability. Overrun which represents air bubble content,

affects to the creamy properties and the physical properties of melt down and hardness of

ice cream. Overrun value equals 100% means that the air makes up 50% of the ice cream

volume. The higher overrun value, the creamier texture the ice cream. Previous studies

showed that high apparent viscosity provided low overrun after whipping, which means

that if the mix is viscous, it might decrease air incorporation into ice creams (Kim and

others 2013). In this study, although milk was replaced by soymilk to make ice creams,

the same result was obtained. The mix of sample KB10-5#1137 was significantly thicker

than others and the overrun of this sample was significant lower than that of other soy ice

creams.

Hardness and melting down properties are influenced by the overrun, size of the

ice crystals, ice phase volume, and fat globule destabilization (Sofjan and Hartel 2004).

The range of hardness was from 7500.40 to 10985.27 N. Sample KB10-5#1137 was

significantly harder than other samples, and sample 534545 was significantly softer than

59

others. The lower overrun led to the harder soy ice cream, while the higher overrun led to

the softer soy ice cream. Furthermore, there was no soy ice cream samples melted before

30 min. Sample KB10-5#1137 started melting down at the 35 min and melted 78.3% of

sample during the 90 min period, while sample 534545 started melting down at the 50

min and melted only 32.3% of sample during the 90 min period. Samples were shown

significantly different among each other from the 35 min, and at the 90 min sample

KB10-5#1137 significantly melted more than others (data is shown in Appendix 8). The

lower overrun led the ice creams to melt more quickly. Muse and Hartel (2004) obtained

the same result in regular dairy ice cream. One possible reason is that high overrun could

contribute to a reduced rate of heat transfer due to a larger volume of air. Consequently,

for soy ice creams in this study, the high overrun related to soft texture and low melting

rate; the low overrun related to hard texture and high melting rate.

Table 4.10 Overrun value and hardness of soy ice cream samples

Samples Overrun (%) Hardness (N)

KB10-23#1681 26.18bc

9301.82b

KB10-5#1137 15.58c 10985.27

a

KB10-25#1235 33.94ab

8463.03b

KB09-5(2)#15 32.17b 8717.87

b

534545 38.30a 7500.40

b


60

Figure 4.3 Percentage of melting down of five soy ice cream samples

61

CHAPTER 5

CONCLUSION

The varieties did not significantly affect the fat content and protein content of the

soymilks, while sample 534545 was significantly higher than the other samples on total

solids content, because of its high sucrose content. In addition, sample KB10-5#1137 was

significantly higher than the other three samples on total solids content, but the reason is

not fully understood. Because of the high amount of fatty acid, sample KB10-5#1137 was

significantly more viscous than other soymilks. Since the soybeans that made sample

534545 did not have hilum on the seed skin, sample 534545 had the lightest color of the

soymilks. Moreover, samples that contained low RFOs concentration had a significant

lower yellow color than the samples that contained regular RFOs content. However,

there were no significant differences among samples on redness.

The results of descriptive sensory evaluation showed that lipoxygenase free

soymilk (sample KB10-25#1235) had the least beany flavor and most rice-like flavor.

High oleic acid content soymilk (sample KB10-5#1137) was the thickest one, due to its

fatty acid composition. Sample 534545 was the sweetest one. Consumer acceptance test

showed that sample KB10-5#1137 was the most accepted one. Though in descriptive

sensory analysis sample KB10-5#1137 was the least sweet one, the flavored soymilks

that were provided to consumers did not show any significant difference with other

samples on flavor and appearance aspects. In conclusion, even though sample KB10-

5#1137 showed some differences in the beany flavor attribute from other samples in the

descriptive analyses, there was significant difference in consumers’ preference. Therefore,

62

high oleic acid soymilk sample KB10-5#1137 can be considered an ideal formulation for

new soymilks.

The color of the soy ice creams showed the same result as soymilk color in that

the clear hilum sample was the lightest one, and low RFOs content samples had less

yellow color. High fatty acid content soy ice creams had higher viscosity and lower

overrun, due to a weak ability of air incorporation. Lower overrun led to hard and

unstable texture. Thus, sample KB10-5#1137 was the hardest soy ice cream and melted

more quickly than other samples. Although the high oleic acid soymilk got the best score

in the overall category from the consumer acceptance test due to its thicker mouth feel, it

did not show a good texture when making a soy ice cream.

Future studies could include:

1) To compare the five soymilk samples made from new soybean lines with the

commercial soymilk and cow milk;

2) To design a descriptive sensory evaluation of the soy ice cream samples;

3) To produce some other applications, such as tofu, tempeh, soy yogurt, etc and

investigate the effect of soybean lines on the physical, chemical and sensory

properties.

63

APPENDICES

APPENDIX 1. INFORMED CONSENT FOR CONSUMER

ACCEPTANCE TEST

I, (Date_________________) consent to participate in this research project and

understand the following:

PROJECT BACKGROUND: This project involves gathering human sensory data on

soy milk made from several varieties of soybeans. The data will be collected for analysis

and may be published. You must be at least 18 years of age to participate.

PURPOSE: The purpose of this sensory test is to study the beany flavor intensity and

consumer acceptance soy milks.

VOLUNTARY: This sensory test is entirely voluntary. You may refuse to answer any

question or choose to withdraw from participation at any time without any penalty or loss

of benefits to which you are otherwise entitled.

WHAT DO YOU DO? All participants of the sensory panel will attend a 5-min

evaluation session in which they taste several soy milks.

BENEFITS: Soy milk is a good source of protein and is lactose-free. For people who

are lactose intolerant or allergic to casein, soy milk is an excellent substitute of cow’s

milk. Drinking soy milk is often an acquired taste. To improve the acceptance of soy milk,

new varieties of soy beans are being developed to reduce the intensity of beany flavor in

soy milk. Your participation in this sensory test will facilitate the development of new

varieties of soy beans for soy milk application in the future.

RISKS: The expected risks are none other than those encountered in normal daily food

consumption. All products have either been prepared under sanitary conditions or are

commercially available products bought in a grocery store. If you know that you are

allergic to soy products (e.g. soybeans or soy proteins), you may NOT participate in this

study.

CONFIDENTIALITY: Your confidentiality will be maintained in that a participant’s

name will not appear on the ballot or in the published study itself. The data will only be

reported in aggregate form. Score sheets/data will be stored for a period of seven years

and then destroyed.

Thank you for your assistance in developing these soy milks. Although great

strides have been made in the instrumental analysis of foods, the development of new

foods still requires the human sensory response and feedback. Your efforts are greatly

appreciated. If you have any questions regarding the study, please contact Dr. Fu-hung

64

Hsieh at (573) 882-2444. If you have questions regarding your rights as a participant in

research, please feel free to contact the Campus Institutional Review Board at (573) 882-

9585.

Please keep one copy of this consent form for your records!

65

APPENDIX 2. CONSUMER ACCEPTANCE TEST

Date: .

This sensory test is about soy milk. Please swallow some of the soy milk at one time for

each sample, give the score of the sample, and then rinse your mouth with water to

continue to the next sample. Remember do not re-taste the sample during the first part of

this test.

First Part: Please choose one of the score in the forms below for each sample,

according to your experience.

Sample: #

Extremely

dislike

1

2 3 4

Neither

like or

dislike

5

6 7 8

Extremely

like

9

Appearance

(Color)

Flavor

Mouth feel

Overall

Sample: #

Extremely

dislike

1

2 3 4

Neither

like or

dislike

5

6 7 8

Extremely

like

9

Appearance

(Color)

Flavor

Mouth feel

Overall

66

Sample: #

Extremely

dislike

1

2 3 4

Neither

like or

dislike

5

6 7 8

Extremely

like

9

Appearance

(Color)

Flavor

Mouth feel

Overall

Sample: #

Extremely

dislike

1

2 3 4

Neither

like or

dislike

5

6 7 8

Extremely

like

9

Appearance

(Color)

Flavor

Mouth feel

Overall

67

Sample: #

Extremely

dislike

1

2 3 4

Neither

like or

dislike

5

6 7 8

Extremely

like

9

Appearance

(Color)

Flavor

Mouth feel

Overall

Second Part: Please rank the sample in an order from best sample to worst sample:

Sample # > Sample # > Sample # > Sample # >Sample #

68

APPENDIX 3. INDIVIDUAL TRAINING SESSION FOR

DECRIPTIVE SENSORY EVALUATION

ABX Test

Judge # Sample # Session # Date .

Directions: 1. Taste the sample based on the given order;

2. The left two samples (Reference A and Reference B) are different, please

determine which of the two samples match the last one and indicate by cycling the

code of the sample;

** If no similarity is apparent between the unknown sample and references, you must guess.

Reference A Reference B Unknown sample X

Code xxx xxx xxx

69

APPENDIX 4. DESCRIPTIVE SENSORY EVALUATION OF SOY

MILK

Judge # Sample # Session # Date:

Directions:

1. Please follow the order, from left to right, to test the sample. Write down your name

and the Sample Code;

2. Taste the reference for the first attributes, expectorate or swallow, to perceive the

intensity of reference;

3. Clean and rinse your mouth with water and crackers if needed;

4. Taste adequate sample to evaluate the intensity. Expectorate or swallow the sample;

5. Place a vertical mark on the horizontal line at the position that best describes the

perceived intensity of the given attribute;

6. Rinse your mouth by water (or crackers if needed) before you start the next attribute;

7. Start the next attribute by following Step 2 – 6;

8. After you finish one sample, please rinse your mouth by adequate water and crackers.

ATTENTION:

1. If you eat crackers, please make sure that you get rid of all crackers in your mouth,

since the crackers may have some negative influences on your results.

2. If you find a new attribute, which is not listed, please use the “Others” line on the

bottom of the last page to judge its intensity, so that this unknown attribute will not

affect your evaluation of the listed attributes. (You DO NOT need to write down the

attribute’s name of the “others”).

3. You can re-taste the sample among all the attributes, but please DO NOT go back to

samples you have already completed.

70

Flavor:

Sweetness:

None Strong

Bitter:

None Strong

Astringency:

None Strong

Beany:

None Strong

Rice-like

None Strong

71

Mouth-feel:

Thickness:

Thin Thick

Graininess:

Silky Grainy

Others:

None Strong

PLEASE:

1. Rinse your mouth by water and crackers;

2. Have a 5 min break, at least;

3. Make sure that all the residue of crackers are cleaned in

your mouth;

4. Start the next sample.

72

APPENDIX 5. CODE SERVING CONTAINERS FOR CONSUMER

ACCEPTANCE TEST

Subject No. Order in Tray

1 2 3 4 5

1 742 529 216 882 177

2 882 216 742 529 177

3 882 177 742 216 529

4 177 216 742 882 529

5 177 742 216 882 529

6 742 177 216 882 529

7 882 177 216 529 742

8 742 529 177 216 882

9 529 177 216 742 882

10 216 882 742 177 529

11 177 742 882 216 529

12 216 529 882 742 177

13 529 177 882 742 216

14 882 216 529 177 742

15 742 882 177 529 216

16 216 529 742 177 882

17 177 216 742 529 882

18 529 742 216 177 882

19 529 177 882 216 742

20 177 742 529 882 216

21 177 882 529 216 742

22 529 216 742 177 882

23 529 882 742 216 177

24 177 216 742 882 529

25 177 529 216 742 882

73

26 529 742 177 216 882

27 177 882 529 216 742

28 882 216 742 177 529

29 742 882 216 177 529

30 529 216 742 177 882

31 529 882 177 742 216

32 177 742 216 882 529

33 177 216 742 529 882

34 882 529 177 216 742

35 742 177 529 882 216

36 742 216 882 177 529

37 882 177 216 742 529

38 529 177 742 882 216

39 177 216 529 742 882

40 529 177 882 216 742

41 177 882 216 529 742

42 882 529 742 216 177

43 742 529 177 216 882

44 742 882 529 216 177

45 177 529 742 216 882

46 742 529 177 216 882

47 882 216 177 529 742

48 177 882 742 216 529

49 742 882 216 529 177

50 177 529 742 216 882

51 742 529 177 216 882

52 177 529 882 742 216

53 742 177 882 529 216

54 216 177 882 529 742

74

55 529 742 882 216 177

56 177 742 882 529 216

57 882 177 216 529 742

58 216 177 882 742 529

59 742 529 882 216 177

60 742 216 882 177 529

61 742 177 529 216 882

62 216 742 529 177 882

63 529 216 742 177 882

64 177 216 882 742 529

65 742 882 177 216 529

66 177 742 529 216 882

67 216 177 882 529 742

68 216 882 177 529 742

69 882 177 216 529 742

70 882 742 216 529 177

71 177 216 529 882 742

72 177 742 529 882 216

73 177 529 882 742 216

74 882 529 177 742 216

75 177 882 742 529 216

76 177 742 216 529 882

77 216 529 742 177 882

78 742 529 177 216 882

79 177 529 742 216 882

80 742 177 216 882 529

75

APPENDIX 6. CODE SERVING CONTAINERS FOR INDIVIDUAL

TRAINING SESSION (ABX TEST FOR SOME ATTRIBUTES)

Attributes Order in Tray

Reference A Reference B Unknown X

Astringency 177 529 X (same as 529)

177 529 X (same as 177)

216 177 X (same as 216)

216 177 X (same as 216)

Bitter 529 742 X (same as 529)

529 742 X (same as 742)

177 882 X (same as 882)

177 882 X (same as 177)

Beany 742 177 X (same as 177)

742 177 X (same as 742)

882 216 X (same as 882)

882 216 X (same as 882)

Thickness 882 529 X (same as 529)

882 529 X (same as 882)

216 529 X (same as 216)

216 529 X (same as 529)

Graininess 882 742 X (same as 742)

882 742 X (same as 882)

742 216 X (same as 742)

742 216 X (same as 216)

76

APPENDIX 7. CODE SERVING FOR DESCRIPTIVE SENSORY

EVALUATION

Session No. Judge

No.

Order in Tray

1 2 3 4 5

Session One 1 742 529 216 882 177

2 882 216 742 529 177

3 882 177 742 216 529

4 177 216 742 882 529

5 177 742 216 882 529

6 742 177 216 882 529

7 882 177 216 529 742

8 742 529 177 216 882

9 529 177 216 742 882

10 216 882 742 177 529

11 177 742 882 216 529

12 216 529 882 742 177

Session Two 1 141 183 418 417 304

2 141 304 183 418 417

3 304 418 141 183 417

4 141 417 304 183 418

5 417 183 418 141 304

6 418 417 183 141 304

7 304 183 418 141 417

8 304 417 141 418 183

9 141 418 183 417 304

77

10 141 183 418 304 417

11 417 304 141 183 418

12 418 141 304 417 183

Session

Three 1 556 132 868 359 849

2 868 132 556 849 359

3 849 359 556 868 132

4 849 868 556 132 359

5 849 132 359 868 556

6 868 849 359 132 556

7 359 868 849 132 556

8 132 868 556 849 359

9 849 556 132 868 359

10 132 849 359 868 556

11 868 132 556 359 849

12 556 132 868 359 849

*Sample 177, 141,132 are the same sample; Sample 742, 418,849 are the same sample;

Sample 882, 417, 556 are the same sample; Sample 216, 183,868 are the same sample;

Sample 529, 304, 359 are the same sample.

78

APPENDIX8. STATISCAL RESULTS OF MELTING DOWN

Time (min)

Melted Sample (%)

KB10-

23#1681

KB10-

5#1137

KB10-

25#1231

KB09-

5(2)#15 534545

10 0 0 0 0 0

15 0 0 0 0 0

20 0 0 0 0 0

25 0 0 0 0 0

30 0 0 0 0 0

35 0b 1.8

a 2.0

a 2.0

a 0

b

40 0.9b 2.7

a 3.0

a 3.1

a 0

c

45 1.9b 3.6

a 4.1

a 4.0

a 0

c

50 3.0b 4.7

a 5.0

a 5.0

a 0.8

c

55 5.1a 5.7

a 6.1

a 6.1

a 1.9

b

60 8.1a 8.8

a 9.1

a 8.0

a 3.9

b

65 11.0a 12.3

a 12.0

a 11.1

a 8.0

b

70 13.0b 16.0

a 16.0

a 15.0

a 11.0

c

75 16.9c 26.1

a 23.1

b 22.9

b 16.1

c

80 22.9c 40.2

a 35.9

a 31.0

b 20.3

c

85 30.1c 60.9

a 54.9

a 43.0

b 24.8

d

90 44.8c 78.3

a 71.2

a 58.1

b 32.3

d

*Means sharing the same letter within the same row do not differ at p < 0.05

79

APPENDIX 9. CODE FROM STATISTICAL ANALYSIS FOR

SENSORY EVALUATION

Average 3 datasets.

d1 <- read.csv("/Users/Tieming/consulting/Jing/data/data1.csv", header=TRUE)

d1 <- as.matrix(d1)


d2 <- d2[,1:10]

d2 <- as.matrix(d2)


d3 <- as.matrix(d3)

## dataset d: dim (60, 9, 3), each third dimension is a time of survey.

## First x Second dimension:

## obs x (7 attributes + addition evaluation + judge index).

d <- array(c(as.vector(d1[,-1]), as.vector(d2[,-1]), as.vector(d3[,-1])), dim=c(60, 9, 3))

mean.d <- apply(d, c(1,2), mean)[,1:7]

colnames(mean.d) <- colnames(d1)[2:8]

var.d <- apply(d, c(1,2), var)[,1:7]

colnames(var.d) <- colnames(d1)[2:8]

median.d <- apply(d, c(1,2), median)[,1:7]

colnames(median.d) <- colnames(d1)[2:8]

head(median.d)

## remove judge effect for each attribute.

soy <- as.factor(rep(1:5, times=12))

judge <- as.factor(rep(1:12, each=5))

data <- NULL

for(att.index in 1:7){

o <- lm(median.d[,att.index] ~ judge + soy)

summary(o)

o$coefficients

tmp <- median.d[,att.index] - rep(c(0, o$coefficients[2:12]), each=5)

data <- cbind(data, tmp)

}

colnames(data) <- colnames(median.d)

rownames(data) <- NULL

# Prepare mean-centered data

median.mean <- apply(data, 2, mean)

center.d <- data - array(rep(median.mean, each=60), dim=c(60, 7))

head(center.d)

Prin 1 vs. Prin 2

par(mar=c(5,5,1,1))

plot(o$scores[,1], o$scores[,2], xlab="Scores, Prin1 36%", ylab="Scores, Prin2 18%",

cex.lab=1.5, cex.axis=1.5, pch=19, xlim=range(o$scores[,c(1,2)]), ylim=range(o$scores[,c(1,2)]),

cex=1.3, type="n")

index.882 <- seq(3, 60, 5)

points(o$scores[index.882,1], o$scores[index.882,2], pch=7, cex=1.3)

points(o$scores[!(1:55)%in%index.882,1], o$scores[!(1:55)%in%index.882,2], pch=3, cex=1.3)

80

legend("topright", c("KB10-25","others"), cex=1.5,pch=c(7,3))

#install.packages("plotrix")

library(plotrix)

abline(h=0, v=0)

# draw 'beany' vector.

arrows(x0=0, y0=0, x1=o$loadings[4,1]*o$sdev[1]*4, y1=o$loadings[4,2]*o$sdev[2]*4,

length=0.1, lwd=2)

text("Beany", x=o$loadings[4,1]*o$sdev[1]*4, y=o$loadings[4,2]*o$sdev[2]*4+0.5, cex=1.5)

index.177 <- seq(1, 60, 5)

points(o$scores[index.177,1], o$scores[index.177,2], pch=19, col="blue", cex=1.5)

points(o$scores[seq(2,60,5),1], o$scores[seq(2,60,5),2], pch=19, col="purple", cex=1.5)

points(o$scores[seq(4,60,5),1], o$scores[seq(4,60,5),2], pch=19, col="yellow", cex=1.5)

points(o$scores[seq(5,60,5),1], o$scores[seq(5,60,5),2], pch=19, col="green", cex=1.5)

# Table for test

sweetness.est <- tapply(median.d[,1], soy, mean)

sd(sweetness.est)

bitter.est <- tapply(median.d[,2], soy, mean)

sd(bitter.est)

astringency.est <- tapply(median.d[,3], soy, mean)

sd(astringency.est)

beany.est <- tapply(median.d[,4], soy, mean)

sd(beany.est)

ricelike.est <- tapply(median.d[,5], soy, mean)

sd(ricelike.est)

thickness.est <- tapply(median.d[,6], soy, mean)

sd(thickness.est)

graininess.est <- tapply(median.d[,7], soy, mean)

sd(graininess.est)

# ANOVA p values

median.d <- as.data.frame(median.d)

o <- lm(Sweetness ~ soy + judge, data=median.d)

summary(o)

# PCA p values

dim(center.d)

head(center.d)

o <- princomp(center.d)

P <- o$loadings

tmp.pc1.loadings <- NULL




for(soy.index in 1:5){

tmp.d <- center.d[soy!=soy.index,]

# center tmp.d

tmp.center.d <- apply(tmp.d, 2, mean)

tmp.center.d <- tmp.d - array(rep(tmp.center.d,each=48), dim=c(48, 7))

tmp.o <- princomp(tmp.center.d)

81

tmp.P <- tmp.o$loadings

#tmp.o$loadings

squared.sum.1 <- sum(((P-tmp.P)^2)[,1])

squared.sum.2 <- sum(((P+tmp.P)^2)[,1])

if(squared.sum.2 < squared.sum.1){tmp.P <- -tmp.P}

tmp.pc1.loadings <- cbind(tmp.pc1.loadings, tmp.P[,1])













}

pc1.p <- NULL

for(i in 1:7){

sd <- sqrt(sum((P[i,1]-tmp.pc1.loadings[i,])^2)*4/5)

tmp.p <- pt(abs(P[i,1]/sd), 5, lower.tail=FALSE)

pc1.p <- c(pc1.p, tmp.p)

}

round(pc1.p, digits=3)

pc2.p <- NULL

for(i in 1:7){




}


pc3.p <- NULL

for(i in 1:7){




}


pc4.p <- NULL

for(i in 1:7){



82


}


apply(tmp.pc2.loadings, 1, sd)

83

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