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CATEGORIZATION AND SENSORY PROFILING OF FUNCTIONAL BEVERAGES

BY

LAUREN CHIEMI TAMAMOTO

B.S., University of Hawaii at Manoa, 2003 M.S., University of Queensland, 2004

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Food Science and Human Nutrition

in the Graduate College University of Illinois at Urbana-Champaign, 2009

Urbana, Illinois

Doctoral Committee:

Professor Keith R, Cadwallader, Chair Associate Professor Soo-Yeun Lee, Co-Director of Research Professor Shelly J. Schmidt, Co-Director of Research Associate Professor Elvira de Mejia

UMI Number: 3395512

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© 2009 Lauren Chiemi Tamamoto

ABSTRACT

The significant influx of a wide variety of commercially-available functional

beverages has resulted in a beverage segment that is not clearly defined or understood.

The rapid increase in functional beverages has also resulted in the lack of understanding

of the sensory, chemical, and physical effects of functional ingredients in these products.

Therefore, the central hypothesis of this research was that there are distinct functional

beverage categories and that functional ingredients incorporated into beverage

formulations affect the sensory properties of beverages. The objectives were to: 1)

categorize commercially-available functional beverages using three different methods

(ingredient inventory, flow behavior comparison, and a two-step sensory sorting), 2)

develop and validate the two-step sensory sorting method to categorize large number of

samples, 3) determine and describe the effects of functional ingredients (caffeine,

ginseng, and taurine) on the sensory characteristics of model energy drink solutions, and

4) identify effective treatments and levels of bitterness minimizers to reduce the

bitterness of ginseng in water base and model energy drink base solutions. Of the three

categorization methods, the two-step sensory sorting produced the most distinctive and

defined categories. The seven functional beverage categories generated were: Enhanced

Waters, Energy Drinks, Fruit Smoothies, Nutritional Drinks, Sports Drinks, Teas, and

Yogurt Smoothies. The research suggests that the two-step sorting is a valid and

reproducible method to categorize a large number (~50) of functional beverages. Since

Energy Drinks was one of the most distinct categories generated in the categorization

research, it was the main focus in the functional ingredients study. To determine the key

sensory attributes of model energy drink solutions containing 27 combinations (3x3x3

factorial design) of the three functional ingredients at three concentrations (low, medium,

ii

high), a descriptive analysis (DA) was conducted. The results from the DA research

suggest that ginseng predominantly contributes to the bitter attributes. Bitterness

minimizers were investigated, and it was found that cyclodextrins are effective in

reducing the bitter taste of ginseng in solution. Taste is a key component in the

acceptability of food products and the more known about the inclusion of ingredients into

a food matrix, the belter we can develop successful products.

iii

ACKNOWLEDGEMENTS

Thank you to all who have been instrumental in my journey towards my doctoral

degree. I feel fortunate to have many friends, mentors, and supporters who believed in

me, and who were my cheering squad. I would not be in this position today had it not

been for all the support and encouragement that I received.

I would like to thank my thesis committee-Dr.Keith Cadwallader-Chairperson,

Dr. Elviria DeMejia, and Dr. Youngsoo Lee for all their time and helpful suggestions.

Special thanks to my two advisors Dr. Soo-Yeun Lee and Dr. Shelly Schmidt, who

provided valuable guidance and spent countless hours discussing my research and editing

my papers. Thank you for not only being my advisors, but also my friends who I could

always talk to.

Thank you to my labmates, friends, and family for all their support throughout the

years. All your words of encouragement and friendship helped me to pursue and conquer

one of my most ambitious goals in life. Thank you to my parents for instilling the

importance of education and for providing me with a strong foundation of values. Thank

you to my sister Reagan who is one of my best friends and has been a great older sister

who is always looking after me. Lastly, thank you to my husband Michael (who has read

and heard about my research so much, that he understands it just as much as I do), for

your unconditional love and support, and for just being there for me through it all,

iv

TABLE OF CONTENTS

List of Tables viii

List of Figures x

Chapter 1 -Introduction 1

1.1 Motivation 1 1.2 Objectives 3 1.3 References 4

Chapter 2 - Literature Review 5 2.1 Functional Beverages 5

2.1.1 Functional Beverage Categories 6 2.2 Energy Drinks 9

2.2.1 Functional Ingredients 10 2.2.2 Descriptive Analysis of Energy Drinks 12

2.3 Bitter Taste 14 2.3.1 Bitterness Minimizers 15 2.3.2 Masking Agents 16 2.3.3 Molecular Interaction 16 2.3.4 Bitter Taste Receptor Blockers 17 2.3.5 Cyclodextrins 18

2.4 Sorting and Categorization Methods 19 2.5 Concluding Remarks '. 23 2.6 References 24 2.7 Tables 32

Chapter 3 - Categorization of Commercially-Available Functional Beverages by Chemical, Physical, and Sensory Commonalities 33 3.1 Abstract .' ,...-,- 33 3.2 Introduction 34 3.3 Materials and Methods 36 3.4 Results and Discussion 42 3.5 Conclusions 48 3.6 References 49 3.7 Tables and Figures 52

v

Chapter 4 - Validation and Reproducibility Study of a Two-Step Sensory Sorting Method to Categorize Functional Beverages 64 4.1 Abstract 64 4.2 Introduction 65 4.3 Materials and Methods : 67 4.4 Results and Discussion 75 4.5 Conclusions 81 4.6 References 82 4.7 Tables and Figures 85

Chapter 5 - Sensory Profile of a Model Energy Drink with Varying Levels of Functional Ingredients-Caffeine, Ginseng, and Taurine , 104 5.1 Abstract 104 5.2 Introduction 105 5.3 Materials and Methods 108 5.4 Results and Discussion 113 5.5 Conclusions 118 5.6 References 118 5.7 Tables and Figures 121

Chapter 6 - Sensory Properties of Ginseng Solutions Modified by Masking Agents ....129 6.1 Abstract 129 6.2 Introduction 130 6.3 Materials and Methods 132 6.4 Results and Discussion 139 6.5 Conclusions 145 6.6 References 146 6.7 Tables and Figures 149

Chapter 7 - Summary 158

Chapters -References 162

Appendix A: Functional Beverage Ingredient Inventory Comparison Chart 173

Appendix B: Two-Step Sensory Sorting Method-Free Sorting Task Sample Scorecard 174

Appendix C: Template of Functional Beverage Names and Number Codes Stickers used in the Two-Step Sensory Sorting Method 175

Appendix D: Two-Step Sensory Sorting Method Sample Sorting Scorecard 176 Appendix E: Descriptive Analysis Recruitment Prescreening Questionnaire 177

vi

Appendix F: Descriptive Analysis Recruitment Prescreening Taste Identification

Test 179

Appendix G: Infonned Consent Form for Sensory Evaluation Studies 180

Author's Biography 181

vii

LIST OF TABLES

Table 2.1: Amount of Functional Ingredients in Commercially-available Energy Drinks purchased in September 2006 32

Table 3.1: Fifty commercially-available functional beverages and corresponding numerical codes 52

Table 3.2: Functional beverage category names generated through visual observation of the beverage ingredient commonalities in the ingredient inventory spreadsheet 53

Table 3.3: Viscosities of Newtonian Functional Beverages measured at 20°C 54

Table 3.4: Viscosities of non-Newtonian Functional Beverages at a shear rate of 50 s"1 55

Table 4.1: Fifty commercially-available functional beverages and corresponding numerical codes 85

Table 4.2: Adjusted Rand Index values of the comparison of clusters generated through free and fixed sorting tasks by Panels 1 to 4 86

Table 4.3: Compilation of Panel 2 and 3's validation study results of commercially available functional beverages sorted into categories by visual and

visual-oral fixed sorts compared to Panel l 's results 87

Table 4.4: Compilation of Panel 1 and 4's reproducibility study results comparing commercially-available functional beverages sorted into categories by visual and visual-oral free and fixed sorting task results 89

Table 5.1: Amount of functional ingredients listed on Nutritional Facts labels of a sampling of popular commercially-available energy drinks 121

Table 5.2: Amount of functional ingredients (caffeine, ginseng, and taurine) in 100 mL model energy drink solutions 122

Table 5.3: Terms, definitions, references, and ratings for scale anchors of the descriptive attributes for the model energy drink solutions 123

Table 5.4: Analysis of Variance on 13 descriptive attributes rated for model energy drink solutions 124

Table 5.5: Mean intensity scores of sensory attributes of varying levels of functional ingredients 125

viii

Table 5.6: Correlation analysis on significant sensory attributes for 27 combinations of functional ingredients in model energy drink solutions 126

Table 6.1: Solution treatment codes and corresponding levels of y-, p-CDs, and their combinations in both 100 mL water base and 100 mL model energy drink base solutions 149

Table 6.2: Bitterness intensity rankings (l=least bitter to 6=most bitter) of the bitterness minimizing treatments incorporated in a 0.0529 g ginseng/100 mL water solution 150

Table 6.3: Mean bitterness intensity rating scores (0 to 9) of bitterness minimizing treatment levels incorporated in a 0.0529 g ginseng/100 mL water solution 151

Table 6.4: Analysis of Variance on descriptive attributes rated for ginseng solutions containing varying levels of y- and P-CDs 152

Table 6.5: Mean quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 21 solution treatments combining water base and model energy drink base solutions and with and without nose clips usage data 152

Table 6.6: Mean" quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 7 y-CD solution treatments in water base or model energy drink base, without nose clips and with nose clips usage data 153

Table 6.7: Mean" quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 7 P-CD solution treatments in water base or model energy drink base, without nose clips and with nose clips usage data 154

ix

LIST OF FIGURES

Figure 2.1: Chemical Structure of Caffeine 10

Figure 2.2: Chemical Structure of Ginsenoside 11

Figure 2.3: Chemical Structure of Taurine 12

Figure 3.1: Multidimensional Scaling of the results of the ingredient inventory categorization with stress = 0.227 56

Figure 3.2: Agglomerative hierarchical clustering of 50 functional beverages (including decarbonated beverages) by viscosity measurement using .the ARES RFS III 57

Figure 3.3: Agglomerative hierarchical clustering of 33 Newtonian functional beverages by viscosity measurement using the ARES RFS III 58

Figure 3.4: Agglomerative hierarchical clustering of 17 non-Newtonian functional beverages by viscosity measurement at 50 sec"1 shear rate using the ARES RFS III 59

Figure 3.5: Multidimensional Scaling of a visual free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.265 and functional beverage categories generated through the free visual sorting method 60

Figure 3.6: Multidimensional Scaling of a visual-oral free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.290 and functional beverage categories generated through the free visual-oral sorting method 61

Figure 3.7: Multidimensional Scaling of a visual fixed sort (Part 2) of 50 functional beverages plotted in two dimensions with stress = 0.289 and corresponding beverage categories 62

Figure 3.8: Multidimensional Scaling of a visual-oral fixed sort (Part 2) of 45 functional beverages plotted in two dimensions with stress = 0.283 and corresponding beverage categories 63

Figure 4.1: Flow chart of studies and panels, categories, types of sorting task, and results of the conducted two-step sensory sorting method 91

Figure 4.2: Multidimensional Scaling Panel l's visual free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.265 and functional beverage categories generated through the free visual sorting method

Figure 4.3: Multidimensional Scaling of Panel l 's visual-oral free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.290 and functional beverage categories generated through the free visual-oral sorting method 93

Figure 4.4: Multidimensional Scaling of Panel l 's visual fixed sort (Part 2) of 50 functional beverages plotted in two dimensions with stress = 0.289 and corresponding functional beverage categories 94

Figure 4.5: Multidimensional Scaling of Panel l 's visual-oral fixed sort (Part 2) of 45 functional beverages plotted in two dimensions with stress = 0.283 And corresponding functional beverage categories 95

Figure 4.6: Multidimensional Scaling of Panel 2's visual fixed sort of 50 functional beverages plotted in two dimensions with stress = 0.301 and corresponding fixed functional beverage categories a) Multidimensional Scaling of Panel 4's visual free sort data of 50 96

Figure 4.7: Multidimensional Scaling of Panel 2's visual-oral fixed sort of 46 functional beverages plotted in two dimensions with stress = 0.271 and corresponding fixed functional beverage categories 97

Figure 4.8: Multidimensional Scaling of Panel 3's visual fixed sort of 50 functional beverages plotted in two dimensions with stress = 0.241 and corresponding fixed functional beverage categories 98

Figure 4.9: Multidimensional Scaling of Panel 3's visual-oral fixed sort of 46 functional beverages plotted in two dimensions with stress = 0.284 and corresponding fixed functional beverage categories 99

Figure 4.10: Multidimensional Scaling of Panel 4's visual free sort (Part 1) of 46 functional beverages plotted in two dimensions with stress = 0.260 and functional beverage categories generated through the free visual sorting method 100

Figure 4.11: Multidimensional Scaling of Panel 4's visual-oral free sort (Part 1) of 46 functional beverages plotted in two dimensions with stress = 0.232 and functional beverage categories generated through the free visual-oral sorting method 101

Figure 4.12: Multidimensional Scaling of Panel 4's visual fixed sort (Part 2) of 46 functional beverages plotted in two dimensions with stress = 0.261 and corresponding fixed functional beverage categories 102

xi

Figure 4.13: Multidimensional Scaling of Panel 4's visual-oral fixed sort (Part 2) of 46 functional beverages plotted in two dimensions with stress = 0.262 and corresponding fixed functional beverage categories 103

Figure 5.1: Principal component analysis biplot of covariance matrix of mean sensory attributes of 27 combinations of functional ingredients in model energy drink solutions with varimax rotation 127

Figure 5.2: Agglomerative hierarchical clustering (AHC) of attribute ratings for 27 combinations of functional ingredients in model energy drink solutions on the dissimilarity scale by Euclidean distance and agglomeration by Ward's method , 128

Figure 6.1: Effect of y-CD levels on (a) quinine bitter and (b) caffeine bitter aftertaste intensity ratings of ginseng solution treatments with and without nose clips and in water base or model energy drink base solutions 155

Figure 6.2: Effect of p-CD levels on (a) quinine bitter and (b) caffeine bitter aftertaste intensity ratings of ginseng solution treatments with and without nose clips and in water base or model energy drink base solutions 156

Figure 6.3: Agglomerative hierarchical clustering (AHC) of quinine bitter and caffeine bitter aftertaste attribute mean intensity ratings for 21 ginseng solution treatments containing varying levels of y-CD and p-CD on the dissimilarity scale by Euclidean distance and agglomeration 157

xii

CHAPTER 1- INTRODUCTION

1.1 Motivation

Functional beverages are a booming market, with hundreds of new beverages

introduced each year (Packaged Facts 2009). The popularity of functional beverages

stems from the fact that they are a healthy alternative to soft drinks because they provide

additional health benefits. The origins of functional beverages began with the

introduction of beverages geared to replace fluids and electrolytes after exercise (Leveen

2007). Energy drinks were then developed for consumers who desired an extra boost of

energy in a beverage. The functional beverage market is rapidly expanding, and it is

necessary to understand the characteristics associated with specific types of functional

beverages, to meet consumers' expectations. Therefore, the final central hypothesis of

this thesis is that there are distinct functional beverage categories, and that the functional

ingredients incorporated into beverage formulations affect the sensory properties of

beverages.

One of the initial working hypotheses of this research was that there are particular

mouthfeel and sensory expectations that correspond to different types of functional

beverages. The questions we wanted to answer included: is there a particular mouthfeel

associated with an isotonic drink that is unique in comparison to an energy drink? Or

could a highly viscous solution be considered a tea? To answer these questions, a

literature review was conducted, and it was found that there was no set of standard

definitions of functional beverage categories. The rapid introduction of new functional

beverages into the market raised the need to define the specific categories of these

beverages. The expansion of the functional beverage market resulted in the creation of

1

numerous hybrid-type beverages, which combine multiple concepts. Therefore, the

initial research focus was then expanded to answer the question, "What are the different

categories of functional beverages and what makes each beverage category distinct?"

The first hypothesis of this research was that functional beverage can be classified into

well-defined categories through categorization methods.

In the categorization research, energy drinks were found to be one of the most

distinctive and popular categories of the beverage market. The ingredients of thirteen

commercial carbonated energy drink products were inspected, and it was found that

caffeine, ginseng, and taurine were some of the most common functional ingredients

contained in energy drinks. In 2007, ginseng, caffeine, and taurine were all considered

one of the top 15 functional ingredients consumers seek in functional beverages (Lai

2007). Yet, limited research had been conducted to determine the sensory effects

associated with the addition of these functional ingredients in food products. Consumers

want products that provide health benefits and have a pleasant taste (Drewnowski and

Gomez-Carneros 2000), and the inclusion of ingredients to a beverage solely based on

functional properties may result in a product rich in bioactives, but with an unacceptable

taste.

Therefore, it is necessary to investigate the resulting effects from the

incorporation of functional ingredients into a beverage matrix. This is important because

understanding the interaction between the base solution and ingredients will help in

creating pleasant tasting products (Granato 2002). The next hypothesis in this study was

that the functional ingredients in a model energy drink solution will have synergistic

effects on the sensory properties of the drink. Understanding the effects of the addition

2

of specific functional ingredients into a food matrix will aid in the development of new

food products by providing potential solutions to flavor issues such as reducing negative

flavor attributes with the addition of a masking agent. It could also help in selecting

concentrations of functional ingredients that are acceptable to consumers.

The results from the sensory study on the synergistic effects of caffeine, ginseng,

and taurine into a model energy drink solution suggested that ginseng was the most

dominant functional ingredient in the solution and that it imparts a bitter taste. The •

bitterness in functional foods often reduces the liking of a product (Tuorila and Cardello

2002), and identifying effective methods of minimizing the bitterness in functional

beverages will allow formulators to produce products that have health benefits as well as

acceptable sensory qualities. Thus, the last part of this research focuses on minimizing

bitterness of ginseng in a model energy drink solution. The hypothesis was that the use

of cyclodextrins will aid in minimizing bitterness of ginseng in model energy drink

solutions while still maintaining ginseng's health benefits. This research can be utilized

in the development of acceptable energy drinks, and also to predict changes in sensory

characteristics when reformulating functional ingredients in energy drinks.

1.2 Objectives

The two main hypotheses of this research were that 1) functional beverages can

be classified into defined categories through relatively quick and easy methods, and 2)

the inclusion of functional ingredients has an effect on the sensory properties of energy

drinks. The first objective of this research was to categorize commercially-available

functional beverages using three different methods: 1) ingredient inventory, 2) flow

3

behavior comparison, and 3) two-step sensory sorting. The second objective was to

develop and validate the two-step sensory sorting method to categorize a large number of

samples. The third objective was to determine and describe the effects of functional

ingredients (caffeine, taurine, and ginseng) on the sensory characteristics of model energy

drink solutions. The fourth and final objective was to identify effective treatments and

levels of bitterness minimizers to reduce the bitterness of ginseng in water base and

model energy drink base solutions. The findings from this research will be beneficial to

the development of acceptable functional beverage formulations.

1.3 References

Drewnowski A, Gomez-Carneros C. 2000. Bitter taste, phytonutrients, and the consumer: a review. Am J Clin Nutr 72(6): 1424-35.

Granato H. 2002. Manipulating Flavor Perception in Functional Products. Natural Products Insider [serial online]. Available from Posted 8 April 2002 2002.

Lai GG. 2007. Getting Specific with Functional Beverages. Food Technology [serial online]. 61 (12):Available from Posted 2007.

Leveen T. 2007. Functional Beverages: Market Evolution. Natural Products Marketplace [serial online]. Available from Posted 2007.

Packaged Facts. 2009. Functional Foods and Beverages in the U.S. 1-210.

Tuorila I-I, Cardello AV. 2002. Consumer responses to an off-flavor in juice in the presence of specific health claims. Food Qual Pref 13(7-8):561-9.

4

CHAPTER 2 - LITERATURE REVIEW

2.1 Functional Beverages

In the US, concerns about disease and health have increased the popularity of

functional food products (Schmidl and Labuza 2000, Urala and LMhteenmaki 2003).

Functional foods are consumed because they are thought to provide more benefits than

ordinary foods. It is also more convenient to consume a beverage providing health

benefits rather than swallow vitamins or pills for the same health benefits (Leveen 2007).

These "total health" and weight management concerns have prompted growth in the

number of functional food and beverages available on the market (Lai 2007) and

consumers are now seeking products which provide an added health benefit to ordinary

food products. In 2007, there were over $10.1 billion in functional beverage sales in the

US, and by 2010 functional beverage sales are projected to increase to over $12 billion

(Mintel 2008).

There is currently no legal definition of a "functional beverage" or universally

accepted categories of functional beverages in the United States. The Institute of Food

Technologists (2005), however, has defined functional beverages as beverages that

provide health benefits beyond basic nutrition. Therefore, functional beverage categories

may encompass beverages containing probiotics, stimulants, or additional vitamins and

minerals. The inclusion of ingredients based on functional needs is a greater driving

force in product development than category boundaries (Humphries 2007), and has

resulted in numerous new products introduced each year. The significant increase in

functional beverage popularity has led to an influx of new products and the introduction

of numerous "hybrid-type" beverages to the market. These hybrid-type beverages fall

5

under the "functional beverage" definition because they provide unique health benefits,

and at the same time incorporate multiple beverages concepts. For example, there are

now diet energy drink teas and dairy-based diet beverages on the market.

Some benefits of functional ingredients include: replenishing electrolytes in the

body, providing an extra boost of energy, or aiding with digestion. A few popular

functional ingredients included in beverage formulations are: antioxidants, stimulants,

botanicals, vitamins, and minerals (Lai 2007). Some beverages on the market incorporate

ingredients naturally containing functional benefits, while other beverages include

synthetic ingredients which provide the same benefits. These functional ingredients

affect the sensory experience, and may sometimes result in unpleasant sensory .

characteristics, such as bitterness or chalkiness. Studies, however, have shown that

consumers are more tolerant of unpleasant flavors if the beverages deliver additional

health benefits (LeClair 2000).

The functional beverage boom has led to an increase in new products as

companies are attempting to capitalize on this market (Wright 2008). Consumers desire

functional beverages that encompass multiple concepts and provide specific health

benefits. The functional beverage market is driven by consumers, thus there has been a

shift to create more hybrid-type products. Therefore, there is a need to understand the

influence of functional ingredients on the sensory properties to create belter tasting

functional beverages.

2.1.1 Functional Beverage Categories

The constant introduction of functional beverages to the market makes it difficult

to pinpoint the major functional beverage categories and corresponding definitions.

6

Initially, energy drinks and sports drinks were considered functional beverages, but now

the inclusion of botanicals and probiotics are considered part of the functional beverage

market (Alldrick 2006). Functional beverages with claims of managing appetite or aiding

in younger-looking skin are currently part of the influx of new functional beverages

introduced to the market (Leveen 2007). Publications often refer to loosely defined

categories, but the categories that are considered part of the functional beverages segment

are never consistent. The lack of functional beverage categories leads to an absence .of

requirements and standards of identity. The general beverages market, which has been

established for many years, has guidelines and regulations concerning ingredient amounts

and package labeling. The absence of functional beverage categories also may hinder the

purchase intent of consumers and could lead to improper positioning of a product (Lord

2000).

Mintel Reports (2008) segmented the functional beverage market into six

categories: juices and juice drinks; smoothies and yogurt drinks; teas; soy-based drinks;

energy drinks and enhanced water; and sports drinks. Rehydrating sports drinks and meal

or diet drinks were not considered functional beverage categories in the Mintel Reports

(2008). Mintel reports, however, are not available to the general public, therefore, these

functional beverage categories are not common knowledge. In the Packaged Facts -

Functional Foods and Beverages in the US report (2009), the eight functional beverage .

categories included in the report were: shelf-stable bottled juice drinks, blends and

smoothies; refrigerated soymilk, kefir, and milk substitutes; fresh milk; bottled water;

loose/bagged tea; refrigerated juice; drink concentrates; and ready-to-drink tea and coffee

(Packaged Facts 2009). Beverages containing "no" or "low" calories or carbohydrates

7

were not considered part of the functional beverage segment unless they also

incorporated specific health claims. Packaged Facts is also a publication that is not

accessible to the general public, and these functional beverage categories are not common

knowledge.

A trade magazine segmented the functional beverage market into six categories

which includes: sports and performance drinks; energy drinks; ready to drink (RTD) teas;

enhanced fruit drinks; soy beverages; and enhanced water (Anonymous 2008). In the

Japan's beverage market, the segmentation of nutritional and health beverages are

separated into six categories which include: nutritional drinks; functional drinks;

nutritionally-balanced drinks; sports drinks; near water; and soy milk beverages (Ohki

and others 2004). An article published that the characterizing differences between sports

hydrating drinks and energy drinks were the type of carbohydrates incorporated in the

formulation and pH levels (Berry 2009); although hybrid products combining properties

of both energy drinks and sports drinks have also been introduced on the market, While

the Canadian government still does hot know whether energy drinks or sports drinks are

considered a food or a drug (Farrell and others 2009), Not only is it important to

categorize functional beverage for proper marketing and consumer expectations, but for

legal regulations and guidelines of products.

Beverage categories are loosely referred to in industry periodicals, but there are

no standard categories and definitions of functional beverages. To the researchers'

knowledge, there have been no research-based attempts to determine and define the

functional beverage categories on the market. There also have been no sensory studies

attempting to categorize functional beverages currently available on the market.

8

2.2 Energy Drinks

Mintel (2009) defines energy drinks as beverages that "specifically claim to

provide an energy or stimulation boost." Energy drinks are one of the fastest growing

segments of the functional beverage market. In 2008, energy drink sales were over $4.7

billion and has been estimated to grow to over $7 billion by 2014 (Mintel 2009).

Between 2004 and 2008, over 1,000 new energy drinks were introduced into the market

(Packaged Facts 2009). The origins of energy drinks began in Southeast Asia when truck

drivers consumed the drinks to stay awake. The popularity of the drinks expanded to

Europe as a means to stay awake for late night clubbing, and then consumed as an

alternative to coffee for students (Burg 1998). Energy drinks have gained popularity due

to the boost of energy provided by the large concentration of stimulants. They are also an

alternative to coffee as a source of caffeine, while also containing functional ingredients

such as antioxidants, taurine, ginseng, and B vitamins. Many energy drinks generally

include active ingredients such as glucose, caffeine and taurine, as well as other health-

oriented ingredients such as ginseng and various vitamins and minerals.- •

An increase in the concern about health and sugar intake resulted in a slight

decline in the energy drink segment, and now, reduced sugar lines and energy shots are

being introduced into the market. Energy shots, which are a concentrated form of energy

drinks, are gaining popularity because of the claimed energy boost without all the

excessive calories from sugar (Mintel 2008). Energy shots, however, were recently

labeled as a dietary supplement by the Food and Drug Administration (FDA), rather than

as a beverage. The large amounts of functional ingredients in a concentrated energy shot

9

have the same problem of unpleasant taste as energy drinks, therefore solutions must be

developed.

Flavor, however, is the consumer's most important attribute in energy drinks.

Therefore, the industry has put more effort into improving the taste of functional

beverages (LeClair 2000), For example, functional ingredients are typically masked with

sweeteners, but incorporating pleasant flavors is necessary in creating a successful

product.

2.2.1 Functional Ingredients

Three of the most common functional ingredients in energy drinks are caffeine,

ginseng, and taurine. Caffeine is a methylxanthine

with the chemical formula of CSH10N4O2 (Figure

2.1). It is a white, odorless powder, with a low

solubility and is usually combined with other

chemicals such as purines and pyrimidines to Figure 2.1: Chemical Structure of Cnrfcinc

increase its solubility (Spiller 1998). Caffeine is

commonly found in cola products and in recent years has been incorporated into snack

foods such as cereal bars and sunflower seeds (Cosgrove 2008). No research was found

on caffeine's effect on texture or on the mouthfeel of beverages. Caffeine is on the US

Food and Drug Administration's GRAS list and is limited to no more than 0.02% by

volume in cola-type products (Food and Drug Administration 2003). Currently there are

no regulations regarding the maximum amount of caffeine allowed in energy drinks. The

typical amount of caffeine in energy drinks ranges from 0.20 to 1.13% by volume (Table

2.1).

10

Caffeine is known to impart a bitter taste in foods, and is more apparent in simpler

flavors (Mobini and others 2005). In a research study on teas, it was found that panelists

could tell the difference between the absence and presence of 100 mg of caffeine in the

tea solutions (Yeomans and others 2007). A review of caffeine taste studies concluded

that caffeine contributes to the flavor of some beverages (Allison and Chambers 2000).

Ginseng is from the Araliaceae family and contains ginsenosides (Figure 2.2), •

which are active steroid-like compounds (Spiller 1998). These active compounds of-CH,OH

ginseng are triterpenoid saponin glycosides, H / ° \ O - ^ C.H' ., . £ ' C H »N:H I -CH«< C - C H '

which also are responsible for the bitter flavors 0H,—~u ^ " ^ — ^ '

of ginseng (Court 2000a). Ginseng extract is HO'

known to have'antioxidant properties (Jung and ' ' I CHjOHj

others 2002), and may aid in alleviating j\J5H u)|

cognitive function (Coon and Ernst 2002) and Figllrc2 £ c |)cmica] Structureoroinscnosidc

some health conditions such as diabetes (Vuksan and Sievenpiper 2005). Some research

has been conducted on ginseng consumption for increased energy, help with indigestion,

and overall improvement of health (Court 2000b). Yet, there has been limited research

validating these medicinal properties of ginseng (Vogler and others 1999, Kilts and Hu

2000). There are known toxicity levels of ginseng consumption, although Kitts (2000),

suggests that intake of less than 15 g of ginseng per day may be a good limit because

greater amounts may run the risk of confusion or depression.

Limited research has been conducted in the US on the characteristic flavors of

ginseng. According to research conducted in Korea, key flavors of ginseng has been

described as earthy, astringent, and bitter (Kim 1985). Ginseng is growing in popularity

11

and in 2008, 38% of consumers sought functional beverages containing ginseng (Mintel

2008).

Taurine is a derivative of the amino acid cysteine with the chemical formula of

C2H7NO3S (Figure 2.3), and is present in the tissues of o II humans and animals. It contains sulfur and was first

H O NV N H 2 isolated from the bile of ox. It aids with bile acid

o Figure 2.3: Chemical Structure of murine conjugation, retinal development, and central

nervous system function (Lourenco and Camilo 2002). Taurine has also been found to

aid in immunity and may have antioxidant properties (Yu and Kim 2009). Taurine is

commonly incorporated in energy drinks and in muscle-building supplements because it

has been shown to reduce muscle fatigue in mice (Warskulat and others 2004). The

typical amount of taurine available in energy drinks is 1000 mg/100 mL which is enough

to stimulate the growth of muscles (Table 2.1). Taurine was described to add a broth-like

flavor to a chicken meat (Minor and others 1966). Other than the chicken flavor study,

there has been no sensory studies conducted on the tastes and flavors associated with the

incorporation of taurine into a food or beverage matrix.

2.2.2 Descriptive Analysis of Energy Drinks

Sensory Evaluation is a method used to evoke, measure, analyze, and interpret

reactions perceived by the senses (Anonymous 1975). Descriptive Analysis (DA) is one

type of sensory methodology used to quantitatively profile a product (Stone and Sidel

2004). This method has been used to identify off-flavors in products, for shelf-life and

quality control testing, and in competitor comparison. In DA, a group of panelists serve

as human analytical instruments and are trained to hone in on the key attributes of

12

products. Panelists are trained to describe and evaluate the samples, and to collectively

determine the key product descriptors. There are two main recognized types of

descriptive analysis methods, Quantitative Descriptive Analysis (QDA) and the Spectrum

Method (Meilgaard and others 2006). The QDA method allows panelists to generate and

determine the references used for describing the samples. The Spectrum method includes

a longer panel training time and has a standard set of references to evaluate the products

against.

Studies have been conducted on the acceptability of new functional beverages, but

limited research has been done on the effects of functional ingredients on the sensory

properties of model functional beverage solutions. A challenge in the creation of . .

functional foods is to minimize the off-flavors of the food (Tuorila and Cardello 2002).

The effects of the incorporation of specific functional ingredients must be researched

prior to the addition into a formulation. In a study conducted by Luckow and Delahunty

(2004), the addition of probiotics and prebiotics in orange juice and found that

differences could be detected. A study on the addition of varying concentrations of

caffeine and vitamins to fruit juices was conducted to determine the preference between

juices (Smit and Rogers 2002). Another sensory test was conducted on the acceptable

levels of caffeine incorporated in fruit juice and it was found that up to 350 mg/L was the

maximum acceptable level of caffeine in a mixture of tropical fruit juices (de Sousa and

others 2007). While Camire (2000) determined that there was no significant difference

between orange juice with and without 600 mg of ginseng (20% ginsenosides) per liter.

Camire also determined that ginseng concentrations of 1000 mg/L of orange juice

resulted in a medicinal taste. One study varied the amounts of glucose, fructose, citric

13

acid, and lactic acid in a beverage and found that there was an increase in flavor

perception based on the type of sugar incorporated into the formulation (Stevens 1997).

Research related to the addition of ingredients has suggested that there is a

synergistic effect of including multiple ingredients into a formulation. Previous studies

suggest that mixtures of tastants result in an increase in overall intensity ratings of the

compound mixture (Delwiche 2004). Mutual suppression of both sweetness and

bitterness resulted in the mixture of sucrose and quinine solutions (Frank 2003). Flavors

can be suppressed when mixed with bitter tastes (Allison and Chambers 2000).

Therefore, the more functional ingredients added in a beverage formulation, the more

likely the tastes will be noticed. Stevens'(1997) research found that in complex solutions

containing multiple tastants, a lower concentration of compounds can be tasted rather

than one compound in just a water solution. A concentration of 0.133mM/L caffeine was

necessary for caffeine detection in water, while in a twelve product mixture, only 0.0125

mM/L caffeine was necessary for detection.

2.3 Bitter Taste

Bitterness is one of the basic tastes sensed by humans and is often described as an

unpleasant, sharp taste. Common bitter compounds include caffeine, vitamins, minerals,

soy products, and polyphenols. Some commonly consumed foods that contain bitter

compounds include coffee, chocolate, beer, and citrus fruits. Bitterness of products often

depends on the amount of hydrophobic and hydrophilic groups on the terminal ends of a

compound. In general, compounds with hydrophobic terminal amino acids were more

bitter than compounds containing hydrophilic terminal amino acids (Asao and others

14

1987, Ramos de Armas and others 2004). Some bitter molecules include alkaloids,

terpenoids, glycosides, and flavanoids; these compounds are present in many functional

ingredients. The functional portion of botanicals usually contain the alkaloids and

glycosides, which impart a bitter taste that is difficult to mask (Granato 2002). The

mechanism of bitter taste perception is that bitter molecules bind to taste receptor cells

(TRC) which are coupled with G-proteins. The coupled bitter molecule and G-protein

releases calcium ions into the cell and sends a message to the brain that taste of a food is

bitter. More exposure to bitter produce could result in the tolerance of bitter tastes, and

may enhance the palatability of a product (Lesschacve and Noble 2005).

2.3.1 Bitterness Minimizers

There are various methods used to reduce or minimize the bitter taste in food and

beverage products. These can range from the incorporation of masking agents, the

addition of bitterness minimizers, or the use of compounds which block bitter taste

receptors on the tongue (Reineccius 2004). Other methods include diluting the beverage

so the bitter compound is below the detection threshold (Rouseff 1990), however, this

will lower the amount of active ingredient available in the formulation. There has been

quite a lot research conducted on treatments to reduce the bitterness in food products, but

the effectiveness of each treatment is dependent on the specific food or beverage matrix

and the amount of treatment is determined by the base formulation (Brandt 2001).

Therefore, often times, bitterness masking molecules are determined though a trial and

error process (Ley 2008).

15

2.3.2 Masking Agents

Masking agents impart additional flavor to food products and are often used to

minimize unpleasant tastes or flavors. The most commonly used masking agent that

masks the bitter taste in functional beverages is sweeteners (Roy and Roy 1997,

Schiffman and others 2003). Some artificial sweeteners also impart a bitter taste along

with sweetness; therefore, bitterness masking treatments must be explored to reduce the

additional bitterness. In herbal beverages, the amount of sweeteners are increased to '

increase sweetness perception and to aid in masking bitterness receptors (Katan and Roos

2004).

The incorporation of complimentary flavors is used to minimize the off-flavors of

the base formulation. Studies have been conducted using flavors such as vanilla to mask

off-flavors in soymilk (Gnadt 2007) and cocoa syrup to mask the bitterness of quinine

hydrochloride (Reid and others 1956). Ley conducted difference tests between caffeine

solutions and potential bitterness-masking compounds (Ley and others 2006).

Homoeriodictyol sodium salt reduced the bitterness of a 100 mg/L caffeine solution and

hydroxybenzac acid vanillylamides reduced the bitterness of a 500 mg/L caffeine

solution (Ley and others 2006). Ley also conducted research on comparing solutions

against caffeine references to determine the bitter equivalents (Ley and others 2005).

2.3.3 Molecular Interaction

Research has been conducted on the incorporation of fat or the addition of sodium

gluconate to reduce bitter perception (Keast 2008). The inclusion of fatty acids has been

found to decrease the threshold levels of basic tastes such as bitterness (Mattes 2007).

Amino acids have been studied and found to decrease bitterness of caffeine. The use of

16

taurine and other amino acids have been found to decrease the bitterness of KCl (Tamura

and others 1990). Tasteless peptides, such as taurine, have hydrophobic regions that

might.be useful in minimizing bitterness (Roy and Roy 1997). The use of zinc sulfate

reduced the bitterness of quinine-HCl solutions (Keast and Breslin 2003), although the

addition of zinc sulfate into formulations tends to increase the astringency of solutions.

Another study conducted by Keast (2008) showed that zinc lactate inhibited the bitterness

of caffeine solutions, but also decrease the sweetness intensity.

2.3.4 Bitter Taste Receptor Blockers

Other methods involve blocking bitter receptors on the tongue and adding

chemicals to ingredients that interact with the bitter compounds to reduce the bitter taste

(Ley 2008). Research on bitter taste receptor blocking relies on the understanding of the

bitter taste mechanism. Compounds that fit into the taste receptors or that are too large to

fit in the taste receptors also minimize.

The focus on bitterness minimizers arises from the increase of ingredients and

products which have characteristic bitter tastes. Minimizers that can reduce the bitter

taste without added flavors or side effects are more useful than others. Cyclodextrins

(CDs) have been used to reduce the bitterness in products such as fish flavor, soybean

aromas, and medications (Cravotto and others 2006). It was suggested by Soldo and

Hofmann (2005) that when evaluating the effectiveness of bitterness minimizers, the

change in threshold perception for the detection of bitterness is a better method than

absolute bitterness ratings.

17

2.3.5 Cyclodextrins

Cyclodextrins (CDs) are large ring-shaped molecules created through the

enzymatic conversion of starch. They are an odorless, white powder that are soluble in

water. Common types of CDs are a, [3-, and y-CDs, which consist of 6, 7, and 8

glucosidic units, respectively. This ring-shaped structure allows CDs to trap smaller

molecules, thus forming inclusion compounds. This unique structure gives CDs many

useful food-related applications.

Cyclodextrins can be used as an emulsifier, a stabilizer for fragile compounds

such as flavors, colors, amino acids, and vitamins (Dodziuk 2006), a solubilizer for

pharmaceuticals (Loftsson and Brewster 1996), and a stabilizer in foods such as cookies

and chewing gums (Dodziuk 2006). CDs have also been used to create lower cholesterol

products by forming inclusions of cholesterol in CDs and then taking the CD

complexalions out of the product; this method has been used to lower cholesterol egg

products (Smith and others 1995). According to the FDA, all a, P-, and y-cyclodextrins

(CDs) are generally recognized as safe (GRAS) for use as a stabilizer, emulsifier, carrier,

and formulation in foods at levels between 1 to 5%.

Cyclodextrins have been used to minimize the bitterness of compounds. P-CDs at

a 0.4% concentration was able to reduce the bitterness of a 0.05% caffeine solution by

90% (Binello and others 2004). p-CDs have been shown to reduce the bitterness of

ginseng tea (Takeuchi and Naae 1992). The success of P-CDs as a bitterness minimize!-

was more than the use of a- and y-CDs, possibly because high amounts of p-CDs impart a

sweet taste (Binello and others 2004). It was also found that CDs remove the bitter taste

of sweeteners such as stevioside (Astray and others 2009). A study on y-CDs found that

18

there was no difference between consuming yogurt containing 8 g y-CDs/100 g versus no

cyclodextrins present in yogurt (Koutsou and others 1999).

2.4 Sorting and Categorization Mclhods

Sorting methods have been used in many fields to understand the relationship

between products or concepts (Rugg and McGeorge 1997, Viswanathan and Childers

1999). These methods allow researchers to gather information about panelists'

perceptions of a large group of products (Viswanathan and Childers 1999) and provides

structured information about the relationship among products (Mervis and Rosch 1981).

Sorting tasks have also been commonly used as marketing tools as a way to obtain

consumer insight on product perception and for comparison to competitor's products.

Sorting tasks can range from having panelists sort products into predefined categories, to

allowing panelists to freely sort the products. Different types of sorting procedures range

from sorting cards containing descriptor words to having panelists evaluate and sort food

products.

Sorting methods can be used as a quick and simple method to gather descriptors

and relationship information about products and are much less time-consuming compared

to other methods of sensory evaluation. These methods require minimal panelist training

and can often be conducted in one session. Sorting methods are useful when gathering

information on large groups of products (Cartier and others 2006), while other methods

such as quantitative descriptive analysis (QDA) produce more detailed information on a

smaller group of products.

19

Prior to their applications to food, these sorting methods have been applied to

nonfood materials such as car fabrics (Giboreau and others 2007), colored plastic chips

(Faye and others 2004), and oral health care products (Bertino and Lawless 1993).

Sorting methods were first introduced to sensory work in 1995 when Lawless and others

conducted work on different cheeses (Lawless and others 1995). This method lias been

successful in sorting other food items such as types of water (Falahee and MacRae 1997),

snack bars (King and others 1998), red wine (Gawel and others 2000), novel food

products (Woolf and others 2002), and yogurts (Saint-Eve and others 2004).

Free sorting is a sorting method which is simple and only requires objects to be

sorted, criteria for objects to be sorted, a record sheet, and instructions (Coxon 1999).

This method has few restrictions which include that more than one category must be

created and that all objects must be sorted into mutually-exclusive categories (Lim and

Lawless 2005). Free sorting incorporates panelists' normal thought processes into the

categorization of products and results in candid opinions. Another type of sorting method

is fixed sorting, which involves a sorting products based on predefined criteria (Coxon

1999). Panelists are confined in their sorting by either set categories or definitions.

Another sorting method is the projective mapping, which involves placing

products on a blank sheet of paper by similarities and differences in attributes in two

dimensions (Perrin and others 2008). The more similar the products are, the closer they

are placed on the sheet of paper. The paper is later divided and marked into uniform

squares to determine the distance of the products from one another. This method has

been used to sort ewe's milk cheeses (Barcenas and others 2004), wines (Pages 2005) and

orange juice (Nestrud and Lawless 2008). Flash profiling is another sensory method in

20

which panelists evaluate the products and come up with their own descriptive terms to

describe the products and cluster similar products together (Delarue and Sieffermann

2004, Tarea and others 2007).

When analyzing sorting data, results are based on consensus of the panel and do

not account for individual differences. Sorting data is analyzed through patterns seen in

the data and the relationships between the products sorted. A multidimensional scaling

(MDS) plot is used to understand the relationship between objects when underlying

dimensions are unknown (Schiffman and others 1981). MDS plots are used as an

analysis tool, as well as to visually understand the relationships among products and are

based on comparing data in a similarity or dissimilarity matrix. This similarity matrix is

based on the frequency in which products are sorted and placed together. Stress values

and correlations are used to determine the reliability of a sorting method. In a MDS plot,

the stress value indicates the goodness of the fit of all the data into the two-dimensional

plot. The more objects to be compared and plotted will result in larger stress values

(Borg and Groenen 2003).

There is currently no universal standard method to compare the generated clusters

from MDS plots or sorting methods. The RV coefficient (Risvik and others 1994, Szejtli

and Szente 2005, Abdi 2007) has been used to calculate the agreement in categories

generated by two different methods. The RV coefficient is calculated by comparing

configurations of the categories on a plot; therefore, the data must be transformed prior to

analysis such that each object has an X and Y coordinate point. The RV coefficient

ranges from 0 to 1, and a value close to 1 means that the two configurations have

excellent agreement. This method has been used as a means to validate categories

21

generated in various sorting research (Faye and others 2004, Carder and others 2006,

Lelicvre and others 2008). Tang and Heymann (2002) also calculated an RV coefficient

to compare the similarity between multidimensional sorting, similarity scaling and a free

choice profiling of grape jellies.

Another method to assess the agreement of the clusters generated between

methods is the Rand Index (RI) or Rand'Measure (Rand 1971) which has been used to

compare clustered data to determine the level of agreement in categories. The Rl (Rand

1971) examined the similarities between the agreements and disagreements through the

comparison of the results from the two sorts. The RI compares the generated categories

from two different sorting methods or generated category sorting results to established

categories. Unlike the RV coefficient, the data being compared does not have to be

transformed into a plot. A RI value ranging from 0 to 1 was calculated based on the

number of agreements and disagreements were seen through the comparison of the results

from the two sorts. An ARI value ranges from 0 to 1 and explains the correspondence

between the categories of the two compared sorts, with a value of 1 signifying that the

two sorts were exactly the same. An Adjusted Rand Index (ARI) (Hubert and Arabie

1985) was then developed to adjust for chance agreement between the two sorts. In

Steinly's research (2004), the same clustering method was compared and the RI was a

higher value than the ARI.

Therefore, the ARI is a more sensitive scale than RI for determining the degree of

correspondence between two sorts. The validity of the quality of the cluster recovery of

the ARI value is: >0.86 is in the 95th percentile, 0.77 is in the 90lh percentile, and 0.67 is

in the 85th percentile (Steinley 2004). An ARI less than 0.65 is considered poor recovery

22

of data, a value greater than 0.80 is considered good recovery, while a value greater than

0.90 is considered excellent recovery (Steinley 2004). The ARI is more commonly used

as an analysis tool in other fields of study than Food Science and Sensory Science

(Soufflet and others 2004), although there are a few food-related studies that used the RI

to compare groupings of products (Cartier and others 2006).

Categories provide insight on the attributes most representative of a group of

similar products (Mervis and Rosch 1981). If there are no specific criteria, panelists

generate their own context to compare the similarities of products to identify underlying

similarities to tie the products together. Research conducted on sorting and

categorization methods have been studied to validate Icnown categories (Viswanathan and

Childers 1999).

2.5 Concluding Remarks

Based on the works mentioned, it appears that there are many unanswered

questions regarding the ever-changing functional beverage market. There is no apparent

set of standard functional beverage categories in the US that is easily accessible to the

general public. The development of functional beverage categories is vital to

understanding and regulating the functional beverage market to lessen the confusion in

product marketing, consumer expectations, and legal guidelines. Defined functional

beverage categories would be useful during the development of products because it will

aid in targeting specific characteristics that match the category.

Few studies have been conducted on the individual effects of the incorporation of

functional ingredients into food or beverage matrices. It is important to investigate the

23

sensory effects of the inclusion of functional ingredients to create successful products.

This research seeks to investigate the creation of functional beverage categories. It also

aims to help create better tasting functional beverages through the research of the effects

of incorporating functional ingredients into the matrix and investigating a solution to

minimize the bitter taste of ginseng.

2.6 References

Anonymous 2008. U.S. Functional Beverages Market: A Young Market with Growing Popularity. Food Industry News [serial online]. Available from Posted 28 May 2008 2008.

Anonymous 1975. Minutes of Division Business Meeting. Institute of Food Technologists-Sensory Evaluation Division, IFT.

Abdi H. 2007. The RV coefficient and the congruence coefficient. Encyclopedia of measurement and statistics.Thousand Oaks (CA): Sage

Alldrick AJ. 2006. Functional beverages: satisfying consumers' needs. Agro Food Industry Hi Tech 17(4):41.

Allison ANNMA, Chambers E. 2000. The Importance Of Caffeine As A Flavor Component in Beverages. J Sens Stud 15(4):449-57.

Asao M, Iwamura I-I, Akamatsu M, Fujita T. 1987. Quantitative structure-activity relationships of the bitter thresholds of amino acids, peptides, and their derivatives. J Med Chem 30(10): 1873-9.

Astray G, Gonzalez-Barreiro C, Mejuto JC, Rial-Otero R, Simal-Gandara J. 2009. A review on the use of cyclodextrins in foods. Food Hydrocoll 23(7): 1631-40.

Barcenas P, Elortondo FJP, Albisu M. 2004. Projective mapping in sensory analysis of ewes milk cheeses: A study on consumers and trained panel performance. Food Res Inl 37(7):723-9.

Berry D. 2009. Powerful Beverage Performers. Food Product Design [serial online]. 32-50. Available from Posted 2009.

Bertino M, Lawless HT. 1993. Understanding mouthfeel attributes: a multidimensional scaling approach. J Sens Stud 8(2): 101-14.

24

Binello A, Cravotto G, Nano GM, Spagliardi P. 2004. Synthesis of chitosan-cyclodextrin adducts and evaluation of their bitter-masking properties. Flavour Fragrance J 19(5):394-400.

Borg I, Groenen P. 2003. Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement 40(3):277-80.

Brandt LA. 2001. Flavor Masking: Strategies for Success. Prepared Foods [serial online]. (July 2001):Available from Posted 2001.

Burg JC. 1998. High-Energy Beverages. Food Product Design

Camire ME. 2000. Dietary Supplements. In: M. K. Schmidl, T. P. Labuza, editors. Essentials of Functional Foods. Gaithersburg: Aspen Publishers, Inc. pi 65-180.

Cartier R, Rytz A, Lecomte A, Poblete F, Krystlik J, Belin E, Martin N. 2006. Sorting procedure as an alternative to quantitative descriptive analysis to obtain a product sensory map. Food Qual Pref 17(7-8):562-71/

Coon .IT, Ernst E. 2002. Panax ginseng: a systematic review of adverse effects and drug interactions. Drug Saf 25(5):323-44.

Cosgrove J. 2008. Caffeinated Snacks. Nutraceuticals World [serial online], July/August 2008Available from Posted July 2008.

Court WE. 2000a. Ginseng: The Genus Panax. Singapore: CRC Press. 266 p.

Court WE. 2000b. The Pharmacology and Therapeutics of Ginseng. In: Anonymous Ginseng: The Genus Panax. Singapore: Harwood Academic Publishers, pi 17-197.

Coxon APM. 1999. Sorting Data: Collection and Analysis. Sage Publications Inc.

Cravotto G, Binello A, Baranelli E, Carraro P, Trotta F. 2006. Cyclodextrins as Food Additives and in Food Processing. Current Nutrition & Food Science 2(4):343-50.

de Sousa PHM, Maia GA, de Azeredo HMC, de Souza Filho MSM, Garruti DS, de Freilas CAS. 2007. Mixed tropical fruit nectars with added energy components, hit J Food Sci Tech 42(11): 1290-1296.

Delarue J, Sieffermann JM. 2004. Sensory mapping using Flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products. Food Qual Pref 15(4):383-92.

Delwiche J. 2004. The impact of perceptual interactions on perceived flavor. Food Qual Pref 15(2): 137-46.

25

Dodziuk I-I. 2006. Cyclodextrins and their complexes: chemistry, analytical methods, applications. Weinheim: Wiley-VCH. 489 p.

Drewnowski A, Gomez-Carneros C. 2000. Bitter taste, phylonutrienls, and the consumer:" a review. Am J Clin Nutr 72(6): 1424-35.

Falahee M, MacRae AW. 1997. Perceptual variation among drinking waters: the reliability of sorting and ranking data for multidimensional scaling. Food Qual Pref 8(5):389-94.

Farrell J, Ries NM, Kachan N, Boon H. 2009. Foods and natural health products: Gaps and ambiguities in the Canadian regulatory regime. Food Policy

Faye P, Bremaud D, Durand Daubin M, Courcoux P, Giboreau A, Nicod H. 2004, Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings. Food Qual Pref 15(7-8):781-91.

Food and Drug Administration. 2003. Code of Federal Regulations Title 2 Sec. 182.1180.

Frank RA. 2003. Response context affects judgments of flavor components in foods and beverages. Food Quality and Preference 14(2): 139-45.

Gawel R, Oberholster A, Francis IL. 2000. A 'mouth-feel wheel': terminology for communicating the mouth-feel characteristics of red wine. Australian journal of grape and wine research.6(3):203-07.

Giboreau A, Dacremont C, Egoroff C, Guerrand S, Urdapilleta I, Candel D, Dubois D. 2007. Defining sensory descriptors: Towards writing guidelines based on terminology. Food Qual Pref 18(2):265-74.

Gnadt T. 2007. Effect of Masking Agents and Lactones on the Aroma and Flavor of Soymilk. [dissertation]. University of Illinois at Urbana-Champaign.

Granato H. 2002, Manipulating Flavor Perception in Functional Products. Natural Products Insider [serial online]. Available from Posted 8 April 2002 2002.

Hubert L, Arabie P. 1985. Comparing partitions. Journal of classification 2(1):193-218.

Humphries G. 2007. Nutraceutical Soft Drinks: Innovation in sports, energy, dairy, and functional beverages.

Institute of Food Technologists. 2005. Functional Foods: Opportunities and Challenges.

Jung MY, Jeon BS, Bock JY. 2002. Free, esterified, and insoluble-bound phenolic acids in while and red Korean ginsengs (Panax ginseng CA Meyer). Food Chem 79(1): 105-11.

26

Katan MB, Roos NM. 2004. Promises and Problems of Functional Foods. Crit Rev Food SciNutr44(5):369-77.

Keast RSJ. 2008. Modification of the bitterness of caffeine. Food Qual Pref 19(5):465-72.

Keast RSJ, Breslin PAS. 2003. An overview of binary taste-taste interactions. Food Qual Pref 14(2):111-24.

Kim . 1985. Effects of temperature and sugar addition on the flavor of ginseng lea. Korean Journal of Food Science and Technology. 17(4):304.

King MC, Cliff MA, Hall JW. 1998. Comparison of Projective Mapping and Sorting • Data Collection and Multivariate Methodologies for Identification of Similarity-Of-Use Snack Bars. J Sens Stud 13(3):347-58.

Kitts D, Hu C. 2000. Efficacy and safety of ginseng. Public Health Nutr 3(4A):473-85.

Koutsou GA, Storey DM, Bar A. 1999. Gastrointestinal tolerance of y-cyclodextrin in humans. Food Additives & Contaminants 16(7):313-7.

Lai GG. 2007. Getting Specific with Functional Beverages. Food Technology [serial online], 61(12):Available from Posted 2007.

Lawless HT, ShengN, Knoops SSCP. 1995. Multidimensional scaling of sorting data applied to cheese perception. Food Qual Pref 6(2):91-8.

LeClair K. 2000. Breaking the Sensory Barrier for Functional Foods. Food Product Design [serial online], 6 November 2006. Available from http://www.foodproductdesign.com/articles/462/462 0297DE.html. Posted 1 September 2000.

Lelicvre M, Chollet S, Abdi H, Valentin D. 2008. What is the validity of the sorting task for describing beers? A study using trained and untrained assessors. Food Qual Pref

Lesschaeve I, Noble AC. 2005. Polyphenols: factors influencing their sensory properties and their effects on food and beverage preferences. Am J Clin Nutr 81(1 Suppl):330S-5S.

Leveen T. 2007. Functional Beverages: Market Evolution. Natural Products Marketplace [serial online]. Available from Posted 2007.

Ley JP, Blings M, Paetz S, Krammer GE, Bertram HJ. 2006. New bitter-masking compounds: hydroxylated benzoic acid amides of aromatic amines as structural analogues of homoeriodictyol. J Agric Food Chem 54(22):8574-9.

Ley JP. 2008. Masking Bitter Taste by Molecules. Chemosensory Perception l(l):58-77.

Ley JP, Krammer G, Reinders G, Gatfield IL, Bertram HJ. 2005. Evaluation of bitter masking flavanones from Herba Santa (Eriodictyon californicum (H. & A.) Ton-., Hydrophyllaceae). J Agric Food Chem 53(15):6061-6.

Lim J, Lawless HT. 2005. Qualitative differences of divalent salts: multidimensional scaling and cluster analysis. Chem Senses 30(9):719-26.

Loftsson T, Brewster ME. 1996. Pharmaceutical applications of cyclodextrins. 1. Drug solubilization and stabilization. J Pharm Sci 85(10):

Lord JB. 2000. New Product Failure and Success. In: A. L. Brody, J. B. Lord, editors. Developing New Food Products for a Changing Marketplace. Lancaster: Technomic • Publishing Co, Inc. p55-86.

Lourenco R, Camilo ME. 2002. Taurine: a conditionally essential amino acid in humans? An overview in health and disease. Nutr Hosp 17(6):262-70.

Mattes RD. 2007. Effects of linoleic acid on sweet, sour, salty, and bitter taste thresholds and intensity ratings of adults. AJP-Gastrointestinal and Liver Physiology 292(5):G1243.

Meilgaard M, Civille GV, Carr BT, 2006. Sensory Evaluation Techniques. CRC Press.

Mervis CB, Rosch E. 1981. Categorization of Natural Objects. Annual Reviews in Psychology 32(1):89-115.

Minor LJ, Pearson AM, Stine CM. 1966. Chicken Flavor Studies. J Agric Food Chem 14(4):416-9.

Mintel. 2009. Energy Drinks and Energy Shots-US.

Mintel. 2008. Functional Beverages-US August 2008. Mintel Reports.

Mobini S, Elliman TD, Yeomans MR. 2005. Changes in the pleasantness of caffeine-associated flavours consumed at home. Food Qual Pref 16(8):659-66.

Nestrud MA, Lawless HT. 2008. Perceptual mapping of citrus juices using projective mapping and profiling data from culinary professionals and consumers. Food Qual Pref 19(4):431-38.

Ohki K, Nakamura Y, Takano T. 2004. The Japanese Nutritional Health Beverage Market. In: Anonymous Beverages in Nutrition and Health. Totowa: Humana Press. p377-387.

Packaged Facts. 2009. Functional Foods and Beverages in the U.S. 1-210.

• 28

Pages J. 2005. Collection and analysis of perceived product inter-distances using multiple factor analysis: application to the study of 10 white wines from the Loire Valley. Food Qual Pref 16(7):642-9.

Perrin L, Symoneaux R, Maitre I, Asselin C, Jourjon F, Pages J. 2008. Comparison of three sensory methods for use with the Napping procedure: Case often wines from Loire valley. Food Qual Pref 19(1): 1-11.

Ramos de Armas R, Gonzalez Diaz H, Molina R, Perez Gonzalez M, Uriarle E. 2004. Stochastic-based descriptors studying peptides biological properties: modeling the bitter lasting threshold of dipeptides. Bioorg Med Chem 12(18):4815-22.

Rand WM. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 846-50.

Reid AW, Becker CH, REID AW. 1956. The use of cocoa syrups for masking the taste of quinine hydrochloride. J Am Pharm Assoc Am P.harm Assoc 45(3): 151-2.

Reineccius GA. 2004. Flavoring Systems for Functional Foods, In: T. Wilson, N. J. Temple, editors. Beverages in Nutrition and Health. Totowa: Humana Press. p89-97.

Risvik E, Mcewan JA, Colwill JS, Rogers R, Lyon DM. 1994. Projective mapping: a tool for sensory analysis and consumer research. Food Qual Pref 5(4):263-9.

Rouseff RL. 1990. Bitterness in Food Products and Beverages. Amsterdam: Elsevier. 356 P-

Roy G, Roy GM. 1997. Modifying Bitterness: Mechanism, Ingredients, and Applications. CRC Press.

Rugg G, McGeorge P. 1997. The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Syst 14(2):80-93.

Saint-Eve A, Paci Kora E, Martin N. 2004. Impact of the olfactory quality and chemical complexity of the flavouring agent on the texture of low fat stirred yogurts assessed by three different sensory methodologies. Food Qual Pref 15(7-8):655-68.

Schiffman SS, Reynolds ML, Young FW. 1981. Introduction to Multidimensional Scaling: Theory, Methods, and Applications. New York: Academic Press. 413 p.

Schiffman SS, Sattely-Miller EA, Graham BG, Zervakis J, Butchko HH, Stargel WW. 2003. Effect of repeated presentation on sweetness intensity of binary and ternary mixtures of sweeteners. Chem Senses 28(3):219-29.

Schmidl MK, LabuzaTP. 2000. Essentials of Functional Foods. Gaithersburg: Aspen Pub. 395 p.

Smit HJ, Rogers PJ. 2002, Effects of'energy' drinks on mood and mental performance: critical methodology. Food Qual Pref 13(5):317-26.

Smith DM, Awad AC, Bennink MR, Gill JL. 1995. Cholesterol reduction in liquid egg yolk using p-cyclodextrin. J Food Sci 60(4):691-4.

Soldo T, Hofmann T. 2005. Application of Hydrophilic Interaction Liquid Chromatography/Comparative Taste Dilution Analysis for Identification of a Bitter Inhibitor by a Combinatorial Approach Based on Maillard Reaction Chemistry, J Agric Food Chem 53(23):9165-71.

Soufflet I, Calonnier M, Dacremont C. 2004. A comparison between industrial experts' and novices' haptic perceptual organization: a tool to identify descriptors of the handle of fabrics. Food Quality and Preference 15(7-8):689-99.

Spiller MA. 1998. Caffeine. Boca Raton: CRC Press. 363 p.

Steinley D. 2004. Properties of the Hubert-Arabie adjusted Rand index. Psychol Methods 9(3):386-96.

Stevens JC. 1997. Detection of very complex taste mixtures: generous integration across constituent compounds. Physiol Behav 62(5): 1137-43.

Stone H, Sidel JL. 2004. Sensoiy Evaluation Practices. Academic Press.

Szejtli J, Szente L. 2005. Elimination of bitter, disgusting tastes of drugs and foods by cyclodextrins. Eur J Pharm Biopharm 61 (3): 115-25.

Takeuchi T, Naae N. 1992. Stationary phase effect on enantioselectivity of dansyl phenylalanine in microcolumn liquid chromatography with y-cyclodextrin as mobile phase additive. J Chromatogr 595(1-2): 121-4.

Tamura M, Mori N, Miyoshi T, Koyana S, Kohri H, Okai H. 1990. Practical Debittering Using Model Peptides and Related Compounds. Agric Biol Chem 54(1):41-51.

Tang C, Heymann I-I. 2002. Multidimensional Sorting, Similarity Scaling and Free-Choice Profiling of Grape Jellies. J Sens Stud 17(6):493-509.

Tarea S, Cuvelier G, Sieffermann JM. 2007. Sensory Evaluation of the Texture of 49 Commercial Apple and Pear Purees. J Food Qual 30(6): 1121-31.

Tuorila I-I, Cardello AV. 2002. Consumer responses to an off-flavor in juice in the presence of specific health claims. Food Qual Pref 13(7-8):561-9.

Urala N, Lahteenmaki L. 2003. Reasons behind consumers functional food choices. Nutr Food Sci 33(4):148-58.

30

Viswanathan M, Childers TL. 1999. Understanding How Product Attributes Influence Product Categorization: Development and Validation of Fuzzy Set-Based Measures of Gradedness in Product Categories. J Market Res 36(l):75-94.

V.ogler BK, Pittler MH, Ernst E. 1999. The efficacy of ginseng. A systematic review of randomised clinical trials. Eur J Clin Pharmacol 55(8):567-75.

Vuksan V, Sievenpiper JL. 2005. Herbal remedies in the management of diabetes: Lessons learned from the study of ginseng. Nutrition, Metabolism and Cardiovascular Diseases 15(3): 149-60.

Warskulat U, Flogel U, Jacoby C, Hartwig HG, Thewissen M, Merx MW, Molojavyi A, Heller Stilb B, Schrader J, Haussinger D. 2004. Taurine transporter knockout depletes muscle taurine levels and results in severe skeletal muscle impairment but leaves cardiac function uncompromised. FASEB Journal 18(3):577.

Woolf A, Wright R, Amarasiriwardena C, Bellinger D. 2002. A child with chronic manganese exposure from drinking water. Environ Health Perspect 110(6):613-6.

Wright R. 2008. Nutraceuticals Coast in the Beverage Market. Nutraceuticals World [serial online]. Available from Posted July 2008.

Yeomans MR, Mobini S, Chambers L. 2007. Additive effects of flavour-caffeine and flavour-flavour pairings on liking for the smell and flavour of a novel drink. Physiol Behav92(5):831-9.

Yu J, Kim AK. 2009. Effect of Taurine on Antioxidant Enzyme System in B16F10 Melanoma Cells. In: Anonymous Taurine 7. New York: Springer. p491.

31

2.7 Tables

Table 2.1: Amount of Functional Ingredients in Commercially-available Energy Drinks purchased in Sept

Product Name Cocaine Full Throttle MDX No Fear NOS Red Bull Red Jak Rockstnr Sobe Adrenaline Rush Tab Eiicray

Serving Size (mL) 248 237 237 237 237 245 237 237 245 310

ember 2006

Caffeine (nifi) 2?0 72 47 87 125 80 82 80 79 95

Taurine (nig) 750 605

U 1000 1000 1000 947 1000 1000 785

B2 (mg) 0.0 0.0 0.0 0,0 0.0 0.0 0.0 5.8 0.0 0.0

B3 (nig)

0 6 0 0 0

28 0

20 0 6

B5 (nig)

0 0 0 0 0 7 0 10 0 0

B6 ("iR)

6 0 0 2 2 7 5 2 5 1

BI2

(MR) 36.0 0.6 0.0 6.0 6,0 7.2 4.8 6.0 6.0 1.2

Ginseng Extract

(i"R) 0

90 0 0 0 0 0 0 0

116

Panax Ginseng Extract

(nig) 0 0 il

50 50 0

100 25 25 0

// denotes that ingredients were listed on the Nutritional Facts labels, but ingredient amounts were unavailable.

CHAPTER 3 - CATEGORIZATION OF COMMERCIALLY-AVAILABLE FUNCTIONAL BEVERAGES BY CHEMICAL, PHYSICAL, AND SENSORY COMMONALITIES

3.1 Abstract

The significant influx of a wide variety of commercially-available functional

beverages has resulted in a beverage segment that is not clearly defined or understood.

Companies are now producing hybrid-type products that combine multiple concepts into

a single beverage. The lack of categories and definitions of these product types in the

functional beverage segment may result in consumer confusion and purchase avoidance

due to the uncertainty of product characteristics.

The objectives of this research were to determine the main categories of

functional beverages through classification by chemical, physical, and sensory

commonalities of the beverages and to evaluate the effectiveness of these categorization

methods. Three categorization methods: 1) ingredient inventory, 2) flow behavior

comparison, and 3) two-step sensory sorting were used to categorize fifty commercially-

available functional beverages. Of the three categorization methods, the two-step sorting

method produced the most distinctive and defined categories. The seven functional

beverage categories generated were: Enhanced Waters, Energy Drinks, Fruit Smoothies,

Nutritional Drinks, Sports Drinks, Teas, and Yogurt Smoothies. The ingredient inventory

and flow behavior comparison methods have the potential to be useful sorting methods,

but did not generate as distinct functional beverage categories as compared to the two-

step sensory sorting method.

The discrepancies between consumer expectations and the chemical, physical, and

sensory properties of beverages can be elucidated by detailed definitions and descriptions

33

of the generated categories, which will also aid in successful marketing and formulation

of new products.

Key Words: categorization, functional beverages, sorting, sensory evaluation

3.2 Introduction

Functional Beverages

In the US, concerns about health and disease have increased the popularity of

functional food products (Schmidl and Labuza 2000, Urala and Lahteenmaki 2003).

These "total health" and weight management concerns have prompted growth in the

number of functional food and beverage products available in the market (Lai 2007). In

2006, there were over $7.6 billion in functional beverage sales in the US, and by 2010

functional beverage sales are projected to increase to over $9.9 billion (Humphries 2007).

The functional beverage boom has led to a rapid increase in new products, as

companies attempt to capitalize on this emerging market (Wright 2008). In the US,

however, there is currently no standard definition of a "functional beverage" or defined

categories for functional beverages. Beverage categories are referred to in market

research reports (Mintel 2008, Packaged Facts 2009), but there are no standard categories

and definitions of functional beverages. To the researchers' knowledge, there have been

no science research-based attempts to determine and define functional beverage

categories on the market. Based on products currently on the market and trade magazines,

functional beverages encompass beverages containing probiotics, stimulants, or vitamins

and minerals. The significant increase in functional beverage popularity has also led to

the introduction of new "hybrid-type" beverages. These hybrid-type beverages

incorporate multiple concepts and are considered "functional beverages" because they

34

provide unique health benefits. For example, there are now diet energy drink juices and

thick, dairy-based diet functional beverages on the market.

For maximum consumer satisfaction, it is vital that product taste, texture, and

flavor match consumers' expectations (Orth and de Marchi 2005). Functional beverages

in the market must meet consumers' sensory expectations of the labeling and image of

the product. Therefore, determining and defining functional beverage categories will aid

in proper product positioning that match consumers' expectations.

Sorting and Categorization Methods

Sorting methods have been used in many.fields of study to understand the

relationship between products or concepts (Rugg and McGeorge 1997, Viswanathan and

Childers 1999). These sorting tasks are also commonly used as marketing tools as a way

to obtain consumer insight on product perception and comparison to competing products.

Sorting tasks can range from panelists sorting products into predefined categories, to

panelists freely sorting products. Different types of sorting procedures range from

sorting cards containing descriptor words to having panelists evaluate and sort food

products (Lawless and Glatter 1990, Lawless and others 1995, Rugg and McGeorge

1997, Tang and Heymann 2002,). Categorizing products and determining where products

"fit" in the market can be obtained through sorting tasks. Category membership aids in

understanding consumer expectations of a product (Yamauchi and Markman 2000) and

provides a clear concept in consumers' minds, which is important in purchase intent of

specific products (Lord 2000).

Distinguishing characteristics of functional beverage categories may be attributed

to the ingredients incorporated in a formulation, Amendola and others (2004)

35

categorized the difference between energy drinks and sports drinks based on the chemical

differences between the products. While a study conducted by Kappes and others (2007)

showed that commercial cola and lemon-lime flavored carbonated beverages could be

clustered into groups based on physical property measurements such as pH, viscosity,

titratable and Brix levels.

Categorizing functional beverages based on ingredient similarities and physical

properties may provide insight in determining beverage categories. The selection of

functional or base ingredients for beverage formulation may play a role in defining a

beverage category. The objectives of this research were to determine the main categories

of functional beverages through classification by chemical, physical, and sensory

commonalities of the beverages and to evaluate the effectiveness of these categorization

methods.

3.3 Materials and Methods

Functional Beverages

Fifty ready-to-drink'functional beverages (Table 3.1) were purchased from three

local supermarkets (County Market, Meijer, and Schnucks) in Champaign-Urbana,

Illinois in September 2006. Brand-name products were purchased, while generic or store

brand products were not included in the study, to minimize any bias associated with the

lack of marketing of these products. If multiple flavors of a product were available, the

original flavor or a derivative of a berry flavor was purchased to minimize dissimilarities

due to flavorings or colorants.

36

Ingredient Inventory Categorization

Ingredients listed on the Nutrition Facts labels on each beverage package were

entered into a Microsoft Excel spreadsheet (Microsoft Corporation). Over 200 different

ingredients were compiled; however, the specific quantities of ingredients in each

beverage were not included in the analysis. A dissimilarity co-occurrence matrix was

created using XLStat, 2008.4.02 (Addinsoft) to compare the number of identical

ingredients that were contained in each functional beverage pair. The co-occurrence

matrix was followed by a two-dimensional multidimensional scaling (MDS) plot with

Kruskal's stress-1 (Kruskal 1964) to determine the relationships among products due to

ingredient commonalities. The two dimensions plotted had a random initial

configuration, and multiple iterations were run to create a MDS plot with the lowest

stress level.

Flow Behavior Comparison Categorization

The flow behavior properties of the fifty functional beverages were measured

using the Advanced Rheometric Expansion System Rheometric Fluid Spectrometer

Model III (ARES RFS III), the SR5 Peltier Circulator (TA Instruments), and the TA

Orchestrator Software Version 8.03 (TA instruments). Data were analyzed using XLStat

2008.4.02 (Addinsoft).

The calibration and flow behavior methods of Kappes and others (2006) were

used to measure beverage flow behavior. Beverage samples in the amount of 1.11 mL

were placed between two parallel plates (50 mm diameter) with a 5 mm gap, and flow

behavior was measured using a rate sweep test in log mode program beginning at 0.6 s"1

until 200 s"1. Three replications of each beverage were measured at 20°C. Instrument

37

calibration was confirmed by measuring two oil standards (N2 [2.188 mPa • s], N.8

[0.641 mPa • s], Cannon Instrument Co.) at 20°C and deionized distilled water at 20°C.

The measured value of the deionized distilled water was 1.003±0.015 mPa-s.

Functional beverages that were not homogenous were shaken prior to each sample

measurement, Carbonated beverages were decarbonated prior to each measurement to

eliminate the interference of carbon dioxide bubbles with viscosity measurements

(Kappes and others 2006). The decarbonation method described in Kappes (2006), which

consists of microwaving and stirring, was used to decarbonate the beverages.

Flow behavior for each sample was determined based on the slope of shear stress

over shear rate. Functional beverages were classified as Newtonian if the relationship

between shear stress and shear rate was linear and non-Newtonian if the relationship was

non-linear. The viscosities of the Newtonian functional beverages were calculated using

the ratio of shear stress (dyne • cm" ) over shear rate (s ). The apparent viscosities of

each non-Newtonian beverages were calculated by the ratio of shear stress (dyne • cm"2)

over shear rate (s'1) at a shear rate of 50 s"1, which is a representative shear rate value that

food and beverages experience in the mouth (Hollowood and others 2002, Cook and

others 2003).

Beverages were categorized by their average viscosity values using an

agglomerative hierarchical cluster (AHC) analysis. The AHC used Euclidean distance

and Ward's method (1963) of agglomeration criterion with an automatic truncation at the

largest relative increase of dissimilarity between groups using XLStat, 2008.4.02

(Addinsoft). Two additional AHC analyses were conducted on the thirty-three

Newtonian beverages and the seventeen non-Newtonian beverages.

38

Two-Step Sensory Sorting Categorization

Part 1-Free Sort

The free sort consisted of a fourteen-member untrained panel (3 males, 11

females; 18 to 50 years old) categorizing the fifty functional beverages by both visual and

visual-oral assessments. All beverages were placed around a table and panelists were not

restricted to the order they evaluated the beverages. A set of blank worksheets and a

sheet of sticker labels with functional beverage product names were given to each

panelist. Panelists were instructed to group the beverages into self-defined, mutually

exclusive categories and describe each category with common, key characteristics.

Panelists independently completed the sorting tasks and created category names and key

descriptors for each generated category and wrote them down on the worksheets. The

use of the sticker labels regulated the sorting process by eliminating panelists' ability to

place a beverage into multiple categories and to make sure that all beverages were placed

into at least one category.

For the visual assessment, panelists sorted beverages based on common visual

characteristics. These visually observable characteristics could include any visual cues,

such as packaging information, perceived opaqueness, color, or thickness of the

beverage. In addition, previous exposure to the product, such as advertising influence,

was allowed in the visual sort and the visual-oral sort. This method has few restrictions

which include that more than one category must be created and that all objects must be

sorted into mutually-exclusive categories (Lim and Lawless 2005). After the visual sort

was completed the same sorting process was applied for the visual-oral evaluation of the

same products. Again panelists were provided with a set of blank worksheets and a sheet

39

of sticker labels to aid in the sorting process. This sort was labeled a visual-oral sort

because panelists were allowed to view and taste the functional beverages during the

• sorting process. Panelists were instructed to sort the beverages primarily focusing on oral

evaluations. Characteristics generated from the visual-oral evaluation included

mouthfeel, level of sweetness, level of bitterness, and prior experience with the product.

Category names and characteristics were determined based on descriptor words and

category names most commonly generated by the fourteen panelists. If more than half of

the panelists used the same or similar descriptors, these characteristics were used to

describe the categories.

The qualitative data generated from the visual and visual-oral sorts were then

transformed into quantitative data through a series of statistical analyses. First, to

compare the frequency in which functional beverages were grouped together, a similarity

co-occurrence matrix was created using XLStat 2008.4.02 (Addinsoft). The co­

occurrence matrix was followed by a two-dimensional multidimensional scaling (MDS)

with Kruskal's stress-1 for a visual representation of the plot.

Functional beverages were clustered together on the MDS plot based on the

number of times each beverage was paired with another beverage. The more often two

beverages were placed in the same category by panelists during the sorts, the closer the

two beverages appeared on the MDS plot. Category names and definitions were

determined based on the most commonly used descriptors and category names generated

by the fourteen panelists.

K-means clustering was conducted on the beverage coordinate points on the MDS

plot to determine which beverages belonged in each generated category. The theory

40

behind the K-means clustering method is that neighboring objects are clustered into

tentative groups based on the nearest mean until convergence is reached (Fraley and

Raftery 1998, MacQueen 1966). Groups are clustered into "classes" based on the

proximity of the relationship between points (X and Y coordinates of each beverage) on

the MDS plot. In K-means analysis, the term "classes" is equivalent to the term

"groups". The number of groups tells the analysis program how many clusters to

separate the products into. Data were analyzed using XLStat 2008.4.02 (Addinsoft).

Part 2-Fixed Sort

The second part of the two-step sensory sorting method was a fixed sorting task of

the same fifty functional beverages using the seven defined categories generated from the

combined results from the visual and visual-oral free sorting tasks (Part 1). A subset of

eight of the initial panelists (2 males, 6 females; 18 to 50 years old) participated in both

visual and visual-oral fixed sorts. Five beverages (Elements Energy®(9), Powerade ̂ M

Advance (32), Sobe® Lean Energy Diet Citrus (41), Yoplait® Nouriche® Smoothie (50))

had become unavailable on the market between conducting the free and fixed sorts, and

thus, could not be included in the fixed sort. Panelists were given a set of worksheets

containing the list of the fixed functional beverage categories emd definitions, and sticker

labels with the functional beverage product names. The only restrictions were that all

beverages had to be placed into a category and could not be placed into more than one

category. Data from the fixed sorts were analyzed using the same methods as previously

described in the free sort (Part 1).

41

3.4 Results and Discussion

Ingredient Inventory Categorization

The two-dimensional MDS plot (Figure 3.1) resulted in one large cluster

containing forty-five functional beverages. The large cluster could be attributed to the

fact that most beverages contain the same base ingredients, and these similarities resulted

in beverages being grouped together on the MDS plot. Five beverages (Boost® (3),

Glucerna®(20), Pediasure®(30), Slim-fast® (36), and Yoplait® Nouriche Smoothie (50)),

however, were not plotted near the large cluster of beverages. The segregation of these

five beverages on the plot could be the result of the large number of additional vitamins

and minerals these products contained. These beverages are often consumed as

nutritional supplements and have five times as many ingredients as some of the other

functional beverages evaluated in this study.

An acceptable stress value for a MDS plot is <0.10, which correlates to good

correspondence between the actual data and the representation of data relationships on

the MDS plot (Krzanowski and Marriott 1995). The ingredient inventory comparison

MDS plot had a stress value of 0.23 and this was due to the large number of beverages

being compared.

Statistical analysis was also conducted on the ingredient commonalities of the

large cluster of forty-five functional beverages, in an attempt to separate out beverage

categories. The MDS plot, however, still resulted in a large clustering of beverages

instead of separate functional beverage clusters. Since water is a base ingredient found in

all the functional beverages, it was removed from the ingredient inventory, and another

analysis was conducted. This also resulted in an MDS plot that did not separate the

functional beverages into groups.

There were distinct ingredient commonalities among beverages, but the

relationship could not be separated out through statistical analysis of a co-occurrence

matrix, followed by a two-dimensional MDS plot. Although categories were not

apparent in the large scale MDS plot, ingredient commonalities of beverages were

observed in the ingredient inventory spreadsheet. Beverage categories were generated

based on common ingredients that were apparent on the ingredient inventory spreadsheet..

Visual examination of the ingredient inventory for all fifty functional beverages resulted

in the formation of seven functional beverage categories and a miscellaneous group

(Table 3.2). Category names were generated based on common descriptors found on the

beverage product labels.

The ingredient inventory method may be a useful categorization method, but more

research will have to be conducted to determine a way to generate categories statistically

or through objective analysis. It is possible that the ingredient inventory categorization

could categorize the beverages by comparing only select types of ingredients (i.e.

stimulants, vitamins, minerals) contained in functional beverages, instead of the complete

list of ingredients. This could result in the separation of beverages into categories based

on the differing functional ingredients. •

Flow Behavior Comparison Categorization

Three beverage categories were generated through the agglomerative hierarchical

clustering (AHC) of the viscosity measurements of all fifty functional beverages. These

categories were defined by the centroid viscosity value of the beverage viscosities: low

(2.21 mPa -s), medium (37.36 mPa -s), and high (74.13 mPa • s). The low viscosity

category consisted of forty beverages that were mostly still, clear beverages including

isotonics and tea-based beverages (Figure 3.2). The medium and high viscosity

43

categories contained the viscous beverages, such as yogurt-based or fruit-based

beverages.

Flow behavior of the beverages were measured and classified as being Newtonian

(shear stress/strain curve is linear) or non-Newtonian (shear stress/strain curve is non­

linear). Data were also analyzed and categories were generated by two separate

agglomerative hierarchical cluster (AHC) analyses: 1) Newtonian products (Table 3.3

and Figure 3.3) and 2) Non-Newtonian products (Table 3.4 and Figure 3.4). Eight

categories were generated through the combination of both AHC analyses, although

there were no obvious similarities that could be used to characterize the generated

beverage clusters. The AHC of the Newtonian beverages show that most of the

decarbonated energy drinks were clustered together along with a few other beverages,

while diet and low calorie beverages were clustered together.

It is possible that the apparent viscosity at a shear rate of 50 s"1 was not the best

representative shear rate to categorize the non-Newtonian pseudoplastic functional

beverages. Pseudoplastic fluids have varying viscosities at different shear rates;

therefore, calculating the apparent viscosity at a lower set shear rate may result in a more

accurate representation of the apparent viscosity of the non-Newtonian functional

beverages than at a shear rate of 50 s'1.

The combination of other physical property measurements, such as pH level, Brix,

and titratable acidity, may provide additional criteria for the beverages to be categorized

in a more meaningful way. Diet and full calorie cola and lemon-lime flavored

commercial carbonated beverages were separately clustered through an AHC based on

44

these physical properties (Kappes 2007), which provided a more meaningful

interpretation of the clusters.

Two-Step Sensory Sorting Categorization

The visual free sort generated six beverage categories (Figure 3.5), while the

visual-oral free sorting method generated seven beverage categories (Figure 3.6). Six of

the beverage groups were the same and included: Enhanced Waters, Energy Drinks, Fruit

Smoothies, Nutritional Drinks, Sports Drinks, and Teas. The visual-oral sort included an

additional category termed, "Yogurt Smoothies". The resulting categories and definitions

remained consistent between the two sorting procedures, which suggest that visual and

visual-oral sorts could be combined into one task to reduce sorting time.

Panelists commented that it was difficult to place beverages encompassing

multiple concepts into a single category. Beverages such as Fuze® Slenderize (14),

Sobe® Tsunami (40), and Sobe® Lean Energy Diet Citrus (41) were a few of the hybrid-

type beverages that panelists had difficulty sorting. The results from the MDS plot

mirror the panelists' uncertainty because these "hybrid-type" beverages were not

consistently placed in the same category by the panelists, thus these beverages were not

grouped on the MDS plot. From this data, it can be concluded that there are functional

beverages on the market that lack a clear concept and are difficult for consumers to

define and characterize.

The fixed sort MDS plots (Part 2), resulted in fewer ungrouped beverages

(Figures 3.7 and 3.8) than the free sort MDS plots (Part 1). These results were expected

since the categories were fixed and panelists were forced to sort each beverage into a pre­

defined category despite, perhaps, a non-ideal fit. The fixed sort, however, helped to

45

categorize some beverages such as Capri Sun Sport (4), Sobe Power (39), Sobe

Tsunami (40), and Sobe® Lean Energy Diet Citrus (41) (Figure 3.3) possibly because the

characteristics of these beverages matched the definitions of the fixed functional

beverage categories (Figures 3.7 and 3.8). Capri Sun® Sport IM (4) was placed in the

Sports Drinks category, while Sobe® Power (39), Sobe® Tsunami (40), and Sobe® Lean

Energy Diet Citrus (41) were placed in the Energy Drinks category. The three previously

mentioned beverages did not completely match the characteristics defining the categories,

but the results suggest that the panelists thought that these functional beverages best

belonged in those categories. The results from the fixed sort also suggest that panelists

were still uncertain about which categories to sort the hybrid-type beverages. Panelists

also commented that they had difficulty placing beverages incorporating multiple

concepts into a single category. Beverages such as Fuze® Refresh (13), Fuze® Slenderize

(14), Propel®'Propel® Fitness Water (33),and Fitness Water (34), were a few of the

hybrid-type beverages that panelists had difficulty sorting. Some of these beverages may

not have been categorized because the key characteristics of fixed functional beverage

categories may have limited inclusion into a categoiy.

The two-step sensory sorting method aids in developing categories based on

similarities among products. The fixed sort confirms the initial beverage categories

created during the free sort, through the general comprehension of the categories by the

panelists. Overall, the two-step sensory sorting method provides insight on the similarity

and dissimilarity relationships between products and aids in category development based

on the grouping of beverages with similar characteristics.

Comparison of Categorization Methods

Generated beverage categories were not consistent across the three methods. The

methods each resulted in the clustering of beverages; however the two-step sensory

sorting method generated distinct categories with distinguishing characteristics that are

meaningful compared to the ingredient inventory and flow behavior comparison

categorization methods. Categorization using the ingredient inventory and flow behavior

comparison methods was based on only one aspect of the beverages, while the two-step

sensory sorting method allowed panelists to categorize beverages using multiple senses

and a variety of information. Therefore, the two-step sensory sorting method was the

only method which included consumer insight in the categorization and definitions of

beverage categories.

Although the ingredient inventory and flow behavior comparison categorizations

were not as successful in categorizing functional beverages as the two-step sensory

sorting method, the information obtained about each beverage could possibly be used to

aid in describing the categories generated by the two-step sensory sorting categorization.

Comparing the ingredient and flow behavior data of individual beverages grouped

together into a category through the two-step sensory sort may result in additional

common characteristics distinctive of a category. For example, the beverages categorized

in the Sports Drinks category all contained multiple sugars and had Newtonian

viscosities; which could be the major chemical and physical characteristics of the Sports

Drinks category. This could possibly result in further defined functional beverage

categories.

47

One limitation of this study's design, regarding all three categorization methods,

is that too few beverages were selected from each of the potential categories. For

example, there was only one low-calorie carbonated energy drink and only two fruit-

based smoothies. If there had been more of those beverage types included in this study, it

is possible that other categories would have been generated. The beverages selected for

this categorization method, however, represented the assortment and quantities of

functional beverages available in the market reflecting the current market trend.

After comparing the results of all three methods, in general there are some basic

major categories that were apparent regardless of the categorization method applied:

Energy Drinks,, Nutritional Drinks,, Sports Drinks, Teas, and Yogurt Smoothies. There

are still some beverages which were difficult to categorize such as Fuze® "Refresh" and

Fuze® "Slenderize". Reasons for the difficulty could be attributed to consumers'

uncertainty or unfamiliarity of the key characteristics of these beverages. In addition, as

more hybrid-type products are introduced into the functional beverage market, a new

beverage category may be generated.

3.5 Conclusions

Strategically marketing a product to meet consumer expectations requires

knowledge of a product's categorical membership. The inclusion of a beverage into a

category provides a quick snapshot of the expected attributes of a particular beverage. It

is also important to determine and define functional beverage categories to aid in the

regulation of the beverages. Regulations would protect and inform consumers about

ingredient compositions specific to functional beverage categories. Therefore, it is

necessary to determine and define categories for the expanding functional beverage

market, which currently lacks distinct categories.

Categorizing functional beverages by ingredient inventory did not result in

categories, possibly due to the expansive list of ingredients compared. The flow behavior

comparison also did not result in distinct functional beverage categories with

distinguishing characteristics. This suggests that flow behavior and measured viscosity at

a shear rate of 50 s"1 may not be the best means of categorizing functional beverages.

The two-step sensory sorting method has potential as a method to categorize functional

beverages because the incorporation of sensory evaluation aids in generating distinct

functional beverage categories. Future research includes investigating the validity and

reproducibility of the two-step sorting method as a rapid method to categorize large

groups of products. Additional research may include an investigation of a free sort

focusing only on an oral sensory evaluation sort of the blinded products, to determine if

the functional beverages could be categorized by only oral sensations and tastes without

the influence of packaging or product identification. The level of sweetness, mouthfeel,

or other oral sensations may play a significant role in generating different functional

beverage categories.

3.6 References

Amendola C, lannilli I, Restuccia D, Santini I, Vinci G. 2004. Multivariate statistical analysis comparing sport and energy drinks. Innovative Food Science & Emerging Technologies 5(2):263-7.

Cook DJ, Hollowood TA, Linforth RST, Taylor A J. 2003. Oral shear stress predicts flavour perception in viscous solutions. Chem Senses 28(1):11-23.

Fraley C, Raftery AE. 1998. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal 41(8):578-88.

Hollowood TA, Linforth RST, Taylor AJ. 2002. The Effect of Viscosity on the Perception of Flavour. Chem Senses 27583-91.

Humphries G. 2007. Nutraceutical Soft Drinks: Innovation in sports, energy, dairy, and functional beverages.

Kappes SK, Schmidt SJ, Lee SY. 2006. Mouthfeel Detection Threshold and Instrumental Viscosity of Sucrose and High Fructose Corn Syrup Solutions. J Food Sci 71(9):S597-602.

Kappes SM, Schmidt SJ, Lee SY. 2007. Relationship between Physical Properties and Sensory Attributes of Carbonated Beverages. J Food Sci 72(1):S001-11.

Kruskal JB. 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29(2): 115-29.

Krzanowski W.T, Marriott FHC. 1995. Multivariate analysis. New York: Halsted Press. 280 p.

Lai GG. 2007. Getting Specific with Functional Beverages. Food Technology [serial online]. 61(12):Available from Posted 2007.

Lawless HT, Glatter S. 1990. Consistency of multidimensional scaling models derived from odor sorting. J.Sensory Studies 5(2):217-30.

Lawless HT, Sheng N, Knoops SSCP. 1995. Multidimensional scaling of sorting data applied to cheese perception. Food Qual Pref 6(2):91-8.

Lim J, Lawless HT. 2005. Qualitative differences of divalent salts: multidimensional scaling and cluster analysis. Chem Senses 30(9):719-26.

Lord JB. 2000. New Product Failure and Success. In: A. L. Brody, J. B. Lord, editors. Developing New Food Products for a Changing Marketplace. Lancaster: Technomic Publishing Co, Inc. p55-86.

MacQueen JB. 1966. Some Methods for Classification and Analysis of Multivariate Observations. Proc. Fifth Berkeley Symp. on Math. Statist, and Prob.281-297.

Mintel. 2008. Functional Beverages-US August 2008. Mintel Reports.

Orth UR, de Marchi R. 2005. Advertising's influence on product experience and purchase intention. Fruit Processing 6:372-76.

Packaged Facts. 2009. Functional Foods and Beverages in the U.S. 1-210.

Rugg G, McGeorge P. 1997. The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Syst 14(2):80-93.

50

Schmidl MK, Labuza TP. 2000. Essentials of Functional Foods. Gaithersburg: Aspen Pub. 395 p.

Tang C, Heymann H. 2002. Multidimensional Sorting, Similarity Scaling and Free-Choice Profiling of Grape Jellies. J Sens Stud 17(6):493-509.

Urala N, Lahteenmaki L. 2003. Reasons behind consumers functional food choices. Nutr Food Sci 33(4): 148-58.

Viswanathan M, Childers TL. 1999. Understanding How Product Attributes Influence Product Categorization: Development and Validation of Fuzzy Set-Based Measures of Gradedness in Product Categories. J Market Res 36(l):75-94.

Ward JH. 1963. Hierarchical grouping to optimize a quantitative function. J Am Stat Assoc 58(301):236-44.

Wright R. 2008. Nutraceuticals Coast in the Beverage Market. Nutraceuticals World [serial online]. Available from Posted July 2008.

Yamauchi T, Markman AB. 2000. Inference using categories. Journal of Experimental Psychology Learning Memory and Cognition 26(3):776-95.

51

3.7 Tables and Figures

Tabic 3.1: Fifty commercially-available functional beverages and corresponding numerical codes.

No. Code

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Commercially-Available Functional Beverages

Arizona® Pomegranate Green Tea

Bolthouse® Farms Fruit Smoothie

Boost®

Capri Sun® Sport ™

Dannon™ - Danimals®

Dannon™ - Light 'n Fit® Smoothie

Dannon™ - Frusion®

Dasani® Flavored Water

Elements Energy®

Ensure® Shake

Fruit20®

Full Throttle®

Fuze® "Refresh"

Fuze® "Slenderize"

Fuze® Green Tea

Gatorade® Endurance

Gatorade® Lemonade

Gatorade® Original

Gatorade® Rain

Glucerna®

Gold Peak™ Iced Tea

Honest Tea®

Lifeway® Lowfat Kefir

Liplon® Original White Tea

MDX

No. Code

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

Commercially-Available Functional Beverages

Metromint®

Minute Maid® Fruit Falls™

Naked® Fruit Smoothie

NOS®

Pediasure®

Powerade™

PoweraderM - Advance

Powerade™ Option

Propel® Fitness Water

Rockstar®

Slimfast Optima®

Snapple® White Tea

Sobe® - NoFear

Sobe® - Power

Sobe® - Tsunami

Sobe® Lean Energy Diet Citrus

Sobe Life Water®

Stonyfield Farm® Organic Smoothie

Sweet Leaf™ Tea

TAB® Energy

Tazo® Iced Tea

Trinity® Water

Whitney's® Yo on the Go™

Yoplait® Go-GURT® Smoothie

Yoplait® Nouriche® SiiperSmoothie

52

Tabic 3.2: Functional beverage category names generated through visual observation of the beverage ingredient commonalities in the ingredient inventory spreadsheet.

Category Names Carbonated Energy Drinks

Energy Drinks

Nutritional Drinks

Sports Drinks

Teas

Waters

Yogurt Smoothies

Miscellaneous Drinks '

Beverages per

Category 6

4

5

8

8

7

8

4

Ingredient Commonalities B vitamins Carbonated Water Natural Extracts (Ginseng) Stimulants (Caffeine, Taurine, or D-ribose) MFCS Natural Extracts Stimulants (Caffeine, Taurine, or D-ribosc) Gums Protein Vitamins and Minerals Electrolytes (Na, K) Natural Sugars Natural Extracts Tea or water infused with tea

B Vitamins Natural Flavors Modified Starches Yogurt None

53

Tabic 3.3: Viscosities of Newtonian Functional Beverages measured at 20 C.

Code

11

27

8

45

26

33

34

42

41

16

47

17

14

18

19

32

37

15

4

44

21

31

24

9 46

35 25

1 12 29 38

39 13

Commercially-Available Beverages

Fruit20®

Minute Maid® Fruit FallsTM

Dasani® Flavored Water

TAB® Energy

Metromint®

PoweradeTM Option

Propel® Fitness Water

Sobe Life Water®

Sobe® Lean Energy Diet Citrus

Gatorade® Endurance

Trinity® Water

Gatorade® Lemonade

Fuze® "Slenderize"

Gatorade® Original

Gatorade® Rain

PoweradeTM - Advance

Snapple® White Tea

Fuze® Green Tea

Capri Sun® Sport TM

Sweet LeafTM Tea

Gold PeakTM Iced Tea

PoweradeTM

Lipton® Original White Tea

Elements Energy® Tazo® Iced Tea Rockstar® MDX

Arizona® Pomegranate Green Tea Full Throttle® NOS® Sobe® - NoFear Sobe® - Power

Fuze® "Refresh"

Viscosity (mPa-s)

0.983 ±0.006

0.983 ±0.040

1.027 + 0.015

1.033 ±0.032

1.037±0.012

1.050 + 0.056

1.100±0.100

1.190±0.017

1.200 ±0.026

1.203 + 0.021

1.203 ±0.021

1.210±0.036

1.227 ±0.049

1.233 ±0.038

1.243 + 0.012

1.250 ±0.010

1.257 ±0.031

1.257 ±0.029

1.267 ±0.031

1.357 ±0.070

1.363 ±0.055

1.363 ±0.045

1.367 ±0.078

1.417±0.031 1.423 ±0.112 1.490 ±0.030 1.533 ±0.040 1.587±0.035 1.587±0.055 1.640 ±0.030 1.933 ±0.086 1.973 ±0.015

2.430 ±0.079 R2 values ranged from 0.989 to 1.000 witli an average and standard deviation of 0.999±0,002

Table 3.4: Viscosities of non-Newtonian Functional Beverages at a shear rate of 50 s .

Code

22

40

30

3

20

10

36

2

43

5

7

28

6

48

49

23

50

Commercially-Available Functional Beverages

Honest Tea®

Sobe® - Tsunami

Pediasure®

Boost®

Glucerna®

Ensure® Shake

Slimfast Optima®

Bolthouse® Farms Fruit Smoothie

Stonyfield Farm® Organic Smoothie

DannonTM - Danimals®

DannonTM - Frusion®

Naked® Fruit Smoothie

DannonTM - Light 'n Fit® Smoothie

Whitney's® Yo on the GoTM

Yoplait® Go-GURT® Smoothie

Lifeway® Lovvfat Kefir

Yoplait® Nouriche® SiiperSmoothie

Viscosity (mPa*s)

1.287 ±0.01

3.340 + 0.06

4.757 + 0.24

9.810±0.12

11.70± 0.77

15.75 ±0.15

32.81 ±2,60

42.03 ± 1.62

47.13 ±0.81

49.12 ±5.61

50.77 ± 1.01

52.45 ± 1.50

54.40 ±7.10

57.93 ±3.78

77.84 ± 2.27

100.6 ±5.0

121.4 ±28.3

55

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• based on the largest relative increase in dissimilarity.

- 4

Figure 3.3: Agglomerative hierarchical clustering of 33 Newtonian functional beverages by viscosity measurement using the ARES RFS III on the dissimilarity scale by Euclidean distance and agglomeration by Ward's Method. The dotted line was computed using the software and truncates groups based on'the largest relative increase in dissimilarity.

58

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Characteristics

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Contains minimal Calories, lightly flavored, clear liquid Contains many nutrients could serve' as a meal-replacement beverage

Contains fermented dairy products; opaque ' Mineral or vitamin enhanced; does not contain caffeine. Labeling targets athletes Contains Tea or Tea Extracts

Figure 3.5: Multidimensional Scaling of a visual free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.265 and functional beverage categories generated through the free visual sorting method. Functional beverages .. were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 3.1 for product names and corresponding number codes.. -

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Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve • as a meal-replacement beverage

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Figure 3.6:.Multidimensional Scaling of a visual-oral free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.290 and functional beverage categories generated through the free visual-oral sorting method. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 3.1 for. product names and corresponding number codes.

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Figure 3.7: Multidimensional Scaling of a visual fixed sort (Part 2) of 50 functional beverages plotted in two dimensions with stress = 0.289 and corresponding functional beverage categories. .Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 3.1 for product names and corresponding number codes.

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Figure 3.8: Multidimensional Scaling of a visual-oral fixed sort (Part 2) of 45 functional beverages plotted in two dimensions with stress = 0.283 and corresponding functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 3.1 for product names and corresponding number ;

codes.

CHAPTER 4 - VALIDATION AND REPRODUCIBILITY STUDY OF A TWO-STEP SENSORY SORTING METHOD TO CATEGORIZE FUNCTIONAL BEVERAGES

4.1 Abstract

Sorting and categorizing are quick and useful methods, which aid in identifying

product traits and highlighting important attributes. The aim of this study was to

determine the validity and reproducibility of a two-step sensory sorting method used to

categorize functional beverages based on qualitative judgments.

A validation study involved two groups of naive panelists sorting forty-six

functional beverages into the fixed categories pre-generated from the initial two-step

sensory sorting method. This was done to confirm that the fixed categories were

understandable and that the beverages could be similarly sorted. To determine if the two-

step sorting task was reproducible, the method was replicated with another group of nai've

panelists.

Adjusted Rand Index (ARI) values greater than 0,90 showed that there was

excellent correspondence between the fixed sorts conducted in the validation study.

Beverages that were difficult to sort in the initial two-step sensory sorting task, however,

were still not consistently categorized by the other two panels. Six functional beverage

categories were generated in the reproducibility study, with the major difference between

the initial sort and the replicated sort being that the Yogurt Smoothie and Fruit Smoothie

categories were combined into one category encompassing both types of beverages in the

replicate. The high ARI values from the validation study (ARI >0.88) and the similar

functional beverage categories generated through the reproducibility study (ARI >0.77)

64

suggest that the two-step sensory sorting method can be used to consistently create

similar functional beverage categories.

Keywords: sensory sorting, categorization, functional beverages, sensory evaluation

4.2 Introduction

Functional Beverage Categories

Consumers' desire for beverages that provide health benefits has led to the

development of many new functional beverages each year. In 2007, functional beverage

sales were over $10 billion (Wright 2008), and in this constantly expanding functional

beverage market, the introduction of these beverages has outpaced the" development of

categories and definitions of beverages in this segment. To have defined categories is

important for proper marketing of products (Murphy and Ross 1994). Categories help to

direct consumers with their purchase intent or acceptance based on the assumed features

of that particular category (Moreau and others 2001). For ultimate consumer Scitisfaction,

consumer expectations for the specific category need to be met. Therefore, it is necessary

to understand the underlying characteristics which are unique in defining functional

beverage categories.

Sorting and Characterization Methods

Sorting methods can be used as a quick and simple means to gather information

about products and are much less time-consuming compared to other methods of sensory

evaluation, such as descriptive analysis. Sorting methods allow researchers to relatively

quickly gather information about panelists' perceptions of a large group of products

65

(Rugg and McGeorge 1997, Viswanathan and Childers 1999), require minimal panelist

training, and can often be conducted in one session.

Prior to their applications to food, sorting methods have been applied to nonfood

materials, such as car fabrics (Giboreau and others 2007), colored plastic chips (Faye and

others 2004), and oral health care products (Bertino and Lawless 1993). Sorting methods

were first introduced to food sensory research when Lawless and others (1995) conducted

a study on the perception and conceptual mapping of the relationship of different cheeses.

Following this research, other food items have been sorted such as water (Falahee and

MacRae 1997), snack bars (King and others 1998), red wine (Gawel and others 2000),

novel food products (Woolf and others 2002), and yogurts (Saint-Eve and others 2004) to

obtain information on product relationships based on attributes.

The free sorting method has few restrictions which include that more than one

category must be created and that all objects must be sorted into mutually-exclusive

categories (Lim and Lawless 2005). Another sorting method is the projective mapping,

which involves clustering products on a blank sheet of paper, which is later divided and

marked into uniform squares to determine the distance of the products from one another

(Perrin and others 2008). This method has been used to sort wines (Pages 2005) and

orange juice (Neslrud and Lawless 2008). Flash profiling, in which panelists observe and

evaluate all the products at once and create their own descriptive terms to describe

products and cluster similar products together, is another method used in sensory sorting

and descriptive work (Delarue and Sieffermann 2004, Tarea and others 2007). All

sorting methods result in providing information on the relationship among products and

descriptors of products. Previous research has only encompassed the sorting of less than

66

30 products (Faye and others 2004). In this research, a large group of products (45 to 50)

was used to determine if they can be sorted in a relatively short amount of time. Thus,

the purpose of the research was to assess the validity and reproducibility of a two-step

sensory sorting method used to categorize functional beverages based on qualitative

judgments.

4.3 Materials and Methods

Products

Fifty ready-to-drink functional beverages were purchased from three local

supermarkets (County Market, Meijer, and Schnucks) in Champaign-Urbana, Illinois in

September 2006. Brand-name products were purchased, while generic or store brand

products were not included in the study, to minimize any bias associated with the lack of

marketing of these products. If multiple flavors of products were available, the original

flavor or a derivative of a berry flavor was purchased to minimize dissimilarities due to

flavorings or colors.

Products were stored in a refrigerator at ~5°C and taken out 5 minutes prior to the

sorting tasks. In addition to their standard packaging, all products were labeled with their

name and respective number code (Table 4.1). Supplies available for panelists to use

during the visual-oral sorting task included: cold water (Absopure, Plymouth, MI) to

rinse between samples, 29.6 mL plastic cups (Solo Cup Company, Urbana, IL) to

dispense the beverages, and spit cups to expectorate samples.

67

Panels

Four panels (Figure 4.1) participated in the validity and reproducibility tests of the

two-step sensory sorting method. Each panel consisted of 10 to 14 untrained panelists.

Panel 1 was the initial panel that participated in visual and visual-oral free and fixed

sorting tasks. The combination of both the free and fixed sorting tasks resulted in the

generation of functional beverage categories. Panels 2 and 3 tested for validity of Panel

l's functional beverage categories by sorting the same set of fifty functional beverages

into the fixed categories pre-generated from the initial two-step sensory sorting method.

Panel 4 participated in a reproducibility study in which the same two-step sensory sorting

method (visual and visual-oral free sorting and'fixed sorting tasks) as Panel 1 was

repealed.

Two-Step Sensory Sorting Method

Part 1-Free Sort

The free sort consisted of categorizing fifty functional beverages by both a visual

and visual-oral sorting task. All beverages were placed around a table and panelists were

not restricted to the order they evaluated the beverages. Panel 1, a fourteen-member

untrained panel (3 males, 11 females, 18 to 50 years old), was instructed to group similar

functional beverages into self-defined, mutually-exclusive categories and describe each

category with common, key characteristics, A set of blank worksheets and a sheet of

sticker labels with functional beverage product names were given to each panelist.

Panelists independently completed the sorting tasks and created category names and key

descriptors for each generated category and wrote them down on the worksheets. The

use of the sticker labels regulated the sorting process by eliminating the possibility of

68

panelists placing a beverage into multiple categories and to make sure that all beverages

were placed into at least one category. Panelists were not restricted in sorting time, and

the average time to complete both free sorting tasks was approximately one hour.

In the visual free sort, panelists focused on grouping beverages based on common

visual characteristics. These visually observable characteristics could include any visual

cues, such as packaging information, perceived opaqueness, color, or thickness of the

beverage. In addition, previous exposure to the product, such as advertising, was allowed

to influence the panelists during the visual sort. The only restriction in the visual free

sort was that at least two beverage categories had to be created. After the visual sort was

completed the same sorting process was applied to the visual-oral evaluation of the same

products. This sort was labeled a visual-oral sort because panelists were able to view and

taste the functional beverages during the sorting process. Panelists were instructed to sort

the beverages primarily focusing on their oral evaluations. Again panelists were

provided with a set of blank worksheets and a sheet of sticker labels to aid in the sorting

process.

Free Sort Category Generation

The sorting data were analyzed to determine the overall functional beverage

categories generated through the free sort. The qualitative data generated from the free

sort were transformed into quantitative data through a series of statistical analyses. First,

a similarity co-occurrence matrix (products x products) was created using XLStat

2008.4.02 (Addinsoft) to compare the frequency in which panelists placed functional

beverages in the same category. The more often beverages were paired together, the

greater the corresponding value comparing the relationship between two products, The

69

greatest value on the co-occurrence matrix between two products was 14 because there

were fourteen panelists participating in the initial sort (Panel 1).

The co-occurrence matrix was followed by a two-dimensional multidimensional

scaling (MDS) plot with Kruskal's stress-1 (Kruskal 1964) using XLStat 2008.4.02

(Addinsoft). Multidimensional scaling (MDS) is a mathematical technique which is used

to display the similarities and dissimilarities between objects on a two-dimensional plot.

For the purpose of rotational consistency on the MDS axes, the relationship distance

between the plot of two beverages (Gluccrna® (20) and Gold Peak™ Iced Tea (21)) was

selected as the initial configuration. These two beverages were very different from each

other in both ingredient formulation and panelist sorting results and were never paired

together in a category by any panelists. The relationship between the two products was

chosen because they were centrally located in two separate, defined clusters of beverages

on the MDS plot. This allowed for a clearer visual comparison between the clusters on

the MDS plots. Choosing the relationship between the two beverages as the initial

configuration allowed the statistical program to rotate the clusters around the axes based

on the relationship between the two beverages.

The MDS plots were plotted in absolute configuration, which calculates the

relationship between objects as closely as possible to the observed distances in the initial

co-occurrence matrix. The quality of fit of the data on the MDS plot was measured by

the stress value, which explained the true recovery of the data on the two-dimensional

plot. The stress value ranges from 0 to 1, with 0 corresponding to no stress and a stress

level less than 0.10 considered an acceptable stress value (Krzanowski and Marriott

1995). The MDS plot program used to analyze the data ran multiple iterations of the data

to create a MDS plot containing the maximum amount of data displayed in two

dimensions with the lowest stress value.

The MDS plots displayed functional beverages clustered together based on the

number of times each beverage was paired with another beverage. The more often two

beverages were grouped together by panelists, the closer the two beverages were plotted

on the MDS plot. The number of categories was selected by visually observing the MDS

plots and grouping beverages that were in close proximity to each other. K-means

clustering (MacQueen 1966) was conducted on beverage coordinate points on the MDS

plot to determine which beverages belonged together in each category. K-means

clustering groups neighboring objects into' tentative groups based on the nearest cluster's

mean value (MacQueen 1966, Fraley and Raftery 1998, Fraley and Raftery 1998).

Groups were clustered into "classes" based on the proximity of the relationship between

points on the MDS plot. In K-means clustering analysis, the term "classes" has the same

definition as the term "groups" of objects. The number of classes tells the analysis

program how many groups to separate the products into. Eight classes were chosen

based on visual observation of the free sort results. Data were analyzed using XLStat

2008.4.02 (Addinsoft) and the K-means clustering groups were created by analyzing the

beverages (X and Y coordinates) on the MDS plot.

Category names and definitions were determined based on the descriptors and

category names most commonly generated by the fourteen panelists. If more than half of

the panelists used similar terminology, the descriptors were included in the category

names and descriptors. Examples of similar terminology include, "healthy drinks" and

"healthy living" or "pre/post workout" and "sports performance."

71

Comparison of Free Sort Generated Categories

An Adjusted Rand Index (ARI) (Hubert and Arabie 1985) was conducted to

determine the similarity between the results of the K-means generated clusters from both

the visual and visual-oral free sorts. The original Rand Index (RI) (Rand 1971) examined

the similarities between the agreements and disagreements obtained through the

comparison of the results from the two sorts. The RI can compare the generated

categories from two different sorting methods or generated category sorting results to

established categories. An ARI value ranges from 0 to 1 and explains the correspondence

between the categories of the two compared sorts, with a value of 1 signifying that the

two sorts were exactly the same. The ARI differs from the original RI in that it adjusts

for chance agreement between the two sorts. In Steinley's research (2004), the same

clustering method was compared and the RI was a higher value than the ARI. Therefore,

the ARI is a more sensitive scale than the RI for determining the degree of

correspondence between the two sorts. The validity of the quality of the cluster recovery

is determined by the ARI value. An ARI value of greater than 0.86 is in the 95lh

percentile, 0.77 is in the 9011' percentile, 0.67 is in the 85th percentile, and 0.60 is in the

80th percentile (Steinley 2004). An ARI value less than 0.65 is considered poor recovery

of data, a value greater than 0.65 is considered moderate recovery, a value greater than

0.80 is considered good recovery, and a value greater than 0.90 is considered excellent •

recovery (Steinley 2004).

Part 2-Fixed Sort

The second part of the two-step sensory sorting method included a fixed sorting

task of the same fifty functional beverages using the seven defined categories generated

72

from the combined results of the visual and visual-oral free sorting tasks. A subset

composed of eight panelists (2 males, 6 females, 18 to 50 years old) who were available

from Panel 1 participated in both visual and visual-oral fixed sorts. Four beverages

(Elements Energy®(9), Powerade™ Advance (32), Sobe® Lean Energy Diet Citrus (41),

and Trinity® Water (47)) had become unavailable on the market between conducting the

free and fixed sorting tasks and thus were not included in the visual-oral fixed sort.

Panelists were forced to sort the forty-six available beverages into the seven defined

categories generated from the combined results of the visual and visual-oral free sorting

tasks. Panelists were given a set of worksheets containing the list of the fixed functional

beverage categories and definitions, and sticker labels with the functional beverage

product names, Data from the fixed sorting tasks were analyzed using the same methods

as previously described in the free sort. Eight classes (seven categories plus an extra

class) were selected as the K-means value. The extra class was added to account for the

beverages that were not observed to be in a cluster on the MDS plot. The fixed sorting

task data were compared to the functional beverage categories generated through the free

sorting task, by calculating the Adjusted Rand Index (ARI).

Validation Study

A validation study was conducted on the categories generated through the two-

step sensory sorting method. The forty-six functional beverages were sorted into seven

previously generated functional beverage categories by two panels of naive panelists

(Panel 2 and Panel 3). The seven previously-generated functional beverage categories

were based on the combined results of both the visual and visual-oral free sorting tasks.

The panelists were not exposed to the free sorting task of the initial panel (Panel 1) and

were forced to sort beverages into the fixed categories generated by Panel 1. Panel 2

73

consisted often untrained panelists (5 males, 5 females, 18 to 50 years old) and Panel 3

consisted of thirteen untrained panelists (4 males, 9 females, 18 to 50 years old). The

panels followed the same instructions and guidelines as the fixed sort from the two-step

sensory sorting method of Panel 1. The same four functional beverages were not

(available for evaluation by Panel 2, while Sobe® Lean Energy Diet Citrus (41) became

available again, but Yoplait® Nouriche® Smoothie (50) became unavailable in the market

for evaluation by Panel 3.

The fixed sort data from the validation study (Panels 2 and 3) were analyzed using

the same methods previously described in the free sort. An ARI was conducted to

compare Panel 2 and Panel 3's sorting data to the functional beverage categories

generated through the initial two-step sensory sorting method. Since four of the

beverages were not available during the validation fixed sorts, only functional beverages

present in both sorts were used in the analyses. Therefore, forty-six beverages were

compared in this validation study.

Reproducibility Study

Panel 4 consisted of thirteen untrained panelists (4 males, 9 females, 18 to 50

years old), who replicated the two-step sensory sort (free sorting task followed by fixed

sorting task) on forty-six functional beverages. Four beverages (Elements Energy® (9),

Powerade™ Advance (32), Sobe® Lean Energy Diet Citrus (41), and Trinity® Water

(47)) had become unavailable on the market between conducting the Panel 1 's free and

fixed sorts. Sobe® Lean Energy Diet Citrus (41) had become available while Yoplait®

Nouriche®SuperSmoothie (50) had become unavailable between conducting the initial

two-step sensory sort and the reproducibility study. In total, there were five beverages

that were not sorted during compared fixed sorting tasks. Therefore, forty-five functional

74

beverages were compared in the reproducibility study. The data were analyzed using the

same statistical analyses as the initial two-step sensory sorting method (Panel 1). The

reproducibility of the method was analyzed using the ARI by comparing the categories

generated from Panel 1 and Panel 4's two-step sensory sorts.

4.4 Results and Discussion

Initial Two-Step Sensory Sorting Method (Panel 1)

Panel l 's visual free sort resulted in six functional beverage categories which

included: Energy Drinks, Enhanced Waters, Fruit Smoothies, Nutritional Drinks, Sports

Drinks, and Teas (Figure 4.6). The visual-oral free sort resulted in seven categories, six

that were the same categories and an additional category named, "Yogurt Smoothies"

(Figure 4.7). The ARI between both free sorts (Table 4.2) was 0.80, which is good

recovery between the sorts. The categories and definitions remained consistent between

the two free sorts, which suggest that visual and visual-oral sorts could be combined into

one task to reduce sorting time. The visual-oral sort incorporates both visual and oral

evaluations of the products which provides a complete sensory experience, thus is the

recommended task to choose to reduce the sorting time of the two-step sensory sorting

method.

The stress value of the MDS plot for visual free sort was 0.265 and 0.290 for the

visual-oral free sort, which are above the acceptable stress level of less than 0.10

(Krzanowski and Marriott 1995). The stress values were high and suggest that the

configuration on the dimensions may not be acceptable. However, the larger the number

of objects compared, the greater the stress value (Kruskal and Wish 1978). In previous

research, stress values have been lower because fewer objects were being compared

75

(Lawless and others 1995, Faye and others 2006). Plotting a large number of products

increases the number of comparisons and makes it difficult to display all the product

relationships in only two dimensions. When analyzing data using an MDS plot, it is

expected that all the relationships between objects will appear on one plot. The

relationships between products on a plot that are further apart are much more accurate

than the relationships between products that are plotted closer together on a MDS plot.

To reduce the high stress value, three dimensions could be plotted on a three-dimensional

MDS plot.

Panelists commented that it was difficult to place beverages incorporating

multiple concepts into a single category. Beverages such as Fuze® Slenderize (14),

Sobe® Tsunami (40), and Sobe® Lean Energy Diet Citrus (41) were a few of the hybrid-

type beverages that panelists had difficulty sorting. The results from the MDS plot

mirror the panelists' uncertainty because these hybrid-type beverages were not

consistently placed in the same category, thus they were not clustered into a particular

group of beverages (Figures 4.2 and 4,3). It can be concluded that there are functional

beverages on the market that lack clear concepts and are difficult for consumers to

categorize.

Both the visual and visual-oral fixed sort MDS plots, resulted in fewer ungrouped

beverages (Figures 4.4 and 4.5) compared to the free sorts. These results were expected

since the categories were fixed and panelists were forced to sort each beverage into pre­

defined categories. The results from the fixed sort also suggest that panelists still had

difficulty sorting hybrid-type beverages such as products Fuze® "Refresh" (13), Fuze®

"Slenderize"(14), Powerade™ Option (33), and Propel® Fitness Water (34) into the fixed

categories because of the disagreement in the placement of beverages. Some beverages

may not have been categorized because the defined fixed functional beverage categories

may have been too restrictive, which excluded beverages from categories. The fixed sort,

however, helped to categorize some beverages such as Capri Sun® Sport1 M (4), Sobe®

Power (39), Sobe® Tsunami (40), and Sobe® Lean Energy Diet Citrus (41) (Figure 4.4

and 4.5), possibly because the characteristics of these beverages matched the definitions

of the fixed functional beverage categories.

The two-step sensory sorting method aids in developing categories based on

panelists' judgments of the similarities among products. Categories were generated

through the free sort, while the fixed sort was instrumental in verifying the generated

categories through the similar placement of beverages into pre-generated categories.

Descriptive analysis studies and free sorts often result in similar product descriptor results

(Faye and others 2004), which suggests that the two-step sensory sorting method may be

a faster method to obtain product descriptors.

The two-step sensory sorting method has the potential to be used as a rapid

sorting method to categorize a large quantity of products. The two-step sensory sorting

method consisted of two approximately one-hour sessions, which makes it a relatively

quick method to gather information about product relationships. There was no group

consensus in the generation of categories; therefore, the development of categories relied

heavily on a series of statistical analyses. Overall, the two-step sensory sorting method

provides insight on the similarity and dissimilarity among products and aids in category

development based on the grouping of beverages with similar characteristics.

77

Validation Study

In general, Panels 2 and 3 sorted the functional beverages into the same categories

as Panel 1 (Table 4.5). Panel 1 and 2 had an ARI of 0.91 for the comparison of the fixed

visual sort and an ARI of 0.93 comparing the fixed visual-oral sorts (Table 4.4). Panels 1

and 3 had an ARI of 0.88 for the fixed visual sort and 0.93 for the fixed visual-oral sort

(Table 4.2). Across all panels, fixed visual and visual-oral sorts, Fuze® "Refresh"(13)

and Fuze® "Slenderize"(14) were not sorted into the same categories by the panelists;

therefore, they did not fall into a specific functional beverage category. In the visual sort,

however, these two functional beverages were placed in the "Enhanced Water" category.

A reason for the placement of beverages into different categories could be due to the

varying judgments of the panelists.

Propel® Fitness Water (34) and Powerade™ - Option (33) were not categorized

by Panel 1; however, Propel® Fitness Water (34) was placed in the "Enhanced Water"

category in Panel 2's visual-oral fixed sort and in Panel 3's visual and visual-oral fixed

sort. A reason the beverages may not have been sorted into a category is that they were

clear in color, unlike other sports drink types, which made panelists uncertain about

which category these beverages belonged. Powerade™ - Option (33) was placed in the

"Sports Drinks" category in both Panel 2 and 3's visual-oral fixed sort, but was not sorted

into a specific category by visual sorting tasks. A possible reason for these results may

be attributed to panelists evaluating that Powerade™ - Option (33) was similar in taste

and mouthfeel but dissimilar in visual cues to other sports drink-type beverages.

Overall, the functional beverage categories generated from the two-step sensory

sorting method were understandable to naive panelists who had not participated in

78

creating the categories. In Tang and Heymann's (2002) research, naive subjects have

comparable product positioning as an expert panel. Therefore, untrained panelists have

similar ability as trained panelists in describing and categorizing functional beverages.,

The results suggest that the Teas, Yogurt Smoothies, Fruit Smoothies, and Nutritional

Drinks were understandable, well-defined categories. The functional beverages chosen in

this study were placed in consistent categories for these four categories by all three panels

(Table 4.3).

Categories in which there were a few discrepancies in beverage placement

included Energy Drinks and Enhanced Waters. The validation study results suggest that

the functional beverage categories generated by panelists in the two-step sensory sorting

method can be understood and used by a naive group of panelists who did not participate

in the free sort.

Reproducibility Study

The purpose of the reproducibility study was to determine if the same functional

beverage categories could be reproduced by a different panel. The categories generated

and replicated may aid in the creation of functional beverage categories that are

universally-understood by the general population. Panel 4's two-step sensory sorting

method resulted in six functional beverage categories (Figure 4.10). The categories

generated by Panel 4 were consistent with Panel l 's categories except that a "Smoothies"

group was created and defined to encompass both dairy and fruit-based smoothies and a

"Carbonated Energy Drink" category replaced Panel l's "Energy Drink" category.

Similar functional beverage categories were expected and observed through the

reproducibility study. The ARI values were all above 0.80 (Table 4.2) which means that

79

there was a good recovery of the compared data. The ARI comparing the visual-oral

fixed sorts between Panel 1 and 4 was 0.77. Possible reasons for the lower value could

be primarily attributed to the different number of fixed functional beverage categories,

and that functional beverages (Powerade™ Option (33), Propel® Fitness Water (34),

Sobe® No Fear (38), Sobe® Power (39), and Sobe® Tsunami (40)) were not sorted into a

category by either panel (Table 4.4). The similar functional beverage categories and high

ARI values suggest that the two-step sensory sorting method is reproducible when

attempting to categorize a set of 45 to 50 functional beverages.

In both the visual and visual-oral free sorts, Panel 4 had difficulty categorizing

Fuze® MRefrcsh"(13), Fuze® MSlenderize"(14), Sobe® Power (39), Sobe® Tsunami (40),

and Sobe® Lean Energy Diet Citrus (41), which were the same functional beverages not

sorted into categories by Panel 1 (Figures 4.10 and 4.11). The fixed sort resulted in

Sobe® Power (39) and Sobe® Tsunami (40) not being placed into a category because the

definition of the newly created "Carbonated Energy Drinks" category excluded the two

beverages. Compared to Panel 1 's data, Panel 4 could not sort Powerade™ Option (33),

and Propel® Fitness Water (34) into a category, but these beverages were placed in the

"Fitness and Sports Drinks" and "Flavored Waters", respectively (Table 4.4, Figures 4.12

and 4.13).

The results from the reproducibility study suggest that there may be functional

beverage categories commonly used and understood. Two sets of panelists were able to

generate a set of similar functional beverage categories with good correspondence. Since

the results from the reproducibility study showed similar generated categories and

80

placement of beverages into categories, a minimum of only thirteen panelists may be

necessary to complete the two-step sensory sorting method,

4.5 Conclusions

We have described a two-step sensory sorting method based on panelists' sorting

of 45 to 50 commercially-available functional beverages. This approach combines

sensory observation of products and the use of multivariate statistics in the development

of functional beverage categories. The two-step sensory sorting method is still in its

initial stages and needs more research to be considered a valid categorization method.

Future studies include combining the results of the sorting research with a

consumer test on the same functional beverages. An external preference map could-be

generated based on the functional beverage categories and the beverages preferred by

consumers. This could aid in the prediction of potential opportunities in the market for

new product development based on the relationship between functional beverage

categories and consumers' preferred beverage characteristics.

Other possible studies include testing the two-step sensory sorting method on

other products such as cereals, candies, and food bars. One study would be to have a

panel sort a set of products that fall into well-known, defined categories to determine if

the method accurately categorizes the products into predetermined well-defined

categories. Another study could be conducted by sorting based on only oral perceptions

using coded samples, to determine if the functional beverages could be categorized by

only oral sensations and tastes without the influence of packaging or product

identification. The level of sweetness, mouthfeel, and other oral sensations may play a

role in different functional beverage categories. Lastly, it would be interesting to conduct

the two-step sensory sorting method on a group of functional beverages including newly

81

introduced products to determine if new functional beverage categories would be created

or if the categories generated in this study would suffice.

4.6 References

Bertino M, Lawless HT. 1993. Understanding mouthfeel attributes: a multidimensional scaling approach. J Sens Stud 8(2): 101-14.

Delarue J, Sieffermann JM. 2004. Sensory mapping using Flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products. Food Qual Pref 15(4):383-92.

Falahee M, MacRae AW. 1997. Perceptual variation among drinking waters: the reliability of sorting and ranking data for multidimensional scaling. Food Qual Pref 8(5):389-94.

Faye P, Bremaud D, Durand Daubin M, Courcoux P, Giboreau A, Nicod H. 2004. Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings. Food Qual Pref 15(7-8):781-91.

Faye P, Bremaud D, Teillet E, Courcoux P, Giboreau A, Nicod I-I. 2006. An alternative to external preference mapping based on consumer perceptive mapping. Food Qual Pref 17(7-8):604-14.

Fraley C, Raftery AE. 1998. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal 41(8):578-88.

Gawel R, Oberholster A, Francis IL. 2000. A 'mouth-feel wheel1: terminology for communicating the mouth-feel characteristics of red wine. Australian journal of grape and wine research,6(3):203-07.

Giboreau A, Dacremont C, Egoroff C, Guerrand S, Urdapilleta I, Candel D, Dubois D. 2007. Defining sensory descriptors: Towards writing guidelines based on terminology. Food Qual Pref 18(2):265-74.

Hubert L, Arabic P. 1985. Comparing partitions. Journal of classification 2(1):193-218.

King MC, Cliff MA, Hall JW. 1998. Comparison of Projective Mapping and Sorting Data Collection and Multivariate Methodologies for Identification of Similarity-Of-Use Snack Bars. J Sens Stud 13(3):347-58.

Kruskal JB. 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29(2): 115-29.

82

Kruskal JB, Wish M. 1978. Multidimensional scaling Quantitative applications in the Social Sciences. Sage Publications Newbury Park. 93p.

Krzanowski WJ, Marriott FHC. 1995. Multivariate analysis. New York: Halsted Press. 280 p.

Lawless HT, Sheng N, Knoops SSCP. 1995. Multidimensional scaling of sorting data applied to cheese perception, Food Qual Pref 6(2):91-8.

Lim J, Lawless HT. 2005. Qualitative differences of divalent salts: multidimensional scaling and cluster analysis. Chem Senses 30(9):719-26.

MacQueen JB. 1966. Some Methods for Classification and Analysis of Multivariate Observations. Proc. Fifth Berkeley Symp. on Math. Statist, and Prob.281-297,

Moreau CP, Markman AB, Lehmann DR. 2001. "What is it?" Categorization flexibility and consumers' responses to really new products.-Journal of Consumer Research 27(4):489-98.

Murphy GL, Ross BH. 1994. Predictions from uncertain categorizations. Cogn Psychol 27(2): 148-93.

Nestrud MA, Lawless HT. 2008. Perceptual mapping of citrus juices using projective mapping and profiling data from culinary professionals and consumers. Food Qual Pref 19(4):431-38.

Pages J. 2005. Collection and analysis of perceived product inter-distances using multiple factor analysis: application to the study of 10 white wines from the Loire Valley. Food Qual Pref 16(7):642-9,

Perrin L, Symoneaux R, Maitre I, Asselin C, Jourjon'F, Pagfis J. 2008. Comparison of three sensory methods for use with the Napping® procedure: Case often wines from Loire valley. Food Qual Pref 19(1): 1-11.

Rand WM. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 846-50.

Rugg G, McGeorge P. 1997. The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Syst 14(2):80-93.

Saint-Eve A, Paci Kora E, Martin N. 2004. Impact of the olfactory quality and chemical complexity of the flavouring agent on the texture of low fat stirred yogurts assessed by three different sensory methodologies. Food Qual Pref 15(7-8):655-68.

Steinley D. 2004. Properties of the Hubert-Arable adjusted Rand index. Psychol Methods 9(3):386-96.

83

Tang C, Heymann H. 2002. Multidimensional Sorting, Similarity Scaling and Free-Choice Profiling of Grape Jellies. J Sens Stud 17(6):493-509.

Tarea S, Cuvelier G, Sieffermann JM. 2007. Sensory Evaluation of the Texture of 49 Commercial Apple and Pear Purees. J Food Qual 30(6): 1121-31.

Viswanathan M, Childers TL. 1999. Understanding How Product Attributes Influence Product Categorization: Development and Validation of Fuzzy Set-Based Measures of Gradedness in Product Categories. J Market Res 36(l):75-94.

Woolf A, Wright R, Amarasiriwardena C, Bellinger D. 2002. A child with chronic manganese exposure from drinking water. Environ Health Perspect 110(6):613-6.

Wright R. 2008. Nutraceuticals Coast in the Beverage Market. Nutraceuticals World [serial online]. Available from Posted July 2008.

4.7 Tables and Figures

Table 4.1: Fifty commercially-available functional beverages and corresponding numerical codes.

No. Code

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Commercially-Available Functional Beverages

Arizona® Pomegranate Green Tea

Bolthouse® Farms Fruit Smoothie

Boost®

Capri Sun® Sport ™

Dannon™ - Danimals®

Dannon™ - Light 'n Fit® Smoothie

Dannon™ - Frusion®

Dasani® Flavored Water

Elements Enerpy®

Ensure® Shake

Fruit20®

Full Throttle®

Fuze® "Refresh"

Fuze® "Slenderize"

Fuze® Green Tea

Gatorade® Endurance

Gatorade® Lemonade

Gatorade® Original

Gatorade® Rain

Glucerna®

Gold Peak™ Iced Tea

Honest Tea®

Lifeway® Lowfat Kefir

Lipton® Original White Tea

MDX

No. Code

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

Commercially-Available Functional Beverages

Metromint®

Minute Maid® Fruit Falls™

Naked® Fruit Smoothie

NOS®

Pcdiasure®

Powerade™

Powerade™ - Advance

Powerade™ Option

Propel® Fitness Water

Rockstar®

Slimfast Optima®

Snapple® White Tea

Sobe® - NoFcnr

Sobe® - Power

Sobe® - Tsunami

Sobe® Lean Energy Diet Citrus

Sobe Life Water® Stonyfield Farm® Organic Smoothie

Sweet Leaf™ Tea

TAB® Energy

Tazo® Iced Tea

Trinity® Water

Whitney's® Yo on the Go™

Yoplait® Go-GURT® Smoothie

Yoplait® Nouriche® SiiperSmoothie

85

Table 4.2: Adjusted Rand Index values of the comparison of clusters generated through free and fixed sorting tasks by Panels 1 to 4.

Comparison Adjusted

Rand Index Visual vs. Visual-Oral Sorts Panel 1 Free Visual Panel 1 Fixed Visual Panel 2 Fixed Visual Panel 3 Fixed Visual Panel 4 Free Visual

Panel 1 Free Visual-Oral Panel 1 Fixed Visual-Oral Panel 2 Fixed Visual-Oral Panel 3 Fixed Visual-Oral Panel 4 Free Visual-Oral

0.80 0.94 0.84 0.97 0.87

Validation Sorts Panel 1 Fixed Visual Panel 1 Fixed Visual-Oral Panel 1 Fixed Visual Panel 1 Fixed Visual-Oral

Panel 2 Fixed Visual Panel 2 Fixed Visual-Oral Panel 3 Fixed Visual Panel 3 Fixed Visual-Oral

0.91 0.93 0.88 0.93

Reproducibility Sorts Panel 1 Free Visual Panel 1 Free Visual-Oral Panel 1 Fixed Visual Panel 1 Fixed Visual-Oral

Panel 4 Free Visual Panel 4 Free Visual-Oral Panel 4 Fixed Visual Panel 4 Fixed Visual-Oral

0.84 0.85 0.82 0.77

Table 4.3: Compilation of Panel 2 and 3's validation study results of commercially-available functional beverages sorted into categories by visual and visual-oral fixed sorts compared to Panel l 's results. The categories were determined based on K-means clustering. ED=Energy Drinks, EW=Enhanced Waters, FRU=Fruit Smoothies, M=MisceIIancous, NUT=Nutritional Drinks, SP=Sports Drinks, T=Tcas, and YOG=Yogurt Smoothies. # denotes beverages that were not available for purchase during the time of the sorting tasks.

87

Table 4.3 (cont.)

No, Code

12 25

29 35

38 45 39

40

41 9

32

8

11 26

27 42 47

13 14 2

28 34 33

3 10

20 30 36

4 16

17 18

19 31

1 15

21 22 24

37 44 46

5 6

7 23

43 48

49 50

Commorclally-Avallablo Beverages

Full Throttle'1'

MDX NOS®

Rockstar® Sobe®-NoFear TAB® Energy

Sobe® - Power

Sobe®-Tsunami Sobe® Lean Energy Diet Citrus

Elements Energy® Powerade™ - Advance

Dasani® Flavored Water Fruit20® Metromint®

Minute Maid® Fruit Falls™ Sobe Life Water® Trinity® Water

Fuze® "Refresh" Fuze® "Slenderize" Bolthouse® Farms Fruit Smoothie

Naked® Fruit Smoothie Propel® Fitness Water Powerade™ Option Boost® Ensure® Shake

Glucerna® Pediasure®

Slimfast Optima® Capri Sun® Sport ™

Gatorade® Endurance Gatorade® Lemonade

Gatorade® Original Gatorade® Rain

Powerade™

Arizona® Pomegranate Green Tea Fuze® Green Tea

Gold Peak™ Iced Tea Honest Tea®

Llpton® Original White Tea Snapple® White Tea

Sweet Leaf™ Tea Tazo® Iced Tea

Dannon™- Danlmals® Dannon™ - Light 'n Fit® Smoothie Dannon™ - Frusion® Lifeway® Lowfat Kefir

Stonyfield Farm® Organic Smoothio Whitney's® Yo on the Go™

Yoplait® Go-GURT® Smoothie Yoplait® Nouriche® SuperSmoolhle

FIxod Visual Panol 1

ED

ED

ED ED ED ED

ED

ED

ED ED

ED

EW

EW EW

EW EW EW

EW EW FRU FRU

•̂.'••M '• & '.Ms iViWpr&y ^Nu.r&;; WNUTtsft S&fmtSS fWrAS

SP

SP

SP

SP

SP SP

T T

T T

T T

T T

". YOG : ,-• YOG- . '•?YOGV.

H'J'YOG ,'•

.'.-YOG-. '•

:V.;;YOG; •

':..,.Y.OG',; •YOG

FIxod Visual-

Oral Panol 1

ED

ED ED ED

ED ED

ED

ED

# # #

EW EW EW

EW EW

# ' ' " - - ; 'M.-"

,:.-i&M- ••••< FRU FRU

•*•'•'• M •'..''•'. : ;M ••.•

^ f l U T ^ i ${$NOTi$$ #ISNUIJS& w#Mi®&i ^KNUT^i^;

SP SP

SP

SP

SP SP

T T

T

T

T T

T T

'• .YOG'' YOG

. YOG •

v . ypp' . . YOG

. YOG. •

'"'YOG YOG

FIxod Visual Panol 2

ED

ED ED

ED ED ED

'• ' r M " ' M • . ED

ED

M EW

EW EW

EW EW EW

w '-M .'

. ' • • ' ' M - ' " ' FRU FRU

YI.M ' ' •, M •.

!,©'NUI.$;

ii&'NUTfly ^ 'NUT^ i feNus-fls: 1»:.l'NUT.f©

SP SP

SP

SP SP

SP

T T

T T T T

T T

.' YOG YOG •

"•;.•. YOG' • '.

•i,yyoG •.. '•'rY.OG' ..

*• YOG :'-5'"YOG '

YOG

Fixod Visual-Oral

Panol 2

ED

ED ED ED

ED ED

ED

ED

# # #

EW

EW EW EW

EW

# "*''? M' '% - :.M "'

FRU FRU EW

SP

JkMyxtfjsi 'SijijKlDT^i: t'fKNUf®-^HWX#£ WMTrWIfo

SP SP

SP SP SP

SP

T T

T T

T T

T T

. YOG. YOG

,'YOG'

.. ...YOG.. ..

.'; YOG • YOG

..-'.YOG' ' YOG

FIxod Visual Panol 3

ED

ED ED ED

ED ED

ED

ED ED

ED

'•'.M EW

EW EW EW

EW EW

* " W > ' M " ' '• '"""

''''•.'• M ;- • FRU FRU

EW

••'... • M >$MnM WINUTSSF

ijj'ftNUisfeSt

S»iiW.K iMi$UT;i&

SP SP

SP

SP SP

SP

T T

T T

T

T

T T

YOG • YOG .- YOG"::

. . .YOG .v .

... YOG:. '

• .•'YOG' ••

. ..YOG' YOG

FIxod Visual-Oral

Panol 3

ED

ED ED ED

ED

ED ED

ED

ED

# #

EW EW EW

EW EW

# :.'.r-M"i5.!.

••• M-••'••:. FRU FRU

EW SP

;V ;NUTK<«

AH'NUTrAft

&i'.'NUTtff >'4NM'Ti«

'"'rjNUT^f SP SP SP

SP SP

SP

T

T

T T

T

T T

T ••" YOGv': • YOG'-'

. .L .YOG' ' .

'..•.:YOG :

\ '• YOG

' YOG

YOG ..

#

88

Tabic 4.4: Compilation of Panel 1 and 4's reproducibility study results comparing commercially-available functional beverages sorted into categories by visual and visual-oral free and fixed sorting task results. The categories were determined based on K-mcans clustering. ED=Energy Drinks, EW=Enhanccd Waters, FRU=Fruit Smoothies, M=Misccllaneous, NUT=Nutritional Drinks, SP=Sports Drinks, T=Tcas, and YOG=Yogurt Smoothies. # denotes beverages that were not available for purchase during the time of the sorting tasks.

Tab

No. Code

12

25

29

35

38

45

9

32

39

40

41

8

11

26

27

42

47

13

14

2

28

34

33

3

10

20

30

36

4

16

17

18

19

31

1

15

21

22

24

37

44

46

5

6

7

23

43

48

49

50

c 4.4 (cont.)

Commerc ia l l y -Ava i l ab le Boveraqes

Full Throttle®

MDX

NOS®

Rockstar®

Sobe* - NoFear

T A B " Energy

Elements Energy*

Powerade™ - Advance

Sobe® - Power

Sobe®-Tsunami

Sobe® Lean Energy Diet Citrus

Dasani® Flavored Waler

Frult20®

Metromint®

Minute Maid® Fruit Falls™

Sobe Lite Water®

Trinity® Waler

Fuze® "Refresh"

Fuze® "Slenderize"

Bolthouse® Farms Fruit Smoothie

Naked® Fruit Smoothie

Propel® Fitness Water

Powerade™ Option

Boost®

Ensure® Shake

Glucerna®

Pediasuro®

Slimfast Optima®

Capri Sun® Sport ™

Gatorade® Endurance

Gatorade® Lemonade

Gatorade® Original

Gatorade® Rain

Powerade™

Arizona® Pomegranate Green Tea

Fuze® Green Tea

Gold Peak™ Iced Tea

Honest Tea®

Lipton® Original Whi te Tea

Snapple® White Tea

Sweet Leaf™ Tea

Tazo® Iced Tea

Dannon™ - Danlmals®

Dannon™ - Light 'n Fit® Smoothie

Dannon™ - Frusion®

Lifeway® Lowfat Kefir

Stonyfield Farm® Organic Smoothie

Whitney's® Yo on the Go™

Yoplai t* Go-GURT® Smoothie

Yoplait® Nouriche® SuperSmoolhle

Froo V isua l

Panot 1

ED

ED

ED

ED

ED

ED

ED

ED

''";f M" •

"•.•.•> M ' •

• '••'"M .

EW

EW

EW

EW

EW

EW

;•• M

''• M

YOG

. Y O G •

EW

SP

WWr!® &RUT& •v?'Ninv*s ^'NUfRl $f t iUT$

••"M '• SP

SP

SP

SP

SP

T

T

T

T

T

T

T

T

•."•YOG.

i YOG

' . .YOG

' Y O G . ' '

V . Y O G :

• ; Y O G

;'.'YOG

YOG

Freo Visual-Oral Panel 1

ED

ED

ED

ED

ED

ED

ED

" . M

M

' .M

M

EW

EW

EW

M

EW

EW

M

• M ••

FRU

FRU

SP

SP

i.J&iNMTi'-ti-i;

; ! ! #NUT&$

^ f i N U T ^ j -

:WNLIT)#; :

foiSNlntfvSv

M

SP

SP

SP

SP

SP

T

T

. T

T

T

T

T

T

• ' " . ' lYOG' '

. YOG •

. YOG •

' . . ' .YOG •'

.• -.YOG •

- . . Y O G •

"YOG.

YOG

FIxod V isua l Panol 1

ED

ED

ED

ED

ED

ED

ED

ED

ED

ED

ED

EW

EW

EW

EW

EW

EW

EW

EW

FRU

FRU

^ M : . <

'$>.'M';3:.'

&NUTV?

ittNUTft SflltJTvff

'MU& #N.Ut&

SP

SP

SP

SP

SP

SP

T

'•t'T

:';' T ' ' X

X

.-. X T

T

i.-YOG.

• ' . 'YOG.

' ;YOG •

l l Y O G

. i 'YOG •,

l uYOG. ;

kTtae „• •:;.y.o'G

FIxod V isua l -Ora l

Panol 1

ED

ED

ED

ED

ED

ED

it

# ED

ED

it

EW

EW

EW

EW

EW

it

'.• M >

' ' M '

FRU

FRU

.::MM*&'

?^MiiS::s ISiiNUT®

>'JSM(JT;®i •S'f.ROt;;̂ ',' v.&iftU.T-Htt

^ N U t l ^ ! SP

SP

SP

SP

SP

SP

• T

X

X

• T

X •

• T

• X

T

;'' : r.,YOG/.

. ' . Y O G . . .

:.. YOG

• ..YOG

•••'.-YOG--...-

. ' • ;YOG. ' ',

, ••:•..'YOG v".

.'•AYOG'. '

Froo V isua l Panol 4

ED

ED

ED

ED

ED

ED

ED

..VM . •

V'M ' ' . J M . .

"••.' M .

EW

EW

EW

EW

EW

EW

• ' M

V'"M . ' Y O G

.; YOG •

r.^M-'-:. SP

• » * ^'NUjrii':'

mm H.WT&: piNUT,N

SP

SP

SP

SP

SP

SP

T

T .

' T

• -T

'• T

T

T

T

"YOG

''.'.YOG

•.•yoG.

'::'.!YOG

'5YOG

J'YO'G'':

;;;,YOG' •

" Y O G '

Free V isua l -

Ora l Panol 4

ED

ED

ED

ED

ED

ED

it

it

' ." ' M '

' M '

M

EW

EW

EW

EW

EW

if

M

' M '

YOG

" •YOG; . ;

EW

SP

SkNfthJK ?#NUTv&

ft'N'UM'f

^ N U T * ! : v

.."NUTi?1'

• M

SP

SP

SP

SP

SP

T

T

X

• T

T

T

T

r Y O G .

YOG

.YOG

. YOG

. .YOG •

• vYOG •

t O G

it

FIxod V i sua l Panel 4

ED

ED

ED

ED

ED

ED

ED

SP

ED

ED

ED

EW

EW

EW

EW

EW

EW

. M

• M-- '

• Y O G '

. Y O G .

'y>M-

SP

^IKUTA!

'>'!Nmxi< y^ura

tf.NU-tf;

SNUT*: SP

SP

SP

SP

SP

SP

T

X

T

T

T

T

T

T

YOG

YOG

Y O G .

'•' iYOG

.YOG, . .

YOG' . -

.-•YOG .

• Y O G

F ixed V i sua l -

Oral Panol 4

ED

ED

ED

ED

M

ED

it

it

•'•' M

'. M •

. 'M

EW

EW

• EW

EW

EW

it

•' M .

'• " M

YOG

-YOG

EW

SP

SNUtiS .^tipf/Vi

v?,N.UT./.:;

• ^ N U J ; * ?

•*$Wff-\

SP

SP

SP

SP

SP

SP

T

T

T

T

T

T

X

T

YOG

:• YOG

' Y O G • '

YOG

YOG.

/•YOG

• YOG

it

Two-step Sensory Sorting Method

Panel 1 (PI) 13 panelists (3 MT OF)

Validation Study Panel 2 (P2)

10 panelists (5 M6 F)

Validation Studv . Panel3 (P3)

13 panelists (3 MTOF)

Reproducibilrtv Studv Panel (P4)

13 panelists (3 MT OF)

Figure 4.1: Flow chart of studies and panels, categ sorting method. M=MaIes and F=FemaIes

PI Fixed Categories

PI Fixed

Categories

PI Filed Categories

2*0^^

P4 Fixed

V

Y

V

V

V

V

Msual

Msual-Oral

Msual

Msual-Oral

Msual

Msual-Oral

Visual

Mstnl-Oral

Visual

Msual-Oral

Msual

Msual-Oral

^ PI Free Sort

Generated Fixed Categories

^

P4 Free Sort Generated Fixed

Categories

ies, types of sorting task, and results of the conducted two-step sensor}'

12

Q 0

-4

-12

Group6

Group 1,

Group 5,

-12 -8 0

Dim1

12

Group 1

2

3

4

•'5

6

C^egory

Energy Drinks

Enhanced Waters

Nutritional Drinks

Smoothies

Sports Drinks

Teas

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid Contains many nutrients could serve as a meal-replacement beverage

Contains fermented dairy products; opaque Mineral or vitamin enhanced; does not contain caffeine. Labeling targets athletes Contains Tea or Tea Extracts

Figure 4.2: Multidimensional Scaling Panel l 's visual free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.265 and functional beverage categories generated through the free visual sorting method. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

12

-4

-8

-12

Group 5

-12 0

Dim1

12

Group

1

2

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly

flavored, clear liquid

Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

Figure 4.3: Multidimensional Scaling of Panel l 's visual-oral free sort (Part 1) of 50 functional beverages plotted in two dimensions with stress = 0.290 and functional beverage categories generated through the free visual-oral sorting method. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. . Refer to Table 4.1 for product names and corresponding number codes.

Group 2

Group 1

Group4

o

Dim1

12

Group

1

2 .

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid

Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not

contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

Figure 4.4: Multidimensional Scaling of Panel l 's visual fixed sort (Part 2) of 50 functional beverages plotted in two dimensions with stress = 0.289 and corresponding functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

12

-4

-12

-12 0

Dim1

12

Group 1

2

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

Figure 4.5: Multidimensional Scaling of Panel l 's visual-oral fixed sort (Part 2) of 45 functional beverages plotted in two dimensions with stress = 0.283 and corresponding functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 4.1 for product names and corresponding number codes.

12

-12

Group 5 Group 4

-12

j-JSroup 7

Group 3

o

Diml

Group

1

2

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

Figure 4.6: Multidimensional Scaling of Panel 2's visual fixed sort of 50 functional beverages plotted in two dimensions with stress = 0.301 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 4.1 for product names and corresponding number codes.

-12

-12 0

Diml

12

Group

1

2 .

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid

Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not

contain caffeine. Labeling targets

Contains Tea or. Tea Extracts

Contains fermented dairy products;

Figure 4.7: Multidimensional Scaling of Panel 2's visual-oral fixed sort of 46 functional beverages plotted in two dimensions with stress = 0.271 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

a

-12

Group

1

2

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid

Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

-12 12

Diml

Figure 4.8: Multidimensional Scaling of Panel 3's visual fixed sort of 50 functional beverages plotted in two dimensions with stress = 0.241 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 4.1 for product names and corresponding number codes.

o 00

Group

1

2

3

4

5

6

7

Category

Energy Drinks

Enhanced Waters

Fruit Smoothies

Nutritional Drinks

Sports Drinks

Teas

Yogurt Smoothies

Characteristics

Contains stimulants such as caffeine and taurine; provides extra energy

Contains minimal Calories, lightly flavored, clear liquid Contains 100% Fruit,; non-clear liquid; all natural

Contains many nutrients could serve as a meal-replacement beverage

Mineral or vitamin enhanced; does not contain caffeine. Labeling targets

Contains Tea or Tea Extracts

Contains fermented dairy products;

Figure 4.9: Multidimensional Scaling of Panel 3's visual-oral fixed sort of 46 functional beverages plotted in two dimensions with stress = 0.284 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

12

-12

-12

Diml

12

Group

1

2

3

4

5

6

Category

Carbonated Energy Drinks

Flavored Waters

Nutritional Healthy Drink

Smoothies

Sports and Fitness Drinks

Teas

Characteristics

Drinks containing caffeine or other

stimulants and is bubbly; provides a sense of

restoring energy

Water drinks with added flavors or vitamins; tastes like sweetened flavored water; no stimulants added

Meal replacements which are related to health; contains a lot of added nutrients; has a milk-like texture and is thick Beverages made with yogurt and/or fruit; thick and creamy and has fruit flavors Marketed to refuel body, contains electrolytes; not too sweet; thirst-quenching, and very light

Tea-based drinks, and is labeled with "tea" or "iced tea"; the main flavor is tea

Figure 4.10: Multidimensional Scaling of Panel 4's visual free sort (Part 1) of 46 functional beverages plotted in two dimensions with stress = 0.260 and functional beverage categories generated through the free visual sorting method. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 4.1 for product names and corresponding number codes.

12

-12

-12 0

Diml

Group

1

2

3

4

5

6

Category

Carbonated Energy Drinks

Flavored Waters

Nutritional Healthy Drink

Smoothies

Sports and Fitness Drinks

Teas

Characteristics

Drinks containing caffeine or other stimulants and is bubbly; provides a sense of restoring energy

Water drinks with added flavors or vitamins;

tastes like sweetened flavored water; no

stimulants added

Meal replacements which are related to

health; contains a lot of added nutrients; has

a milk-like texture and is thick

Beverages made with yogurt and/or fruit;

thick and creamy and has fruit flavors

Marketed to refuel body, contains electrolytes; not too sweet; thirst-quenching, and very light

Tea-based drinks, and is labeled with "tea" or "iced tea"; the main flavor is tea

Figure 4.11: Multidimensional Scaling of Panel 4's visual-oral free sort (Part 1) of 46 functional beverages plotted in two dimensions with stress = 0.232 and functional beverage categories generated through the free visual-oral sorting method. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

Group

1

2

4

5

6

Category

Carbonated Energy Drinks

Flavored Waters

Nutritional Healthy Drink

Smoothies

Sports and Fitness Drinks

Teas

Characteristics

Drinks containing caffeine or other

stimulants and is bubbly; provides a sense of

restoring energy Water drinks with added flavors or vitamins; tastes like sweetened flavored water; no stimulants added

Meal replacements which are related to

health; contains a lot of added nutrients; has

a milk-like texture and is thick

Beverages made with yogurt and/or fruit;

thick and creamy and has fruit flavors

Marketed to refuel body, contains electrolytes; not too sweet; thirst-quenching, and very light

Tea-based drinks, and is labeled with "tea" or "iced tea"; the main flavor is tea

Figure 4.12: Multidimensional Scaling of Panel 4's visual fixed sort (Part 2) of 46 functional beverages plotted in tvvo dimensions with stress = 0.261 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot Refer to Table 4.1 for product names and corresponding number codes.

Group 1

2

3

4

5

6

Category

Carbonated Energy Drinks

Flavored Waters

Nutritional Healthy Drink

Smoothies

Sports and Fitness Drinks

Teas

Characteristics

Drinks containing caffeine or other stimulants and is bubbly; provides a sense of restoring energy Water drinks with added flavors or vitamins; tastes like sweetened flavored water; no stimulants added Meal replacements which are related to health; contains a lot of added nutrients; has a milk-like texture and is thick Beverages made with yogurt and/or fruit; thick and creamy and has fruit flavors Marketed to refuel body, contains electrolytes; not too sweet; thirst-quenching, and very light Tea-based drinks, and is labeled with "tea" or "iced tea"; the main flavor is tea

Figure 4.13: Multidimensional Scaling of Panel 4's visual-oral fixed sort (Part 2) of 46 functional beverages plotted in two dimensions with stress = 0.262 and corresponding fixed functional beverage categories. Functional beverages were grouped based on K-means clustering of beverage coordinate points on the MDS plot. Refer to Table 4.1 for product names and corresponding number codes.

CHAPTER 5 - SENSORY PROFILE OF A MODEL ENERGY DRINK WITH VARYING LEVELS OF FUNCTIONAL INGREDIENTS-CAFFEINE, GINSENG, AND TAURINE

5.1 Abstract

Energy drinks have increased in popularity in recent years due to the claimed

energy boost provided by functional ingredients. A multitude of functional ingredients

have been utilized; however, there is limited research on their sensory effects in energy

drink formulations. Descriptive analysis was conducted to investigate the effects on the

sensory properties of three common functional ingredients - caffeine, ginseng, and

taurine - in a non-carbonated model energy drink solution. Combinations of these

functional ingredients at three levels (low, medium, high) were added to create a total of

27 different solutions (3x3x3 factorial design). Analysis of variance was performed to

evaluate the sensory effects of the varying concentrations of functional ingredients in

solution. Principal component analysis (PCA) was performed to summarize the

relationship among the attributes and solutions. In general, high levels of caffeine in

solution resulted in low ratings of fruity attributes and high ratings of bitter attributes.

The high level of ginseng in solution was characterized by high ratings of bitter

attributes. A horns effect was observed as the sweet, artificial lemon-lime, pear, mango,

and pineapple attributes were rated lower in intensity with increased ginseng levels.

Taurine levels of up to 416 mg/100 mL had no significant effect on the sensory attribute

ratings of the model energy drink solutions. These findings can be utilized to predict the

changes in sensory characteristics when formulating energy drinks containing these

popular functional ingredients.

104

Key Words: Descriptive Analysis, Energy Drinks, Functional Ingredients, Caffeine,

Ginseng

5.2 Introduction

Energy Drinks

Energy drinks are one of the fastest growing segments of the functional beverage

market, with over 200 drinks introduced into the market between 2006 and 2007 (Reissig

and others 2009). They have gained popularity for the extra energy they provide via a

large concentration of stimulants. Energy drinks are an alternative to coffee as a source

of caffeine, and also contain other functional ingredients such as antioxidants, ginseng,

taurine, and B vitamins. In 2007, there were over $10.1 billion in functional beverage

sales in the US, and by 2010 functional beverage sales are projected to increase to over

$12 billion (Mintel 2008),

Functional Ingredients

Three of the most common functional ingredients in energy drinks are caffeine,

ginseng, and taurine. Caffeine is a methylxanthine with the chemical formula

C8H10N4O2. It is a white odorless powder with low solubility and is usually combined

with other chemicals, such as purines and pyrimidines, to increase its solubility (Spiller

1998). Caffeine is commonly found in cola products and has been incorporated into

snack foods, such as cereal bars and sunflower seeds (Cosgrove 2008). Caffeine is on the

US Food and Drug Administration (FDA)'s Generally Recognized as Safe (GRAS) list

and is limited to no more than 0.02% by volume in cola-type products (Food and Drug

Administration 2003). Currently, there are no regulations regarding the maximum

105

amount of caffeine allowed in energy drinks. The typical amount of caffeine in energy

drinks ranges from 21 to 112 mg/100 mL (wt/vol) (Table 5.1).

Ginseng is from the Araliaceae family and contains ginsenosides, which are active

steroid-like compounds (Spiller 1998). These active compounds of ginseng are

triterpenoid saponin glycosides, which also are responsible for the bitter taste of ginseng

(Court 2000a). Ginseng is known to have antioxidant properties (Jung and others 2002)

and may aid in alleviating some health conditions, such as diabetes and cognitive

function (Coon and Ernst 2002). Some research has been conducted on the efficacy of

consuming ginseng for increased energy, help with indigestion, and overall improvement

of health (Court 2000b). However, there has been limited research validating these

medicinal benefits attributed to ginseng (Kitts and Hu 2000, Vogler and others 1999).

Taurine is a derivative of the amino acid cysteine with the chemical formula of

C2M7NO3S, and is present in the tissues of humans and animals. It aids with bile acid

conjugation, retinal development, and central nervous system function (Lourenco and

Camilo 2002). Taurine has also been found to aid in immunity and may have antioxidant

properties (Yu and Kim 2009). Taurine is commonly incorporated in energy drinks and

in muscle-building supplements because of its suggested benefits such as improved

athletic performance and increased energy. Research suggests that higher levels of

taurine in muscle tissues may improve optimal exercise performance in rats (Yatabe and

others 2009). Taurine supplementation in humans, however, has not been shown to

improve exercise performance (Galloway and others 2008).

106

Sensory Analysis of Energy Drinks

Studies have been conducted on the acceptability of new functional beverages, but

limited research has been done on the effects of functional ingredients on the sensory

properties of model functional beverage solutions. Luckow and Delahunty (2004)

conducted research on the addition of probiotics and prebiotics in orange juice, while

Smit and Rogers (2002) added different levels of caffeine and vitamins to an energy drink

to determine the difference in preference with the addition of stimulants. They found that

the energy drinks containing higher concentrations of caffeine, vitamins, and stimulants

were not liked as much as the energy drinks containing lower concentrations of

functional ingredients. Panelists also preferred pure water over both energy drinks.

Another sensory test found that the concentration of 100 mg/L caffeine in a mixed

tropical fruit juice nectar had acceptable ratings (de Sousa and others 2007). While

Qimire (2000) determined that there was no significant difference between orange juice

with and without 600 mg of ginseng (20% ginsenosides) per liter, and ginseng

concentrations of 1000 mg/L of orange juice resulted in a medicinal taste.

Research related to the sensory effects of the addition of ingredients has suggested

that there is a synergistic effect of including multiple ingredients into a formulation.

Previous studies suggest that mixtures of tastants result in an increase in overall intensity

ratings of the compound mixture (Delwiche 2004). Therefore, the more functional

ingredients added to a beverage formulation, the more likely the tastes will be noticed.

The objective of this study was to investigate the effects on the sensory properties of

three common functional ingredients - caffeine, ginseng, and taurine - in a non-

carbonated model energy drink solution.

107

5.3 Materials and Methods

Model Energy Drink Formulation

The base model energy drink solution was composed of 1106.45 g spring water

(Absopure, Plymouth, MI), 285.00 g high fructose corn syrup (Isosweel 5500, Tate &

Lyle, Decatur, IL), 4.04 g sodium citrate (Tate & Lyle, Decatur, IL), 3.80 g citric acid

(Tate & Lyle, Decatur, IL), and 0.70 g potassium citrate (Tale & Lyle, Decatur, IL). The

model energy drink base solution was developed to have a Brix of 12.20°B and pH of

3.0, which fall in the range of commercially-available energy drink values. Non-

carbonated "still" solutions were used in this study to eliminate interference between the

carbonation and the actual changes due to the different levels of functional ingredients

tested in the model energy drink solutions.

Sample Preparation

Functional ingredients tested included caffeine (Fisher Scientific, Fair Lawn, NJ,

07410), 80% ginsenosides panax ginseng (Amax NutraSource, Inc, Eugene, OR), and

taurine (Nutrabio.com, Inc. Middlesex, NJ). Combinations of three levels of the three

functional ingredients (caffeine, ginseng, taurine) were added to the,base solution to

create a total of 27 different solutions (3x3x3 factorial design) as shown in Table 5.2.

The concentrations of functional ingredients added to the model solution were

determined based on the range of the amounts available in a sampling of commercially-

available energy drinks (Table 1). For each solution, functional ingredients were

weighed and brought up to a 200 mL volume with spring water (Absopure, Plymouth,

MI). The solutions were then mixed for five minutes with a magnetic stir bar on a stir

plate. The functional ingredient mixture solution was added to 447 mL of the model

108

energy drink solution and mixed for another five minutes with a magnetic stir bar on a

stir plate.

The solutions were stored overnight in sealed wide-mouth glass mason jars .

(14400-67000 Ball®, Alltrista, Munice, IN) at ~5°C in a commercial grade refrigerator.

On the same day as evaluation, approximately 35 mL samples were poured into 73.9 mL

plastic souffle cups (Solo Cup Company, Urbana, IL) labeled with random 3-digit codes.

The samples were stored in the refrigerator until 10 minutes prior to evaluation.

Panelists Selection and Screening

The panelist recruitment and selection process included a questionnaire, a test for

6-n-propyl-2-thiouracil (PROP) status, and a basic tastes test (sour, sweet, bitter, salty).

The questionnaire asked volunteers about basic demographic information, allergies,

smoker status, frequency of functional beverage consumption, and schedule availability.

PROP taster status was determined by presenting volunteers pieces of filter paper

impregnated with PROP following Zhao and others (2003) paper disc method. If the

volunteers could not taste anything on the paper they were considered a non-taster. If the

volunteers could taste a bitter taste, they were labeled a taster.

The basic taste test consisted of presenting volunteers with 20 mL of basic taste

solutions in 59.2 mL plastic souffle" cups (Solo Cup Company, Urbana, IL), Basic taste

solutions labeled A through F (sweet, sour, bitter, waler, salty, and sour, respectively)

were presented to volunteers. The basic taste solutions tested included: 0.70% sucrose

(C&H Sugar Company, Inc. Crockett, CA) solution for the sweet solution, a 0.05% citric

acid (Tate & Lyle, Decatur, IL) solution for the sour solution, a 0.02% caffeine (Fisher

Scientific, Fair Lawn, NJ) solution for the bitter solution, and a 0.10%o sodium chloride

(Morton , Chicago, IL) solution for the salty solution. All solutions were prepared with

spring water (Absopure, Plymouth, MI). Two sour solutions were presented to minimize

the chance of blind guessing by the volunteers.

Thirteen panelists (4 males, 9 females, 18 to 50 years old) were selected based on

non-smoker and positive PROP taster status, and coirectly identifying two or more of the

basic taste solutions. Four panelists correctly identified all the solutions, three panelists

identified four of the six solutions, five panelists identified three of the six solutions, and

one panelist identified two of the six solutions. Panelists' frequency of energy drink

usage ranged from rarely to daily consumption.

Panelist Training

Panel training consisted of sixteen 1-hour sessions, which included evaluating

three complete replications of the 27 solution set (Table 5.2), Initial training sessions

were conducted at a round table setting under incandescent lighting. The first two days.

included an introduction to the descriptive analysis method to be used in the study and

familiarization with sample solutions. During the next two days, panelists generated

descriptor terms and term definitions for nine samples. For each term and definition

generated, panelists selected and refined a reference. References were chosen to reflect

the sensory attributes of the solutions.

Once the panel generated the terms, developed definitions, and selected

references, the terms were narrowed to the thirteen terms that best represented the

sensory attributes of the solutions. The thirteen terms included: artificial lemon-lime

flavor, citrus, mango, pineapple, pear, sweet, tart, bitter tea, fruit bitter, astringent, bitter

tea afterfeel, fruit bitter afterfeel, and moutheoating (Tabic 5.3). Panelists had a difficult

110

time pinpointing the bitter attributes perceived in the samples. To determine the specific

bitter taste perceived in the samples, bitter references presented to the panelists included a

0.25% caffeine solution, lemon seeds, brewed black tea solution (100 mL brewed tea and

200 mL water), a 0,16% PROP solution, a 0.44% naringin solution, and quinine solutions

(0.013%, 0.067%, 0.05%, 0.097%). Panelists agreed upon the brewed black lea solution

and the 0.44% naringin solution as the references that best matched the bitter tastes

detected in the solutions.

The references for each term were then rated on a 16-point categorical scale (0 to

15) to generate anchors for each attribute. Panelists rated the solutions for each attribute

against group-determined reference anchors. The rinse protocol determined by the

panelists was a warm water (~40°C) rinse followed by a cold water (~20°C) rinse.

Panelists were instructed to follow the rinsing protocol prior to evaluating the first sample

and between samples.

The sampling protocol consisted of sipping one-third of the sample (~12 mL) and

moving it to contact all sides of the tongue and mouth for about 5 seconds before rating

the attributes. The first third of the sample was used to evaluate aroma-by-mouth

attributes, the second third to evaluate taste, and the last third to evaluate mouthfeel and

afterfeel.

Three practice sessions and six data collection sessions were conducted in

individual sensory booths under red light to mask the slight color difference among

solutions. The color difference was due to the different levels of ginseng in each

solution. Each session consisted of monadically presenting nine samples; five samples

then a two-minute break, followed by the four remaining samples.

I l l

References were made 20 to 24 hours in advance and stored in lidded 73.9 mL

plastic souffle cups (Solo Cup Company, Urbana, IL, 61802) at ~5°C in a commercial

grade refrigerator. Panelists familiarized themselves with the references and reference

intensity scores prior to each session. Panelists then entered the individualized booths to

evaluate the samples. The data were collected by the Compusense//ve version 4.2

(Compusense, Inc. Guelph, Ontario, Canada) program. A modified Williams design

(1950) was used to randomize samples among the panelists to balance out first order

carry over effects (Macfie and others 2007).

Flow Behavior and Viscosity Measurements

The flow behavior and viscosities of the solutions were measured using the ARES

RFS III Rheometer (TA Instruments, New Castle, DE) following the method outlined by

Kappes and others (2006), 1,11 mL of solution was placed between two parallel plates

and a shear rate sweep program was run to measure the flow behavior of the solutions.

The shear rate sweep program was run in log mode in a clockwise direction from a rate of

0.6 to 200 s'1. Solutions were measured at 5°C in triplicate and measured on the same

day panelists evaluated them. All solutions exhibited Newtonian behavior, and viscosity

was calculated by the slope of the linear plot of shear rate (s"1) by shear stress (dyn/cm"2).

Data Analyses

Descriptive analysis and viscosity measurement data were analyzed using the

Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc., Cary, NC). Analysis

of Variance (ANOVA) tested for significant difference of mean scores of the solutions,

panelists, and interactions for each attribute. ANOVA was also clone to determine the

effects of the three levels of the different functional ingredients. Fisher's Least

112

Significant Difference (LSD) was conducted to determine the difference among sample

means and by levels of functional ingredients. Principal component analysis (PCA) on

the covariance data matrix with varimax rotation was done using XLStat 2008,4,2

(Addinsoft, New York, NY). Agglomerative hierarchical clustering (AHC) by Ward's

method (1963) was done on the sensory data to observe groupings among the solutions

using XLStat 7.5.3 (Addinsoft, New York, NY).

5.4 Results and Discussion

The ANOVA results of the model energy drink solutions (Table 5.4) show that for

all attributes, except citrus and moutheoating, the panelist and sample factors were highly

significant (pO.001). There was a significant difference in panelist ratings, which

indicate that panelists used different parts of the scale when rating samples, which is

typically found in descriptive analysis panels. For all attributes, except moutheoating, the

interaction of panelists by sample was also highly significant. Therefore, an adjusted F-

value was calculated to account for the variability of the interaction between panelists

and samples as a source of error. Eleven of the thirteen attributes were still significantly

different across samples after the adjusted F-tesl.

An explanation for the lack of difference in moutheoating rating among the

samples is that each solution contained the same amount of high fructose corn syrup

(MFCS) with a concentration of 20% (wt/vol), which was the dominant ingredient

contributing to the moutheoating of the solutions. Results from Kappes and others (2006)

suggested that the possible mouthfeel detection threshold of MFCS in water falls in the

range of 2.81% to 20.15% (wt/vol). The consistent moutheoating ratings of all the model

113

energy drink solutions suggest that panelists could not detect a moutheoating difference

by the varying levels of functional ingredients.

The mean measured viscosity of the solutions was 2.35±0.06 mPa*s with a range

of 2.24 to 2.51 mPa*s, which was not significantly correlated to any attributes. This

suggests that viscosity measurements were not affected by the levels of functional

ingredients added to the model energy drink, although significant sensory attribute

differences were perceived.

The comparison of mean intensity ratings of the three taurine levels used in our

study did not exhibit a significant difference in ratings of any of the attributes (Table 5.5),

with levels of up to 416 mg/100 mL taurine in solution having no significant effects on

sensory properties. The comparison of mean intensity ratings showed that solutions

containing high levels of caffeine had low ratings for fruity attributes and high ratings for

bitter attributes (Table 5.5). High ginseng and high caffeine levels both increased the

intensity ratings of bitter attributes. It is known that caffeine and ginseng arc two

functional ingredients which have negative sensory characteristics that are difficult to

mask (Backas 2009), which is supported by our findings.

An interesting observation was the horns effect that ginseng and caffeine levels

had on the sweet and fruity attributes (pear, artificial lemon-lime, mango, and pineapple).

The greater the level of ginseng and caffeine in solution, the lower the ratings for the fruit

attributes. Caffeine and ginseng levels may have had a horns effect on the sweet and

fruity attributes due to the bitterness it imparted on the solutions. Results from other

studies have shown that increasing amounts of caffeine reduced the sweetness intensity of

solutions (Pangborn 1960, Calvino and others 1990). Ginseng levels were seen to have a

114

more prominent effect than caffeine levels on the intensity ratings of bitter attributes

(Figures 5.1 and 5.2). A possible explanation for the horns effect is that ginseng is not a

familiar taste for the western consumer, therefore the unfamiliar taste of ginseng may

have played a role in decreasing the perception of the fruit flavors. When unfamiliar

lastes are added to a product, it increases the intensity rating of the taste (Kang and others

2007, Labbe and others 2006). The inclusion of ginseng into the formulation was not

expected with the fruity flavors and may have contributed to the decrease in fruit and

sweet attribute intensity ratings.

The bitter attributes were highly correlated (Table 6.1). The highest correlation

was seen between the fruit bitter and fruit bitter afterfeel attributes (r=0.97, p<0.05) and

the tea bitter and tea bitter afterfeel attributes (r=0.98, p<0.05). The highest negative

correlation was between the sweet and tea bitter and fruit bitter (r=-0.91, p<0.05). The

bitter taste and afterfeel attributes were all highly positively correlated (r=0.95, p<0.05).

This suggests that the bitter attributes might have been measuring the same bitter

perception in the solutions, although in panelist training and term generation, the

panelists determined that there were four distinct bitter attributes identified in the model

energy drink solutions.

Bitter tea has a complex flavor containing both astringent and bitter sensations. In

Drobna's (2004) research, the descriptors for bitter tea included alum for bitterness and

tannic acid for astringency. The main compounds which contribute to the taste and

bitterness in tea are catechins, caffeine, and saponins (Rouseff 1990). Caffeine and

ginseng saponins were present in the energy drink solution and could be the reason

panelists selected the tea reference. During the term generation and reference refinement

115

sessions, a 0.25% caffeine solution was tested as a possible bitter reference. The panel as

a whole did not feel that the bitterness of the caffeine solution could be detected, but

instead the bitterness of brewed black tea was detected in the solutions.

Although there were no fruit flavorings added to the model energy drink

solutions, panelists detected fruity notes in the model energy drink solutions. Fruits are

generally sweet and associated with sweet flavors, which could explain why the pear,

pineapple, and mango attributes were selected. King and others (2007) found that pear

attribute was perceived to be higher in an apple-flavored beverage with a Brix level of

12°B versus an apple-flavored beverage with a Brix level 8°B. The Brix level in the base

model energy drink solution was 12.20°B, which could have contributed to the panelists'

perception of the presence of fruity attributes in the energy drink solutions. Lemon flavor

was found to increase in a beverage solution when the acidity of the solution was

increased (King and others 2007). The citric acid in the base model energy drink

formulation could explain the artificial lemon-lime attributes identified in the solutions.

The low, medium, and high levels of caffeine, ginseng, and taurine provide a

representation of the range of these ingredients in commercially-available energy drinks.

To the researchers' knowledge, there are currently no products on the market which

contain the lowest levels of all of the ingredients or the highest levels of all of the

ingredients. If a product contained high amounts of ginseng (i.e. Red Jak), there was a

moderate level of caffeine in the beverage. Therefore, current commercially-available

beverages may not be as bitter as the model solutions that were studied in this

experiment.

116

Principal component analysis (PCA) on the covariance matrix with varimax

rotation described 65.6% of the variance on Factor 1 and 12.4% on Factor 2 (Figure 5.1).

Factor 1 was defined by the astringent, bitter, and fruity attributes. The astringent, bitter,

and bitter afterfeel attributes were all highly positively correlated, while all negatively

correlated to the artificial lemon-lime, pear, pineapple, and sweet attributes (Table 5.6

and Figure 5.1). Sweet and bitter attributes have been found to be negatively correlated

in Calvino's (1990) research on solutions. Also in Keast's (2008) work, an increased

amount of caffeine in solution decreased the sweetness ratings. The mango and tart

attributes defined Factor 2, which accounted for 12.4% of the variation of data. Factor 2,

however, does show that solutions with lower levels of caffeine and ginseng are located

in the opposite area of the tart attribute, suggesting that there was a higher perception of

tart at higher caffeine and ginseng levels.

Cluster analysis was conducted based on the significant attributes of the 27

solutions, which were clustered into four groups, The clusters were generally

characterized by the different levels of ginseng in solution (Figure 5.2). This suggests

that the high bitter attribute ratings and the low fruit attribute ratings had the most

prominent effect in clustering of solutions, which was mainly caused by the ginseng

level. These findings can be utilized to predict the changes in sensory characteristics

when formulating energy drinks containing these specific functional ingredients.

117

5.5 Conclusions

The more caffeine and ginseng were added to solution, the higher the bitter

attribute ratings of the model energy drink solutions. Determining ways of minimizing

the bitterness in functional beverages will allow manufacturers to produce products that

have more health benefits as well as lower level of objectionable sensory properties.

There was no significant difference in sensory attribute ratings across the taurine levels

(208 to 416 mg/100 mL) added to the model energy drink solutions. The findings from

this study can be used when selecting palatable amounts of caffeine, ginseng, and taurine

to incorporate in an energy drink formulation. The findings can also be utilized to predict

the changes in sensory characteristics when reformulating functional ingredients in

energy drinks.

Future studies may include: 1) determining methods to effectively minimize

ginseng bitterness in energy drinks and 2) identifying acceptable bitterness levels of

energy drinks via a consumer test, which will aid in determining optimal functional

ingredient levels and bitterness masking agents to incorporate into energy drink

formulations.

5.6 References

Backas N. 2009. Brimming Opportunities for Nutraceutical Beverages, Food Product Design [serial online], 19(2):50-68. Available from Posted January 21,2009.

Calvino AM, Garcia-Medina MR, Comelto-Muniz JE. 1990. Interactions in caffeine-sucrose and coffee-sucrose mixtures: evidence of taste and flavor suppression. Chem Senses 15(5):505-19.

Camire ME. 2000. Dietary Supplements. In: M. K. Schmidl, T. P. Labuza, editors. Essentials of Functional Foods. Gaithersburg: Aspen Publishers, Inc. pi 65-180.

Coon JT, Ernst E. 2002. Panax ginseng: a systematic review of adverse effects and drug interactions. Drug Saf 25(5):323-44.

118

Cosgrove J. 2008. Caffeinated Snacks. Nutraceuticals World [serial online], July/August 2008Available from Posted July 2008.

Court WE. 2000a. Ginseng: The Genus Panax. Singapore: CRC Press. 266 p.

Court WE. 2000b. The Pharmacology and Therapeutics of Ginseng. In: Anonymous Ginseng: The Genus Panax. Singapore: Harwood Academic Publishers, pi 17-197.

de Sousa PI-IM, Maia GA, de Azeredo HMC, de Souza Filho MSM, Gamiti DS, de Freitas CAS. 2007. Mixed tropical fruit nectars with added energy components. Int J Food Sci Tech 42(11): 1290-1296.

Delwiche J. 2004. The impact of perceptual interactions on perceived flavor. Food Qual Pref 15(2): 137-46.

Drobna Z, Wismer WV, Goonewardene LA. 2004. Selection of an Astringcncy Reference Standard for the Sensory Evaluation of Black Tea. J Sens Stud 19(2): 119-32.

Food and Drug Administration. 2003. Code of Federal Regulations Title 2 Sec. 182.1180.

Galloway DR, Talanian JL, Shoveller AK, Heigenhauser GJF, Sprict LL, 2008. Seven days of oral taurine supplementation does not increase muscle taurine or alter substrate metabolism during prolonged exercise in humans. J Appl Physiol 105(2):643-51.

Jung MY, Jeon BS, Bock JY, 2002. Free, esterified, and insoluble-bound phenolic acids in white and red Korean ginsengs (Panax ginseng CA Meyer). Food Chem 79(1): 105-11.

Kang MW, Chung SJ, Lee I-IS, Kim Y, Kim KO. 2007. The sensory interactions of organic acids and various flavors in ramen soup systems. J Food Sci 72(9):S639-47.

Kappes SM, Schmidt SJ, Lee SY. 2006, Color halo/horns and halo-attribute clumping effects within descriptive analysis of carbonated beverages. J Food Sci 71(8):S590-5.

Keast RSJ. 2008. Modification of the bitterness of caffeine. Food Qual Pref 19(5):465-72.

King BM, Duineveld CAA, Arents P, Meyners M, Schroff SI, Soekhai ST. 2007. Retronasal odor dependence on tastants in profiling studies of beverages, Food Qual Pref 18(2):286-95.

Kitts D, I-Iu C. 2000. Efficacy and safety of ginseng. Public Health Nutr 3(4A):473-85.

Labbe D, Damevin L, Vaccher C, Morgenegg C, Martin N. 2006. Modulation of perceived taste by olfaction in familiar and unfamiliar beverages. Food Qual Pref 17(7-8):582-9.

119

Lourenco R, Camilo ME. 2002. Taurine: a conditionally essential amino acid in humans? An overview in health and disease. Nutr Hosp 17(6):262-70.

Luckow T, Delahunty C. 2004. Consumer acceptance of orange juice containing functional ingredients. Food Res Int 37(8):805-14.

Macfie HJ, Bratchell N, Greenhoff K, Vallis LV. 2007. Designs to Balance the Effect of Order of Presentation and First-Order Carryover Effects in Hall Tests. J Sens Stud 4(2):129-148.

Mintel. 2008. Functional Beverages-US August 2008. Mintel Reports.

Pangborn RM. 1960. Taste Interrelationships. J Food Sci 25(2):245-56,

Reissig CJ, Strain EC, Griffiths RR. 2009. Caffeinated energy drinks—A growing problem. Drug Alcohol Depend 9(1): 1-10.

Rouseff RL. 1990. Bitterness in Food Products and Beverages. Amsterdam: Elsevier. 356 P-

Smit HJ, Rogers PJ. 2002, Effects of'energy' drinks on mood and mental performance: critical methodology. Food Qual Pref 13(5);317-26.

Spiller MA. 1998. Caffeine. Boca Raton: CRC Press. 363 p.

Vogler BK, Pittler MH, Ernst E. 1999. The efficacy of ginseng. A systematic review of randomized clinical trials. Eur J Clin Pharmacol 55(8):567-75,

Ward JH. 1963. Hierarchical grouping to optimize a quantitative function. J Am Stat Assoc 58(301):236-44.

Williams EJ. 1950, Experimental designs balanced for pairs of residual effects. Australian Journal of Scientific Research 3(3):351-363.

Yatabe Y, Miyakawa S, Ohmori H, Mishima H, Adachi T. 2009. Effects of Taurine Administration on Exercise. In: Anonymous Advances in Experimental Medicine and Biology: Taurine 7. New York: Springer New York. p245-252,

Yu J, Kim AK. 2009. Effect of Taurine on Antioxidant Enzyme System in B16F10 Melanoma Cells. In: Anonymous Taurine 7. New York: Springer. p491.

Zhao L, Kirkmeyer SV, Tepper BJ. 2003. A paper screening test to assess genetic taste sensitivity to 6-n-propylthiouracil. Physiol Behav 78(4-5):625-33.

120

5.7 Tables and Figures

Tabic 5.1: Amount of functional ingredients listed on Nutritional Facts labels of a sampling of popular commereia

Product Name Cocaine Full Throttle IMDX No Fear NOS Red Bull Red Jak Rockstnr Sobe Adrcnnliiic Rush Tab Energy // denotes that ingredient

Serving Size (mL) 248 237 237 237 237 245 237 237 245 310

s were 1

Caffeine (niR) 280 72 47 87 125 80 82 80 79 95

isted on

ly-avai

Taurine (nig) 750 605

it 1000 1000 1000 947 1000 1000 785

able

B2 (mg) 0.0 0.0 0.0 0.0 0.0 0,0 0.0 5.8 0.0 0.0

ener

B3 (mg)

0 6 0 0 0 28 0 20 0 6

gy drinks.

B5 (mg)

0 0 0 0 0 7 0 10 0 0

156 (mg)

6 0 0 2 2 7 5 2 5 1

he Nutritional Facts labels, but the

B12 (I'K) 36.0 0,6 0.0 6,0 6.0 7.2 4.8 6.0 6.0 1.2

Ginseng Extract

(mg) 0

90 0 0 0 0 0 0 0

116

I'annx Ginseng Kxtract (mg)

0 0 U

50 50 0

100 25 25 0

specific amounts were n<

121

Table 5.2: Amount of functional ingredients (caffeine, ginseng, and taurine) in 100 i levels. mL model energy drink so

Solution Name

LCLGLT LCLGMT LCLGHT

LCMGLT LCMGMT LCMGHT

LCHGLT LCMGMT LCHGHT

MCLGLT MCLGMT MCLGHT

MCMGLT MCMGMT

MCMGHT

MCMGLT MCMGMT MCMGHT

HCLGLT MCLGMT MCLGHT

MCMGLT MCMGMT MCMGHT

HCI-IGLT MCMGMT MCMGHT

Caffeine (C)

(iiiR/100mL) LC 21.0 LC 21.0 LC 21.0

LC 21.0 LC 21.0 LC 21.0

LC 21.0 LC 21.0 LC 21.0

MC '63.0 MC 63.0 MC- 63.0

MC 63.0 MC 63.0 MC 63.0

MC 63.0 MC 63.0 MC 63.0

HC 103.4 HC 103.4 HC 103.4

HC 103.4 HC 103.4 HC 103.4

HC 103.4 HC 103.4 HC 103.4

utions, L=low, M=

•

Ginseng (G)

(mg/100 mL) LG 10.5 LG 10.5 LG 10.5

MG 31.5 MG 31.5 MG 31.5

HG 52.5 HG 52.5 HG 52.5

LG 10.5 LG 10.5 LG 10.5

MG 31.5 MG 31.5 MG 31.5

HG 52.5 HG 52.5 HG 52.5

LG 10.5 LG 10.5 LG 10.5

MG 31.5 MG 31.5 MG 31.5

MG 52.5 HG 52.5 HG 52.5

medium, and II=hig

Taurine (T)

(me/100 mL) LT 208.4

MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

LT 208.4 MT 311.8 HT 416.8

122

Table 5.3: Terms, definitions, references, and ratings for scale anchors of the descriptive attributes for the model energy drink solutions.

Term Definition Reference Reference Preparation Rating

(0-15) Aroma-by-Mouth Artificial Lemon-Lime Citrus

Mango

Pineapple

Pear

Taste

Sweet

Tart

Bitter Tea

Fruit Bitter

Mouthfeel / Afterfeel Bitter Tea Afterfeel

Fruit Bitter Afterfeel

Moutheoating

Astringent

The aroma of artificial lemon-lime soda while in the mouth The aroma of diluted Gatorade lemon-lime drink while in the mouth The aroma of diluted mango juice while in the mouth

The aroma of diluted pineapple juice while in the mouth. The aroma of diluted 100% canned pear juice.

Taste of 7% Fructose Solution

The tart and sourness of passion fruit juice.

Bitter Tea is the taste of unsweetened Black Tea while in the mouth

Fruit Bitter is the taste of Naringin Solution while in the mouth

Bitter Tea is the taste of unsweetened Black Tea after it is expectorated.

Fruit Bitter Afterfeel is the taste of Naringin Solution after it is expectorated. The moutheoating of sensation perceived on the teeth, tongue, and sides of the mouth after expectorating the samples

Astringent is a mouthdrying sensation felt on the tongue and sides of the mouth

Decarbonated Sierra Mist lemon-lime soda Diluted Lemon-lime Gatorade

Canned Mango Nectar

Diluted Dole Canned 100% Pineapple Juice Diluted 100% Pear Juice from Canned Pear

Fructose Solution

Passion Fruit Juice

Lipton Black Tea

Naringin Solution

Black Tea

Naringin Solution

HFCS and water solution

Diluted Pomegranate Juice

Decarbonated Sierra Mist

125 mL lemon-lime Gatorade + 125 mL water 200 mL juice+50 mL water

125 mL juice+125 mL water

150 mLjuice+100 mL water

21 g fructose + 300 mL water

125 mL juice+125 mL water

1 Lipton tea bag+100 mL hot water; steeped 5 min. then add 200 mL cold water 0.11 g Naringin + 250 mL water

1 Lipton tea bag+100 mL hot water; steeped 5 min. then add 200mL cold water 0.1 lg Naringin + 250 mL water

30 g Isosweet 55 +300 mL water

8

10

8

10

10

9

10

9

10

9

10

100 mL juice +150 mL water

Table 5.4: Analysis of Variance on 13 descriptive attributes rated for model energy drink solutions. Adjusted F-values with judge x sample interaction as the error term.

Attribute Aroma-by-Mouth Artificial Lemon-Lime Citrus Mango Pineapple Pear Taste Sweet Tart Bitter Tea Fruit Bitter Mouthfeel / Afterfeel Bitter Tea Afterfeel Fruit Bitter Afterfeel Moutheoating Astringent

Replication

4.27 2.26 17.56"' 17.70*" 35.35'"

1.12 37.02'" 1.35 1.31

1.73 1.86 1.08 25.58"*

Panelist

33.87'" 43.83'" 77.56"' 57.48*" 100.49*"

67.60"' 75.87"* 51.90"* 40.31"*

71.16"* 63.83"' 91.97"* 87.13"*

Samples (Solutions)

6.92 1.42 5.04*" 3.45"' 5.21"*

11.99"* 3.42"* 16.46*** 21.68*"

22.83"* 28.73"* 1.08 4.60*"

RxP

0.65 1.76 6.8*" 5.28"' 5.40*"

2.2 5.57*" 5.48"* 2.87*"

2.84*" • 3.33*" 2.83"* 6.43"*

RxS

1.36 0.91 1.53* 1.37 1.66'

1.89 1.33 2.04 2.47'"

2.10" 3.01*" 1.3 1.79*

PxS

1.38" 1.40" 1.47"* 1.57"* 1.28*

1.56*" 1.41" 2.49*" 1.95"*

2.16"* 1.80*" 0.94 1.25*

Adjusted F

5.01*** 1.01 3.43*" 2.2*** 4.07***

7.69*** 2.43"* 6.61"* 11.12*"

10.57"* 15.96*"

3.68*" Statistical significance at p<0.05, p<0.01 and RxS=Replication by Sample Interaction; P*S

p<0.001 are denoted by , , and =Panelist by Sample Interaction.

, respectively.RxP= Replication by panelists interaction;

Table 5.5: Mean intensity scores of sensory attributes of varying levels of functional ingredients.

Attribute Sweet

Artificial lemon-

lime Citrus Mango Pineapple Pear Tart

Bitter Bitter Tea Tea Afterfeel

Fruit Fruit Bitter Mouth-Bitter Afterfeel coating Astringent

CAFFEINE LEVEL

High

Medium Low

GINSENG LEVEL High

Medium Low

TAURINE LEVEL High

Medium Low

6.2a

7.0b

7.6C

6.3a

6.9b

l.T

7.0a

6.9a

7.0*

5.3a

5.8b

6.4C

5.6*

5.8a

6.1"

5.9a

5.9a

5.8a

5.1a

5.1a

5.3b

5.r 5.2a

5.3a

5.0a

5.2a

5.3a

3.0a

3.4b

3.7C

3.1a

3.3b

3.7C

3.4a

3.4s

3.3a

3.6a

3.8b

4.1c

3.6a

3.8b

4.1e

3.8a

3.9a

3.9a

3.8a

4.0b

4.3C

3.7a

4.1b

4.4C

5.2a

5.1ab

4.9b

5.4a

5.1b

4.7C

5.0a

4.3b

3.9C

5.3a

4.5b

3.4C

5.0a

4.2b

3.9b.

5.6a

4.5b

3.0C

4.4a

3.5b

3.0C

4.6a

3.6b

2.6C

4.6a

3.6b

3.3C

5.3a

3.8b

2.4°

6.9a

7.0a

7.0a

6.9a

6.9ab

7.1b

4.4a

4.2b

4.2b

4.7a

4.3b

3.7C

4.2b

4.1 ab

5.0a

5.0a

5.2b

4.4a

4.3a

4.5a

4.3a

4.4a

4.4a

3.5a

3.7a

3.7a

3.7a

3.9a

3.9a

6.9a

6.9a

7.0a

4.2' 4.2!

4.4=

Means within a column per treatment (caffeine, ginseng, and taurine levels) with the same superscript are not significantly different (p<0.05, Fisher's Least Significant Difference Test).

Table 5.6: Correlation analysis on significant sensory attributes for 27 combinations of functional ingredients in model energy drink solutions. Bolded values are significant at p<0.05.

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Citrus

Mango

Pineapple

Pear

Tart

Tea Bitter

Bitter Tea Afterfeel

Fruit Bitter

Fruit Bitter Afterfeel

Moutheoating

Astringent Viscosity

1.00

0.91

0.47

0.10

0.86

0.83

-0.28

-0.91

-0.90

-0.91

-0.88

0.39

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0.50

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0.79

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-0.74

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0.13

0.56

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-0.40

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0.27

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--Factor 1 (65.6%)-->

Figure 5.1: Principal component analysis biplot of covariancc matrix of mean scnsorj' attributes of 27 combinations of functional ingredients in model cnergj' drink solntions with varimax rotation. Factor 1 represents 65.6% of the variation and Factor 2 represents 12.4% of the variation. L=Low, M=Medium, II=High, C=Caffcinc, G=Ginscng, and T=Taurinc.

127

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Figure 5.2: Agglomerative hierarchical clustering (AHC) of attribute ratings for 27 combinations of functional ingredients in model energj' drink solutions on the dissimilarity scale by Euclidean distance and agglomeration by Ward's method. The dotted line was computed using the software and truncates the groups based on the largest relative increase in dissimilarity. L=Low, M=Medium, H=High, C=Caffeine, G=Ginseng, and T=Taurine

CHAPTER 6: SENSORY PROPERTIES OF GINSENG SOLUTIONS MODIFIED BY MASKING AGENTS

6.1 Abstract

Ginseng is one of the most popular functional ingredients found in energy drink

formulations. Although ginseng is Icnown for its health benefits, ginseng is also

notorious for imparting a bitter taste. Incorporating ginseng into beverages without the

bitterness, while still maintaining its health benefits, is necessary for developing an

acceptable product. Thus, the objectives of this study were to: 1) identify effective

treatments for minimizing the bitterness of ginseng in water base and model energy drink

base solutions and 2) determine the sensory effects of incorporating different treatment

levels to minimize the bitterness of ginseng.

Based on the results of a series of pilot studies, which investigated bitterness

reducing treatments including congruent flavor addition, bitterness blocking agent

incorporation, enzymatic modification, ingredient interaction, and complexation, y-

cyclodextrins (y-CDs) and |3-cyclodexlrins (P-CDs) complexing agents were identified as

having the most potential. Descriptive analysis was conducted on the effects of the

inclusion of y-CDs, P-CDs and combinations of y-CDs and P-CDs in solutions containing

0,052 g 80% ginsenosides panax ginseng in 100 mL water and in 100 mL model energy

drink base solutions. Twelve trained panelists evaluated 42 solution treatments (3

treatments x 7 levels x 2 bases) for bitter attributes with and without nose clips. The

most effective treatments were 0.09 g y-CDs in 100 mL of solution and 1.00 g p-CDs in

100 mL solution, which both reduced the bitterness intensity of the solutions by half.

Incorporation of these levels of CDs in water and model energy drink base solutions

containing ginseng will aid in the development of pleasant-tasting funclional beverages.

129

Key Words: Ginseng, Bitterness, Cyclodextrins, Descriptive Analysis

6.2 Introduction

Ginseng and Functional Beverages

Ginseng is one of the most popular functional ingredients consumers seek in

functional beverages (LeClair 2000), and is, thus, being incorporated into numerous

beverage formulations. Ginseng is known for its health benefits, such as aiding in overall

improvement of health (Court 2000b), cognitive function (Coon and Ernst 2002), and

alleviating health conditions, such as diabetes (Vuksan and Sievenpiper 2005). The

active compounds in ginseng are triterpenoid saponin glycosides, which unfortunately are

also responsible for the bitter taste of ginseng (Court 2000a, Reineccius 2004).

A previous energy drink descriptive analysis study showed that panelists rated

bitter attribute intensity high when 0.011 g of ginseng was present in 100 mL of a model

energy drink base solution (Tamamoto and Others 2009 In Submission). Minimizing the

bitterness in food products can enhance palatability and result in a more favorable

product (Reineccius 2004, Lesschaeve and Noble 2005). Attempts have been made to

minimize the bitterness of a ginseng drink using cyclodextrins (Yu 1993), but currently

there is no published research on reducing the bitterness of ginseng in functional

beverages. Determining a treatment regime to minimize the bitterness of ginseng in

solution is beneficial for the creation of acceptable functional beverages.

Bittern ess Min im izers

The addition of congruent or related flavors has been used to reduce unpleasant

tastes. Bitter tastes can be made more acceptable by incorporating favorable flavors such

as coffee, dark chocolate, tea (Reineccius 2004), or citrus (Granato 2002). Bitter tastes

130

have also been reduced by blocking bitter taste receptors on the tongue through the use of

chemicals or flavorings (Katan and Roos 2004). An ingredient interaction in which

amino acids and.peptides, such as taurine reduced the bitter taste in KCl bitter solutions

was reported by Tamura and others (1990). The bitterness of ginseng is associated with a

triterpenoid peptide (Court 2000a), which may be broken down into a sapogenin and

sugar molecules (Huang 1999). Rapidase, an enzyme, breaks down peptide bonds of

bitter compounds during the wine-making process, and has the possibility of breaking

peptide bonds in ginseng.

Another potential bitterness minimizing treatment is the complexation of bitter

molecules using cyclodextrins (CDs), which have been shown to reduce'bitterness in

foods, such as citrus fruits (Konno and others 1982), soy, and coffee (Hamilton and

Heady 1970) and drugs (Szejtli and Szente 2005). Cyclodextrins form inclusion

complexes with the bitter compounds (i.e. naringin, limonin, Ibuprofen), resulting in

reduced bitterness (Szejtli and Szente 2005, Szente and Szejtli 2004). The inclusions are

formed through hydrogen bonding and Van der Waals forces between the bitter

compound and the CDs (Szejtli 1988). The bitter taste is reduced because of the inability

of the complcxed molecules to attach to the taste receptors on the tongue (Szejtli and

Szente 2005). Yu (1993) reported that 5-12% CDs (type not specified in patent)

decreased the bitterness of a ginseng drink. It was also reported in a patent by Lee and

others (2008) that the bitterness of 100 g of ginseng extract in solution could be removed

with the addition of approximately lg of y-CDs. Lee and others (2008) also focused on

increasing the solubility and stability of ginseng extract in solution with the incorporation

ofy-CDs.

131

The objectives of this study were to: 1) identify effective treatments for

minimizing the bitterness of ginseng in water base and model energy drink base solutions

and 2) determine the sensory effects of incorporating different treatment levels to

minimize the bitterness of ginseng.

6.3 Materials and Methods

Preliminary Studies

Pilot Studv 1

A pilot test was conducted to investigate possible treatments to minimize the

bitterness of ginseng in water base and model energy drink base solutions. A solution of

0.052 g 80% ginsenosides panax ginseng (Amax NutraSource, Inc, Eugene, OR) in 100

mL spring waler (Absopure, Plymouth, MI) was the water base solution used for all the

treatments. This level reflects the highest level of ginseng found in a sampling of

commercially-available energy drinks (Tamamoto and others 2009, In Submission).

Solution treatments were all made approximately 24 hours prior to testing and stored in

lidded wide-mouth glass mason jars (14400-67000 Ball®, Alltrista, Munice, IN) in a

commercial grade refrigerator at ~5°C. Samples were poured one hour prior to serving

into 29.6 mL plastic souffle cups (Solo Cup Company, Urbana, IL), labeled with random

3-digit codes. Samples were evaluated by panelists at room temperature (~22°C).

The following five treatments were applied to 100 mL of the water base solution.

A congruent flavor treatment included the addition of 0.001 g of a 80% decanal flavoring

(MCI-Miritz Citrus Ingredients, Warwick, NY), which is a citrus flavor. A bitterness

blocking agent treatment using 0.08 g of a water-soluble natural flavoring called

Resolver® (Wild Flavors, Erlanger, KY), which attaches to bitter taste receptors on the

132

tongue and blocks bitter tastes. The ingredient interaction treatment incorporated 15.16 g

taurine (Nutrabio.com, Inc. Middlesex, NJ) into solution. The enzymatic modification

treatment incorporated 0.2 g of Rapidase AR-2000 (DSM Food Specialties, Delft,

Netherlands) into solution. This solution was made 48 hours prior to serving, and stored

in the refrigerator to allow the enzymes to interact with the ginseng. The complexalion

treatment incorporated the use of 0.090 g food grade y-cyclodextrins, chemical purity

98% (Wacker Fine Chemicals, Adrian, MI).

Eleven untrained panelists participated in the pilot study, Panelists were

presented five treatments in the water base solution and a control of an untreated water

base solution in a randomized order. Panelists were instructed to swish the sample in

their mouth for 10 seconds and then rinse with warm water between samples. Panelists

were then asked to rank the five treatments and the control from least bitter to most bitter

(1 to 6). Data were analyzed using Friedman's Rank Sum Test, followed by a Least

Significant Ranked Difference (LSRD) test to determine the difference among sample

ranks,

Pilot Studv 2

The two treatments with the lowest bitterness ratings from pilot study 1 were

further investigated to determine treatment levels to test in the descriptive analysis study.

Seven 0.03 g incremental levels of y-CDs ranging from 0 to 0.180 g in 100 mL water

base solution and seven 0.200 g incremental levels of Resolver® (bitterness blocking

agent) ranging from 0 to 1.200 g in 100 mL in ginseng solutions were tested. Solutions

were made, stored, and presented using the same methods as in pilot study 1. Fourteen

untrained panelists tasted all fourteen solutions and rated the bitterness intensity of each

133

sample on a 10-point categorical scale (0 to 9). Data were analyzed using Statistical

Analysis Software (SAS) version 9.1 (SAS Institute, Inc., Cary, NC). Analysis of

Variance (ANOVA) tested for significant difference of mean scores of the solutions; this

was followed by Fisher's Least Significant Difference (LSD) to determine the difference

among sample means.

Pilot Studv 3

For pilot study 3, another bitterness minimizing treatment that initially had not

been included in pilot study 1 was tested. P-cyclodextrins (P-CDs), chemical purity

100%, (Wacker Fine Chemicals, Adrian, MI) have been used to mask flavors in foods,

such as fishy and grassy notes (Cravotto and others 2006). p-CD is a smaller ring-shaped

molecule than y-CD and is less expensive ($16.50/kg for p-CDs; $91.007kg for y-CDs).

The addition of p-CDs at seven 0.03 g incremental treatment levels from 0 to 0.180 g P-

CDs in 100 mL water base solution was tested to determine if the use of P-CDs is

effective as a ginseng bitterness minimizer. Solutions were made, stored, and presented

using the same methods as in pilot studies 1 and 2. Ten untrained panelists rated the

bitterness intensity of each sample on a 10-point categorical scale (0 to 9). Data were

analyzed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc., Cary,

NC). Analysis of Variance (ANOVA) tested for significant difference of mean scores of

the solutions; this was followed by Fisher's Least Significant Difference (LSD) to

determine the difference among sample means.

134

Main Studv

Sample Preparation

Base Solutions

Two base solutions were investigated in this study to determine the sensory

effects of the interaction of CDs with only ginseng and then with all the ingredients in a

model energy drink solution. A water base solution composed of 0.052 g of 80%

ginsenosides panax ginseng (Amax NutraSource, Inc, Eugene, OR) in 100 mL spring

water (Absopure, Plymouth, MI). The model energy drink base solution was composed

of 553.23 g spring water (Absopure, Plymouth, MI), 142.50 g high fructose corn syrup

(Isosweet 5500, Tate & Lyle, Decatur, IL), 2.02 g sodium citrate (Tate & Lyle, Decatur,

IL), 1.90 g citric acid (Tate & Lyle, Decatur, IL), and 0.35 g potassium citrate (Tate &

Lyle, Decatur, IL), and 0,052 g 80% ginsenosides panax ginseng (Amax NutraSource,

Inc, Eugene, OR), Non-carbonated "still" solutions were used in this study to simplify

the sensory profile of the base solutions.

Sample Solutions

Twenty-one (3 treatments x 7 levels) solution treatments of y-CDs, P-CDs, and

combinations of varying levels of the two types of CDs (Table 6.1) were added to cither a

water base solution or model energy drink base solution for a total of 42 solution

treatments. The seven combination treatments consisted of combinations of the lowest

non-zero, medium, and high y- and P-CD levels selected from the individual y-CD and p-

CD treatments. For each solution treatment, the levels of CDs used were weighed and

brought up to a volume of 225 mL with the base solution. The solution was mixed for

five minutes with a magnetic stir bar on a stir plate. Approximately 15 mL samples were

135

poured into 29.6 mL plastic souffle cups (Solo Cup Company, Urbana, IL) labeled with

random 3-digit codes, and stored overnight at ~5°C in a commercial grade refrigerator.

Samples were taken out of the refrigerator 30 minutes prior to evaluation, which was

carried out at room temperature (~22°C).

Panel Selection and Screening

The panelist recruitment and selection process consisted of a questionnaire, which

included items concerning demographic information, allergies, smoker status, frequency

of consumption of products containing ginseng, and schedule availability. This was

followed by a test for 6-n-propyl-2-thiouracil (PROP) status, which was determined by

presenting volunteers with pieces of filter paper impregnated with PROP following Zhao

and others (2003) paper disc method. If volunteers could not taste anything on the paper

they were considered a non-taster. If the volunteer could taste a bitter taste, they were

labeled a taster.

A basic tastes test (sour, sweet, bitter, salty) was also used to screen panelists.

The basic taste test consisted of presenting volunteers with 20 mL of basic taste solutions

in 29.6 mL plastic souffle cups (Solo Cup Company, Urbana, IL). Six solutions labeled

A through F (sweet, sour, bitter, water, sally, and sour, respectively) were presented to

volunteers. A 0.70% sucrose (C&H Sugar Company, Inc. Crockett, CA) solution for the

sweet solution, a 0.05% citric acid (Tate & Lyle, Decatur, IL) solution for the sour

solution, a 0.02% caffeine (Fischer Scientific, Fair Lawn, NJ) solution for the bitter

solution, and a 0,10% sodium chloride (Morton®, Chicago, IL) solution for the salty

solution were all prepared with spring water (Absopure, Plymouth, MI). Two sour

solutions were presented to panelists to minimize the chance of blind guessing by the

volunteers.

Initially, thirteen panelists (3 males, 10 females, 18 to 50 years old) were selected

based on non-smoker and positive PROP taster status, and correctly identifying four or

more of the basic taste solutions. Three panelists correctly identified all basic tastes, four

panelists had an 83% accuracy rate, and six of the panelists had a 66% accuracy rate.

Eight panelists responded that they consume products containing ginseng, such as teas,

drinks, and energy drinks. Data were analyzed based on the responses of twelve panelists

(2 males, 10 females, 18 to 50 years of age), since during the panelist training period, one

panelist broke his nose, thus his scores were omitted.

Panelist Training

Panel training consisted of twelve 1-hour sessions, which included evaluating

three complete replications of the 42 solution set (Table 6.1). Initial training sessions

were conducted at a round table setting under incandescent lighting. The first day

included an introduction to descriptive analysis methods and the tasting and rinsing

protocols. During the next two days, panelists tasted the sample solutions, generated

bitter descriptor terms, and defined terms. For each descriptor term generated, panelists

selected a reference that represented the term and definition. The terms and references

determined by the panelists were: quinine bitter, a 0.04% quinine solution, which was

defined as "The taste of a 0.04% quinine solution while in the mouth", and caffeine

bitter aftertaste, a 0.10% caffeine solution, which was defined as "The aftertaste of a

0.10% caffeine solution 5 seconds after it is expectorated". The quinine (Sigma

Chemical Company, St. Louis, MO) had a chemical purity greater or equal to 90%, and

the caffeine (Fischer Scientific, Fair Lawn, NJ) had a chemical purity of 100%.

References were made 20 to 24 hours in advance and stored in lidded 59.2 mL plastic

137

souffle cups (Solo Cup Company, Urbana, IL, 61802) at ~5°C in a commercial grade

refrigerator. Once panelists were familiar with the tasting and rinsing protocols,

panelists were introduced and trained to rate samples with nose clips (Speedo, USA) to

eliminate olfactory stimuli.

Sampling Protocol

The sampling protocol consisted of tasting the entire sample (~15 mL) and

moving it to contact all sides of the tongue and mouth for 10 seconds before rating the

attributes. The caffeine bitter aftertaste attribute was rated 5 seconds after the sample

was expectorated.

Panelists familiarized themselves with the references and reference rating scores

prior to each session, The references for each term were then rated on a 16-point

categorical scale (0 to 15) to generate anchors for each attribute scale. Panelists rated the

solutions for each attribute with group-determined reference anchors. The rinse protocol

determined by the panelists was a vigorous warm water (~52°C) rinse, expectoration,

followed by a second vigorous warm water rinse and then swallowing the water.

Panelists were instructed to follow the rinsing protocol prior to evaluating the first sample

and between samples.

Three practice sessions and six data collection sessions were conducted in

individual sensory booths under incandescent light. Each session consisted of three

rounds of samples; the first round consisted of presenting the two references to the

panelists, followed by a set of seven samples then a two-minute break. Each set of

samples included all seven levels of each treatment. For example, the first set included

seven levels of y-CD in a water base solution. Each set of seven samples was presented

simultaneously in a randomized order to panelists. The panelists were then presented a

138

second set of the same references, then a second set of seven samples, followed by a two-

minute break. This process continued for a total of three rounds per session, with a total

of 21 solutions and 3 sets of references being presented to panelists during each booth

session. All 42 treatments were evaluated in duplicate without nose clips and then with

nose clips. Data were collected by the Compusense five program (version 5.0,

Compusense, Inc. Guelph, Ontario, Canada).

Data Analysis

The descriptive analysis data were analyzed using the Statistical Analysis

Software (SAS) version 9.2 (SAS Institute, Inc., Cary, NC). Analysis of Variance

(ANOVA) tested for significant difference of mean scores of the solutions, panelists and

interactions for each attribute. Agglomerative hierarchical clustering (AHC) by Ward's

method (1963) was carried out on the sensory data to observe groupings among the

solutions using XLStat 7.5.3 (Addinsoft, New York, NY). Clusters were created by

automatic truncation based on the largest relative increase in dissimilarity among

solutions.

6.4 Results and Discussion

Preliminary Studies

Pilot Studv 1

Results showed that there was a significant difference across the bitterness

minimizing treatments at p<0.05 (Table 6.2). y-CDs in the amount of 0.090 g in 100 mL

was ranked as the most effective treatment for reducing bitterness. The Resolver®

treatment was ranked as the second lowest for bitterness with average ranking of 3.09;

however, the Friedman test showed that there was no significant difference between the

139

rankings of this treatment to that of the rapidase and taurine treatments. The control,

rapidase, taurine, and Resolver® treatments all ranked similarly with no statistical

difference shown, while the addition of a citrus flavoring was ranked as being the most

bitter. The results from the pilot study suggested that y-CDs and Resolver have the most

potential to minimize the bitterness of ginseng in solution.

Pilot Study 2

There was a significant difference at p<0.05 among the levels of y-CDs

treatment, which showed a decrease in bitterness intensity ratings with an increase in y-

CD levels (Table 6.3). Therefore, the range of 0 to 0.180 g y-CDs was selected as the

concentration range to test in the descriptive analysis study. There was no significant

difference across the levels of Resolver® at p<0,05, and no apparent trends with

increasing levels of Resolver®. Thus, it was concluded that Resolver® was not an

effective bitterness minimizing agent for ginseng solutions.

The use of Resolver® did not result in the reduction of bitterness in ginseng

solutions; therefore, another pilot study evaluating the effectiveness of P-CD levels was

conducted, as previous research has shown that P-CDs in the concentration of 1%

reduced bitterness in hot drinks, such as coffee and tea (Szente and Szejtli 2004).

Pilot Study 3

There was a significant difference across P-CD levels at p<0.05, with a decrease

in bitterness intensity ratings with an increase in P-CD levels (Table 6,3). The decrease

in bitterness intensity ratings was not as pronounced as the y-CD results from pilot study

2. In addition, the increase in P-CDs concentration showed a general trend of decreasing

bitterness intensity ratings, which suggested that higher p-CD levels would further lower

140

bitterness intensity ratings. Based on these results, higher levels of P-CD (0 to 1,50 g)

were tested, which resulted in the selection of a p-CDs concentration range from 0 to

1.50 g/100 mL solution. After conducting the three pilot studies, the bitterness

minimizing treatments finalized for the main study were y-CDs from 0 to 0.180 g/100

mL solution, and p-CDs from 0 to 1.50 g/100 mL solution,

Main Study

Effectiveness of bitterness minimizing treatments of y-CDs and fi-CDs and their levels

There was a significant difference in panelist ratings, which indicates that

panelists used different parts of the scale when rating samples, which is typically found in

descriptive analysis panels (Table 6.4). There was also a significant difference in the

panelists by samples interaction; therefore, an adjusted F value was calculated to account

for the variability as a source of error. There was a significant difference (pO.OOl)

across sample ratings for both the quinine bitter and caffeine bitter aftertaste attributes.

The quinine bitter and caffeine bitter aftertaste attributes were highly correlated (r=0.91,

p<0.001), which suggests that panelists may have evaluated the same bitter taste in the

solutions although they determined that the two attributes were distinct during the

training sessions.

Samples were rated significantly lower (p<0.0001) in both quinine bitter and

caffeine bitter aftertaste attribute intensities with nose clips compared to without nose

clips. This suggests that the ginseng solutions may contain aromatic compounds that

increase bitterness perception, which demonstrates a halo effect (Lethuaut and others

2005, Muether and Lee 2005, Kappes and others 2006, Wansink and others 2007). In

141

addition, Mojet and others (2005) reported that the use of nose clips resulted in lower

intensity ratings of basic tastants than without the use of nose clips.

In general, the water base solutions had higher bitterness ratings than the

treatments in the model energy drink base solutions. The caffeine bitter aftertaste

attribute rating of ginseng solutions in a water base were rated significantly higher

(pO.OOOl) than the model energy drink base solutions. This was expected because the

model energy drink base solution contained high fructose corn syrup which is sweet and

can help mask bitter compounds (Roy and Roy 1997). Incorporation of sweeteners is one

of the most commonly used methods to mask bitterness in food products (Breslin 1996).

The quinine bitter attribute ratings, however, were not significantly different between the

two base solutions at p<0.05. The results suggest that sweeteners may aid in reducing

bitterness aftertaste rather than the bitter taste while the solution is in the mouth.

Quinine bitter and caffeine bitter aftertaste mean attribute ratings for treatments with y-

CD levels decreased when more y-CDs were incorporated into the solutions (Figure 6.1

and Tables 6.5 to 6.7). The data suggest that a level of 0.090 g y-CDs in 100 mL is an

effective amount of y-CDs necessary to reduce the quinine bitter taste and caffeine bitter

aftertaste in ginseng solutions (Figure 6.1 and Tables 6.6 and 6.7). Levels greater than or

equal to 1.00 g P-CDs in 100 mL solution were needed to significantly reduce the quinine

bitter and caffeine bitter aftertaste intensities of the ginseng in solution (Figure 6.2 and

Tables 6.5 to 6.7). Binello and others (2004) showed that P-CD concentrations greater

than 1.2% impart a sweet taste in solutions. The higher P-CD concentrations could be

responsible for the reduced quinine bitter and caffeine bitter intensity ratings. The results

of the combination treatments showed that there were no synergistic effects of the

142

combined CDs. The results had the same trend as the y- and p-CD treatments, in which

the higher the concentration of CDs, the lower the attribute intensity ratings of the

solutions (Table 6.5).

The effectiveness of CDs as bitterness minimizers in ginseng solutions may be

based on the size of both the CDs and the bitter molecules in ginseng. y-CDs have a

molecular weight of 1296 g/mol and a cavity volume of 427 A, while P-CDs have a

molecular weight of 1134 g/mol and a cavity volume of 262 A (Dodziuk 2006). The

complexation of the bitter compounds by the CDs in ginseng depends on the physical

characteristics such as size, polarity, configuration, and structure of the bitter molecule.

Ginseng is composed of organic acids, sugars, oils, saponins, and other compounds. The

saponins in ginseng include over thirty different ginsenosides (Watson 2003), yet the

exact compounds responsible for the bitter taste have not been identified. Some main

ginsenosides have been identified as the active compounds in ginseng, and may

contribute to the bitter taste in ginseng. These include ginsenosides; RBi, molecular

weight 801.02, Rbi, molecular weight 1109.31, and Rc, molecular weight 947.16 (Giiclii-

Ustundag and Mazza 2007). Although the ginsenosides-have smaller molecular weights

than both y- and P-CDs, the different configuration of the ginsenosides could be the

reason why some may have been less effective in forming a complex with the P-CDs, yet

could complex and fit in the cavity of the larger y-CD molecule.

The chemical interactions between CDs and the compounds in solution, and the

sensory effects of these interactions must also be considered when formulating beverages.

For example, research should be conducted investigating whether the added CDs

complex with only the bitter compounds and not the flavor compounds. Flavorings are

143

often the most expensive ingredient included in a functional beverage formulation.

Understanding the chemistry of the interactions between compounds and CDs will aid in

the selection of the most effective type and amounts of CDs to incorporate into

formulations.

Cluster A nalysis

Cluster analysis was conducted based on the two rated attributes, quinine bitter

and caffeine bitter aftertaste, of the 21 solutions, which were truncated into three groups.

Clusters were generally characterized by levels of both y- and P-CDs (Figure 6.3).

Solutions containing none or the lowest tested level of y-CDs (0.030 g/100 mL) and P~

CDs (0.250 g/100 mL) were clustered together and were rated high in quinine bitter

intensity. A second cluster grouped ginseng solutions incorporating y-CD levels ranging

from 0.030 to 0.060 g and p-CD levels ranging from 0.250 to 0.500 g. All other solution

treatments were clustered together; possibly due to the higher levels of P-CD and y-CD

present in these treatments. This suggests that ginseng solutions containing more than

0.750 g p-CDs/100 mL had similar quinine bitter and caffeine bitter aftertaste intensity

ratings as treatments containing more than 0.090 g y-CDs/100 mL of solution. The

clustering results also suggest that the Combo 7 solution treatment (0.030 y-CDs g/100

mL and 0.875 P-CDs g/100 mL) had similar quinine bitter and caffeine bitter aftertaste

intensity ratings as both the 0.75 g p-CDs treatment and the 0.090 g y-CDs treatment.

These findings may be utilized to formulate ginseng-containing energy drinks with

minimal bitterness.

144

Cost Analysis

Although p-CDs cost five-fold less than y-CDs by weight, less y-CDs are required

to comparably reduce the bitterness of ginseng in solution. y-CDs in the amount of 0.090

g in 100 mL of solution is the most cost effective level of y-CDs treatment at $0.08/L of

solution. The most cost effective level of p-CDs was 1.00 g, which would cost $0.17/L

of solution. This is the level needed to minimize the quinine bitterness to the same

intensity as using 0.090 g y-CDs. It also costs $0.17/L of solution to utilize Combo 7

solution treatment (0.030 y-CDs g/100 mL and 0.875 P-CDs g/100 mL) to reduce the

quinine bitter and caffeine bitter aftertaste attribute intensity levels comparable to the

intensity as the 0.090 g y-CDs treatment. Knowing the acceptable level of bitterness in a

functional beverage would aid in optimizing the amount of cyclodextrins necessary to

produce a less bitter tasting and more appealing product. This can be done by conducting

a consumer acceptability test on a set of model energy drink solutions containing varying

levels of CDs.

6.5 Conclusions

Energy drinks have many functional ingredients incorporated in the formulation

to provide energy and other benefits. These drinks often have medicinal tastes, due to

functional ingredients, such as the bitter perceptions of ginseng, Treatments to minimize

the bitterness of ginseng in solution are important in the creation of more palatable and

acceptable products. The challenge of incorporating ginseng into food products is

retaining the healthful properties of ginseng, while minimizing the bitter tastes.

145

The results of this study suggest that 0.090 g y-CDs significantly reduces the

bitter taste and aftertaste of ginseng in 0.052 g ginseng/100 mL solution. Consumers,

however, may accept higher levels of bitterness intensity in energy drinks through

familiarity of the taste associated with these drinks, and therefore, less bitterness

minimizers may be necessary in formulations. Future research should include conducting

a consumer acceptance test on a ginseng model energy drink base solution incorporating

the different types and levels of CDs to determine the bitterness level that is acceptable to

consumers. This research will be useful in selecting the minimum amount of CDs

necessary to produce an acceptable energy drink.-

Other future studies include investigating the chemistry behind the complexations

occurring between the bitter compounds in ginseng and CDs. The study could be

directed at studying the size and other physical properties of the bitter compounds.

Research focusing on the chemical interactions between CDs and bitter compounds can

then be compared and correlated to sensory studies on bitterness perception.

6.6 References

Binello A, Cravotto G, Nano GM, Spagliardi P. 2004. Synthesis of chitosan-cyclodextrin adducls and evaluation of their bitter-masking properties. Flavour Fragrance J 19(5):394-400.

Breslin PAS. 1996, Interactions among salty, sour and bitter compounds. Trends Food SciTechnol7(12):390-9.

Coon JT, Ernst E. 2002. Panax ginseng: a systematic review of adverse effects and drug interactions. Drug Saf 25(5);323-44.

Court WE. 2000a. Ginseng: The Genus Panax. Singapore: CRC Press. 266 p.

Court WE. 2000b. The Pharmacology and Therapeutics of Ginseng. In: Anonymous Ginseng: The Genus Panax. Singapore: Harwood Academic Publishers, pi 17-197.

146

Cravotto G, Binello A, Baranelli E, Carraro P, Trotta F. 2006. Cyclodextrins as Food • Additives and in Food Processing. Current Nutrition & Food Science 2(4):343-50.

Dodziuk I-I. 2006. Cyclodextrins and their complexes: chemistry, analytical methods, applications. Wcinheim: Wiley-VCH. 489 p.

Granato I-I. 2002. Masking Agents Maximize Functional Foods' Potential. Natural Products Insider [serial online]. Available from Posted 14 January 2002 2002.

Giiclu-Usliindag 6, Mazza G. 2007. Saponins: Properties, Applications and Processing. Crit Rev Food Sci Nutr 47(3):231-58.

Hamilton RM, Heady RE, inventors; 1970. Eliminating Undesirable Taste From Coffee And Tea Extracts And Products. U.S. patent United States Patent 3528819.

Huang KC. 1999. The Pharmacology of Chinese Herbs. Boca Raton: CRC Press. 512 p.

Kappes SM, Schmidt SJ, Lee SY. 2006. Color halo/horns and halo-attribute dumping effects within descriptive analysis of carbonated beverages. J Food Sci 71(8):S590-5.

Katan MB, Roos NM. 2004. Promises and Problems of Functional Foods. Crit Rev Food Sci Nutr 44(5):369-77.

Konno A, Misaki M, Toda J, Wada T, Yasumatsu K. 1982. Bitterness Reduction of Naringin and Limonin by P-Cyclodextrin. Agric Biol Chem 46(9):2203-8.

LeClair K. 2000. Breaking the Sensory Barrier for Functional Foods. Food Product Design [serial online]. 6 November 2006. Available from http://www.foodproductdesign.com/articles/462/462 0297DE.html. Posted 1 September 2000.

Lee SK, Yu HJ, Cho NS, Park JI-I, Kim TH, Abdi H, Kim KM, Lee SK, inventors; October 2008. A Method for Preparing the Inclusion Complex of Ginseng Extract with Gamma-Cyclodextrin, and the Composition Comprising the Same. U.S. patent WO/2008/127063.

Lesschaeve I, Noble AC. 2005. Polyphenols: factors influencing their sensory properties and their effects on food and beverage preferences. Am J Clin Nutr 81(1 Suppl):330S-5S.

Lelhuaut L, Brossard C, Meynier A, Rousseau F, Llamas G, Bousseau B, Genot C. 2005. Sweetness and aroma perceptions in dairy desserts varying in sucrose and aroma levels and in lextural agent. Int Dairy J 15(5):485-93.

Mojet J, Koster EP, Prinz JF. 2005. Do tastants have a smell? Chem Senses 30(1):9-21.

Muether AT, Lee SY. 2005. Halo effect on bitterness and astringency by flavor attributes in soy protein isolate (SPI) model solutions [dissertation]. University of Illinois at Urbana-Champaign.

Reineccius GA. 2004. Flavoring Systems for Functional Foods, In: T. Wilson, N. J, Temple, editors. Beverages in Nutrition and Health. Totowa: Humana Press. p89-97.

Roy G, Roy GM. 1997. Modifying Bitterness: Mechanism, Ingredients, and Applications. CRC Press.

Szejtli J. 1988. Cyclodextrin Technology. Boston: Kluwer Academic Publishers. 450 p.

Szejtli J, Szente L. 2005. Elimination of bitter, disgusting tastes of drugs and foods by cyclodextrins. Eur J Pharm Biopharm 61(3): 115-25.

Szente L, Szejtli J. 2004. Cyclodextrins as food ingredients. Trends Food Sci Technol 15(3-4): 137-42.

Tamamoto,L.C, Schmidt.S.J., Lee S. 2009. Sensory Profile of a Model Energy Drink with Varying Levels of Functional Ingredients-Caffeine, Ginseng, and Taurine. J Food Sci In Submission.

Tamura M, Mori N, Miyoshi T, Koyana S, Kohri I-I, Okai I-I. 1990. Practical Debittering Using Model Peptides and Related Compounds. Agric Biol Chem 54(1):41-51.

Vuksan V, Sievenpiper JL. 2005. Herbal remedies in the management of diabetes: Lessons learned from the study of ginseng. Nutrition, Metabolism and Cardiovascular Diseases 15(3): 149-60.

Wansink B, Payne CR, North J. 2007. Fine as North Dakota wine: Sensory expectations and the intake of companion foods. Physiol Behav 90(5):712-716.

Ward JH. 1963. Hierarchical grouping to optimize a quantitative function. J Am Stat Assoc 58(301):236-44.

Watson DM. 2003. Performance Functional Foods. Woodhead Publishing, Ltd. 300 p.

Yu KK, inventor; 1993. Method for removing bitter taste of Ginseng. Korea Patent 930,005,196 B.

Zhao L, Kirkmeyer SV, Tepper BJ. 2003. A paper screening test to assess genetic taste sensitivity to 6-n-propyllhiouracil. Physiol Behav 78(4-5):625-33.

148

6.7 Tables and Figures

Table 6.1: Solution treatment codes and corresponding levels of y-, [1-CDs, and their combinations in both 100 mL water base and 100 mL model cnergj' drink base solutions

Sample Code yCDl

yCD2

yCD3

yCD4

yCD5

yCD6

yCD7

Cone y-CDs

(g/100 mL) 0

0.030

0.060

0.090

0.120

0.150

0.180

y-CDs Molarity (mol/L)

0

2.31xl0-5

4.63 xlO"5

6.94x10-5

9.25xl0-5

1.16x10-"

1.39X10"1

Cone p-CDs

(g/100 mL) ~

~

~

~

~

~

~

|l-CDs Molarity )(moI/L)

~

~

~

~

~

~

~

PCD1 PCD2

PCD3

PCD4

PCD5

PCD6

PCD7

~ ~

~

~

~

~

~

~ ~

~

~

~

~

~

0 0.250

0.500

0.750

1.000

1.250

1.500

0 2.20x10-"

4,41x10-"

6.61x10-"

8.81x10-"

LlOxlO'3

1.32X10'3

Combo* 1

Combo 2

Combo 3 Combo 4

Combo 5

Combo 6

Combo 7

0.030

0.105

0.180 0.030

0.180

0.105

0.030

2.31 xlO-5

S.lOxlO'5

1.39xl0-5

2.31x10°

1.39xl0-5

S.lOxlO-5

2.31xl0"5

0.250

0.875

1.500 1.500

0.250

0.250

0.875

2.20x10-"

7.71 xlO-4

1.32xl0'3

1.32xl0-J

2.20x10-"

2.20x10-"

7.71X10'4

Combo indicates combination of y- and P-CDs.

149

Tabic 6.2: Bitterness intensity rankings (l=least bitter to 6=most bitter) of the bitterness minimizing treatments incorporated in a 0.0529 g ginscng/100 mL water solution.

Treatment

y-Cyclodextrins

Resolver®

Rapidase

Taurine

Control

Citrus Flavoring

Concentration (g/100 mL)

0.090

0.800

0.200

15.160

0.000

0.001

Mean Ranking"

1.00a

3.09b

3.45bc

4.09 bc

4.64 bc

4.73° Mean rating scores with a superscript letter are not significantly different (p<0,05, Least Significant

Ranked Difference Test).

150

Table 6.3: Mean bitterness intensity rating scores (0 to 9) of bitterness minimizing treatment levels incorporate!:

Treatment (g/lOOmL)

in a 0 Mean Intensity

Rating"

y-Cyclodextrins

0

0.03

0.06

0.09

0.12

0.15

0.18

7.2 r ± 5.79b ±

3.04c ±

1.59c ±

1.64c ±

1.59c ±

2.68'1 ±

1.72

1.76

1.31

0.93

1.08

0.93

1.41

Resolver®

0

0.2

0.4

0.6

0.8

1

1.2

' 6.18a ±

5.46a ±

5.79a ±

4.61a ±

5.79a ±

5.68a ±

6.21° ±

2.11

2.37

2.36

1.94

1.81

2.28

1.72 P-Cyclodextins

0

0.03

0.06

0.09

0.12

0.15

0.18

6.90a ±

6.20ab ±

5.00nbc ±

4.60bc ±

5.50b0 ±

4.50bc ±

5.20° ±

1.60

1.40

2.16

1.58

1.78

2.17

2.10 Mean rating scores within each treatment with a superscript letter are not significantly

different (p<0.05, Least Significant Difference Test).

151

Table 6.4: Analysis of Variance on descriptive attributes rated for ginseng solutions containing varying levels of y- and P-CDs. Adjus ted F-values a wi th j u d g e x sample interact ion as t he e r r o r

Attribute Replication Panelist

Quinine Bitter 0.10 46.44***

Caffeine Bitter Aftertaste 0.14 59.76***

Samples

(Solutions)

106.63***

111.15***

t e rm.

RxP

0.96

0.87

RxS

1.14

1.46*

PxS I 44***

1.61***

Adjusted

F

74.05***

69.04*** Statistical significance at p<0.05, p<0.01 and p<0.001 are denoted by *, " , and "*, respectively. Rxp= Replication by panelists interaction; R*S=Replication by Sample Interaction; PxS=Panelist by Sample Interaction.

Table 6.5: Mean" quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 21 solution treatments combining water base and model energy drink base solutions and with and without nose clips usage data. CDs=CycIodextrins.

Treatment (y-CDs

g/100 mL)

y- l (0)

y-2 (0.03)

7-3 (0.06)

y-4 (0.09)

y-5 (0.12)

y-6 (0.15)

7-7(0.18)

Quinine Bitter

12.91a

11.18b

6.63c

3.99d

3.35e

3.55de

2.79f

Caffeine Bitter

Aftertaste

12.80a

10.53b

6.13c

3.91d

3.44d

3.48d

2.72e

Treatment (p-CDs

g/100 mL)

P-1(0)

P-2 (0.25)

p-3 (0.50)

0-4 (0.75)

P-5 (1.00)

p-6 (1.25)

P-7(1.5)

Quinine Bitter

12.59a

9.58b

7.14c

4.95d

3.83e

3.55e

3.55e

Caffeine Bitter

Aftertaste

12.35a

9.13b

6.89c

4.73d

4.09e

3.59c

3.61"

Treatment (7- and P-CDs

g/100 mL)

Combo 1

Combo 2

Combo 3

Combo 4

Combo 5

Combo 6

Combo 7

Quinine • Bitter

8.54a

3.40bc

3.18c

3.69b

2.97c

3.68b

3.81b

Caffeine Bitter

Aftertaste

7.65a

3.28bcd

3.24^

3.69 te

2.84d

3.27bcd

3.71b

"Means within a column per treatment with the same superscript letter are not significantly different (p<0.05, Fisher's Least Significant Difference Test). * Combo indicates combination of 7- and P-CDs. For 7- and p-CD amounts in the Combination treatments, refer to Table 1.

Table 6.6: Mean quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 7 y-CD solution treatments in water base or model energy drink base, without nose clips and with nose clips usage data. The data are plotted in Figure 6.1. CDs=CycIodextrins.

Treatment

(y-CDs g/100 mL)

Water Base, without nose

clips Water Base,

with nose clips

Model Energy Drink Base,

without nose clips

Model Energy Drink Base,

with nose clips

Quinine Bitterness

7-1(0)

7-2 (0.03)

7-3 (0.06)

7-4 (0.09)

y-5 (0.12)

7-6 (0.15)

7-7 (0.18)

13.54a

11.38b

7.42°

4.33d

3.42d

3.29d

3.08d

± 1.64

±2.30

±3.09

±2.39

±1.91

± 1.78

±2.32

12.54a

1121"

6.92b

3.75c

2.54cd

2.50^

2.00d

± 2.65

± 3.06

± 4.05

± 2.59

± 1.79

± 1.93

± 1.53

12.50a

11.00b

6.46c

4.00d

3.96d

4.58d

3.46d

± 2.64

± 2.75

± 3.46

± 3.31

± 3.01

± 3.09

± 3.09

13.04a

11.13b

5.71c

3.88d

3.50de

3.83e

2.63e

± 1.52

± 2.03

± 2.63

± 2.38

± 2.15

± 2.46

± 2.08

Caffeine Bitter Aftertaste

7-1(0)

y-2 (0.03)

7-3 (0.06)

y-4 (0.09)

7-5 (0.12)

7-6 (0.15)

7-7 (0.18)

13.67a

11.25b

7.08c

4.79d

3.83dc

3.33e

3.08e

±1.31

±1.92

±2.57

±2.28

± 1.93

±1.88

±1.25

13.29a

10.71b

6.67c

3.25d

2.42de

2.17e

1.88e

± 2.01

± 2.68

± 3.62

± 2.19

± 1.61

± 1.66

± 1.92

11.83a

9.88b

6.17c

4.17d

4.50d

4.46d

3.42d

± 2.93

± 3.04

± 3.64

± 3.13

± 3.08

± 3.08

± 2.72

12.42*

10.29b

4.58c

3.42dc

3.00de

3.96cd

2.50ef

± 1.67

± 1.76

± 2.48

± 2.43

± 2.13

± 2.77

± 2.02 Means within a column per treatment with the same superscript letter are not significantly different (p<0.05, Fisher's Least Significant Difference Test).

Table 6.7: Mean" quinine bitter and caffeine bitter aftertaste attribute intensity scores (0 to 15) across all 7 P-CD solution treatments in -water base or model energy drink base, without nose clips and with nose clips usage data. The data are plotted in Figure 6.2. CDs=Cyclodextrins.

Treatment

(p-CDs g/100 mL)

Water Base, without nose clips

Water Base, with nose clips

Model Energy Drink Base,

without nose clips

Model Energy Drink Base,

with nose clips

Quinine Bitterness

P-1 (0)

P-2 (0.25)

p-3 (0.50)

P-4 (0.75)

p-5 (1.00)

P-6 (1.25)

p-7 (1.50)

13.17a

10.58b

8.00°

6.21d

4.58E

3.79e

3.96e

± 1.81

± 2.65

± 3.39

± 2.73 .

± 2.43

± 2.11

± 2.20

12.38*

10.54b

7.3 8C

5.00d

3.42e

3.13e

3.17e

± 2.43

± 3.28

± 3.23

± 2.98

± 2.81

± 2.40

± 2.06

12.63a

8.29b

6.29c

4.00d

3.75d

3.54d

3.96d

^

±

1.84

2.85

± 3.53

±

±

2.78

2.69

± 2.89

± 2.84 •

12.21a

8.92b

6.88c

4.58d

3.58de

3.42d=

3.13e

± 1.96

± 2.32

± 2.46

± 2.38

± 2.26

± 2.89

± 2.54

Caffeine Bitter Aftertaste

P-1 (0)

p-2 (0.25)

p-3 (0.50)

p-4 (0.75)

P-5 (1.00)

p-6 (1.25)

P-7 (1.50)

13.38a

10.25b

8.3 8C

6.29d

5.04de

4.63e

4.58e

± 1.64

± 2.31

± 2.62

± 2.49

± 2.22

± 2.14

± 2.06

12.46a

10.7 lb

7.33c

4.75d

3.33e

3.00e

2.75e

± 2.23

± 2.77

± 2.78

± 2.54

± 2.75

± 2.09

± 2.17

12.25a

8.00b

6.2 l c

3.88d

4.25d

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± 2.84

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"Means within a column per treatment with the same superscript letter are not significantly different (p<0.05, Fisher's Least Significant Difference Test).

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155

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156

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157

CHAPTER 7 - SUMMARY

The significant influx of a wide variety of commercially-available functional

beverages into the market has resulted in a beverage category that is not clearly defined

or understood. The rapid increase in functional beverages has also resulted in the lack of

understanding of the sensory, chemical, and physical effects of functional ingredients in

these products.

Three categorization methods: 1) ingredient inventory, 2) flow behavior

comparison, and 3) two-step sensory sorting were used to categorize fifty commercially-

available functional beverages. The categorization is important in formulating a

successful product based on consumer perception and expectation. Of the three methods,

the two-step sorting method produced the most well-defined categories.

Validation and reproducibility studies were then conducted on the two-step

sensory sorting method. Adjusted Rand Index (ARI) values greater than 0.90 showed

that there was excellent correspondence between the fixed sorts conducted in the

validation study. Six functional beverage categories were generated in the reproducibility

study, with the major difference between the initial sort and the replicated sort being that

the Yogurt Smoothies and Fruit Smoothies categories were combined into one category

encompassing both types of beverages in the replicate. The high ARI values from the

validation study (ARI >0.88) and the similar functional beverage categories generated

through the reproducibility study (ARI >0.77) suggest that the two-step sensory sorting

method can be used to consistently create similar functional beverage categories. This

research suggests that the two-step sorting method is a valid and reproducible method to

relatively quickly categorize a large number (~50) of functional beverages and is useful

158

to the beverage industry for the formulation of acceptable functional beverages. Taste is

a key component in the acceptability of food products and the more information known

about the effects of the inclusion of ingredients into aTood matrix, the belter we can

develop successful products.

The two-step sensory sorting method has the potential as a quick categorization

method that could be useful in product development and marketing. Future research

includes investigating the validity and reproducibility of the two-step sensory sorting

method as a rapid process to categorize large groups of products. One such study would

involve sorting a set of products that fall into well-known, defined categories to

determine if the method accurately categorizes products, It would be interesting to

investigate the effectiveness of the two-step sensory sorting method by using it to

categorize other products such as cereals, candies, and food bars. Another study should

focus only on an oral sensory evaluation sort to determine if the functional beverages

could be categorized by only oral sensations and tastes without the influence of

packaging and prior advertising. The level of sweetness, mouthfeel, or other oral-

sensations may play a significant role in defining and generating functional beverage

categories. Lastly, it would be interesting to conduct the two-step sensory sorting method

on a group of functional beverages including newly introduced products to determine if

new functional beverage categories would be generated. If the two-step sensory sorting

method has the ability to effectively categorize other products, it may be a valuable and

inexpensive tool that aids in the development of product descriptors and understanding

product relationships.

159

To determine the effects of functional ingredients on the sensory properties, we

focused on the "energy drink" category, because it is one of the largest sectors in the

functional beverages market and it was one of the most clearly defined categories

generated from the categorization study. The most common functional ingredients found

in energy drinks are caffeine, ginseng, and taurine. The combinations of the three

functional ingredients were added to a model energy drink solution, and descriptive

analysis (DA) was conducted on 27 combinations (3x3x3 factorial design) of the three

functional ingredients at three concentrations (low, medium, high) to determine the

synergistic effects on the sensory properties of the solutions.

A horns effect was observed as the sweet, artificial lemon-lime, pear, mango, and

pineapple attributes were rated lower in intensity with increased ginseng levels. Taurine

levels of up to 416 mg/100 mL had no significant effect on the sensory attribute ratings of

the model energy drink solutions. The results from the DA research on the effects of

functional ingredients suggested that ginseng contributes predominantly to the bitter

attributes. Therefore, a DA was conducted on 21 ginseng solutions with the use of

masking agents (3 masking agent treatments x 7 levels x 2 bases) to reduce the bitter

attributes contributed by 0.052 g ginseng in 100 mL water base and model energy drink

base solutions. It was found that y- and P-cyclodextrins (CDs) showed the most promise

in reducing the bitterness of ginseng in these solutions. Results showed that 0.09 g y-CD

in 100 mL solution and 1 g P-CD in 100 mL solution both reduced the bitterness intensity

of the solutions by half.

In regards to developing energy drinks containing functional ingredients,

specifically ginseng, future research should include running a consumer acceptance test

160

on a ginseng model energy drink base solution incorporating the different types and

levels of CDs to determine the bitterness level that is acceptable to consumers. Also,

conducting a study on the effect of a specific amount of CDs in solutions containing

varying concentrations of ginseng could be done to determine the degree of effectiveness

CDs have on reducing bitterness in regards to ginseng levels. This research will be useful

in selecting the minimum amount of CDs necessary to produce an acceptable energy

drink.

161

CHAPTER 8 - REFERENCES

Anonymous 2008. U.S. Functional Beverages Market: A Young Market with Growing Popularity. Food Industry News [serial online]. Available from Posted 28 May 2008 2008.

Amendola C, Iannilli I, Restuccia D, Santini I, Vinci G. 2004. Multivariate statistical analysis comparing sport and energy drinks. Innovative Food Science & Emerging Technologies 5(2):263-7.

Anonymous 1975. Minutes of Division Business Meeting. Institute of Food Technologists-Sensory Evaluation Division, IFT.

Abdi I-I, 2007. The RV coefficient and the congruence coefficient. Encyclopedia of measurement and statistics.Thousand Oaks (CA): Sage

Alldrick AJ. 2006. Functional beverages: satisfying consumers' needs. Agro Food Industry Hi Tech 17(4):41.

Allison ANNMA, Chambers E. 2000. The Importance Of Caffeine As A Flavor Component in Beverages. J Sens Stud 15(4):449-57.

Asao M, Iwamura I-I, Akamalsu M, Fujita T. 1987. Quantitative structure-activity relationships of the bitter thresholds of amino acids, peptides, and their derivatives. J Med Chem 30(10): 1873-9.

Astray G, Gonzalez-Barreiro C, Mejuto JC, Rial-Otero R, Simal-Gandara J. 2009. A review on the use of cyclodextrins in foods. Food Hydrocoll 23(7): 1631-40.

Backas N, 2009. Brimming Opportunities for Nutraceutical Beverages. Food Product Design [serial online], 19(2):50-68. Available from Posted January 21, 2009.

Barcenas P, Elortondo FJP, Albisu M. 2004. Projective mapping in sensory analysis of ewes milk cheeses: A study on consumers and trained panel performance. Food Res Int 37(7):723-9.

Berry D. 2009. Powerful Beverage Performers, Food Product Design [serial online], 32-50. Available from Posted 2009.

Berlino M, Lawless HT. 1993. Understanding mouthfeel attributes: a multidimensional scaling approach. J Sens Stud 8(2): 101-14.

Binello A, Cravotto G, Nano GM, Spagliardi P. 2004. Synthesis of chitosan-cyclodextrin adducts and evaluation of their bitter-masking properties, Flavour Fragrance J 19(5):394-400.

162

Borg I, Groenen P. 2003. Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement 40(3):277-80.

Brandt LA. 2001. Flavor Masking: Strategies for Success. Prepared Foods [serial online], (July 2001):Available from Posted 2001. -

Breslin PAS. 1996. Interactions among sally, sour and bitter compounds. Trends Food SciTechnol7(12):390-9.

Burg JC. 1998. High-Energy Beverages. Food Product Design

Calvino AM, Garcia-Medina MR, Cometlo-Muniz JE. 1990. Interactions in caffeine-sucrose and coffee-sucrose mixtures: evidence of taste and flavor suppression. Chem Senses 15(5):505-19.

Camire ME. 2000. Dietary Supplements. In: M. K. Schmidl, T. P. Labuza, editors. Essentials of Functional Foods. Gaithersburg: Aspen Publishers, Inc. pi 65-180.

Cartier R, Rytz A, Lecomte A, Poblete F, Krystlik J, Belin E, Martin N. 2006. Sorting procedure as an alternative to quantitative descriptive analysis to obtain a product sensory map. Food Qual Pref 17(7-8):562-71.

Cook DJ, Hollowood TA, Linforth RST, Taylor AJ. 2003. Oral shear stress predicts flavour perception in viscous solutions. Chem Senses 28(1):11-23.

Coon JT, Ernst E. 2002. Panax ginseng: a systematic review of adverse effects and drug interactions. Drug Saf 25(5):323-44.

Cosgrove J. 2008. Caffeinated Snacks. Nutraceuticals World [serial online]. July/August 2008Available from Posted July 2008.

Court WE. 2000a. Ginseng: The Genus Panax. Singapore: CRC Press. 266 p.

Court WE. 2000b. The Pharmacology and Therapeutics of Ginseng. In: Anonymous Ginseng: The Genus Panax. Singapore: Harwood Academic Publishers, pi 17-197,

Coxon APM. 1999. Sorting Data: Collection and Analysis. Sage Publications Inc.

Cravotto G, Binello A, Baranelli E, Carraro P, Trotta F. 2006. Cyclodextrins as Food Additives and in Food Processing. Current Nutrition & Food Science 2(4):343-50,

de Sousa PHM, Maia GA, de Azeredo HMC, de Souza Filho MSM, Garruti DS, de Freitas CAS. 2007. Mixed tropical fruit nectars with added energy components. Int J Food Sci Tech 42(11):1290-1296.

163

Delarue J, Sieffermann JM, 2004. Sensory mapping using Flash profile. Comparison with a conventional descriptive method for the evaluation of the flavour of fruit dairy products. Food Qual Pref 15(4):383-92.

Delwiche J. 2004. The impact of perceptual interactions on perceived flavor. Food Qual Pref 15(2): 137-46.

Dodziuk I-I. 2006. Cyclodextrins and their complexes: chemistry, analytical methods, applications. Weinheim: Wiley-VCH. 489 p.

Drobna Z, Wismer WV, Goonewardene LA. 2004. Selection of an Aslringency Reference Standard for the Sensory Evaluation of Black Tea, J Sens Stud 19(2): 119-32,

Falahee M, MacRae AW. 1997. Perceptual variation among drinking waters: the reliability of sorting and ranking data for multidimensional scaling. Food Qual Pref 8(5):389-94.

Farrell J, RiesNM, Kachan N, Boon H. 2009. Foods and natural health products: Gaps and ambiguities in the Canadian regulatory regime. Food Policy

Faye P, Bremaud D, Durand Daubin M, Courcoux P, Giboreau A, Nicod H. 2004. Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings. Food Qual Pref 15(7-8):781-91.

Faye P, Bremaud D, Teillet E, Courcoux P, Giboreau A, Nicod H. 2006. An alternative to external preference mapping based on consumer perceptive mapping. Food Qual Pref 17(7-8):604-14.

Food and Drug Administration. 2003. Code of Federal Regulations Title 2 Sec. 182.1180.

Fraley C, Raftery AE. 1998. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal 41(8):578-88.

Frank RA. 2003. Response context affects judgments of flavor components in foods and beverages. Food Quality and Preference 14(2): 139-45.

Galloway DR, Talanian JL, Shoveller AK, Heigenhauser GJF, Spriet LL. 2008. Seven days of oral taurine supplementation does not increase muscle taurine or alter substrate metabolism during prolonged exercise in humans. J Appl Physiol 105(2):643-51.

Gawel R, Oberholster A, Francis IL. 2000. A 'mouth-feel wheel': terminology for communicating the mouth-feel characteristics of red wine. Australian journal of grape and wine research. 6(3):203-07.

164

Giboreau A, Dacremont C, Egoroff C, Guerrand S, Urdapillela I, Candel D, Dubois D. 2007. Defining sensory descriptors: Towards writing guidelines based on terminology. Food Qual Pref 18(2):265-74.

Gnadt T. 2007. Effect of Masking Agents and Lactones on the Aroma and Flavor of Soymilk. [dissertation]. University of Illinois at Urbana-Champaign.

Granato H. 2002. Manipulating Flavor Perception in Functional Products. Natural Products Insider [serial online]. Available from Posted 8 April 2002 2002.

Giiclii-Ustundag O, Mazza G, 2007. Saponins: Properties, Applications and Processing. Crit Rev Food Sci Nutr 47(3):231-58.

Hamilton RM, Heady RE, inventors; 1970. Eliminating Undesirable Taste From Coffee And Tea Extracts And Products. U.S. patent United States Patent 3528819.

Hollowood TA, Linforth RST, Taylor AJ. 2002. The Effect of Viscosity on the Perception of Flavour. Chem Senses 27583-91.

Huang KC. 1999. The Pharmacology of Chinese Herbs. Boca Raton: CRC Press. 512 p.

Hubert L, Arabie P. 1985. Comparing partitions. Journal of classification -2(1): 193-218.

Humphries G. 2007. Nutraceutical Soft Drinks: Innovation in sports, energy, dairy, and functional beverages.

Institute of Food Technologists. 2005. Functional Foods: Opportunities and Challenges.

Jung MY, Jeon BS, Bock JY. 2002. Free, esterified, and insoluble-bound phenolic acids in white and red Korean ginsengs (Panax ginseng CA Meyer). Food Chem 79(1): 105-11,

Kang MW, Chung SJ, Lee MS, Kim Y, Kim KO. 2007. The sensory interactions of organic acids and various flavors in ramen soup systems. J Food Sci 72(9):S639-47.

Kappes SM, Schmidt SJ, Lee SY. 2006. Color halo/horns and halo-attribute dumping effects within descriptive analysis of carbonated beverages. J Food Sci 71(8):S590-5.

Kappes SK, Schmidt SJ, Lee SY, 2006, Mouthfeel Detection Threshold and Instrumental Viscosity of Sucrose and High Fructose Corn Syrup Solutions. J Food Sci 71(9):S597-602.

Kappes SM, Schmidt SJ, Lee SY. 2007. Relationship between Physical Properties and Sensory Attributes of Carbonated Beverages. J Food Sci 72(1):S001-11.

Katan MB, Roos NM. 2004. Promises and Problems of Functional Foods. Crit Rev Food Sci Nutr 44(5):369-77.

165

Keast RSJ. 2008. Modification of the bitterness of caffeine. Food Qual Pref 19(5);465-72.

Keast RSJ, Breslin PAS. 2003. An overview of binary taste-taste interactions. Food Qual Pref 14(2): II1-24.

Kim . 1985. Effects of temperature and sugar addition on the flavor of ginseng tea. Korean Journal of Food Science and Technology. 17(4):304.

King MC, Cliff MA, Hall JW. 1998. Comparison of Projective Mapping and Sorting Data Collection and Multivariate Methodologies for Identification of Similarily-Of-Use Snack Bars. J Sens Stud 13(3):347-58.

King BM, Duineveld CAA, Arents P, Meyners M, Schroff SI, Soekhai ST. 2007. Retronasal odor dependence on tastants in profiling studies of beverages. Food Qual Pref 18(2):286-95.

Kitts D, Hu C. 2000. Efficacy and safety of ginseng, Public Health Nutr 3(4A):473-85.

Konno A, Misaki M, Toda J, Wada T, Yasumatsu K. 1982. Bitterness Reduction of Naringin and Limonin by fi-Cyclodextrin. Agric Biol Chem 46(9):2203-8.

Koutsou GA, Storey DM, Bar A. 1999. Gastrointestinal tolerance of y-cyclodextrin in humans. Food Additives & Contaminants 16(7):313-7.

t .

Kruskal JB, 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29(2): 115-29.

Kruskal JB, Wish M. 1978. Multidimensional scaling Quantitative applications in the Social Sciences. Sage Publications Newbury Park. 93p.

Krzanowski WJ, Marriott FHC, 1995. Multivariate analysis. New York: Halsted Press. 280 p.

Labbe D, Damevin L, Vaccher C, Morgenegg C, Martin N. 2006. Modulation of perceived taste by olfaction in familiar and unfamiliar beverages. Food Qual Pref 17(7-8):582-9.

Lai GG. 2007. Getting Specific with Functional Beverages. Food Technology [serial online], 61(12):Available from Posted 2007.

Lawless HT, Glatter S. 1990, Consistency of multidimensional scaling models derived from odor sorting. J.Sensory Studies 5(2):217-30.

Lawless HT, Sheng N, Knoops SSCP, 1995. Multidimensional scaling of sorting data applied to cheese perception. Food Qual Pref 6(2):91-8.

166

LeClair K. 2000, Breaking the Sensory Barrier for Functional Foods. Food Product Design [serial online], 6 November 2006. Available from http://www.foodproductdesign.com/articles/462/462 0297DE.html, Posted 1 September 2000.

Lee SK, Yu HJ, Clio NS, Park JH, Kim TH, Abdi H, Kim KH, Lee SK, inventors; October 2008. A Method for Preparing the Inclusion Complex of Ginseng Extract with Gamma-Cyclodextrin, and the Composition Comprising the Same. U.S. patent WO/2008/127063.

Lelicvre M, Chollet S, Abdi I-I, Valentin D. 2008. What is the validity of the sorting task for describing beers? A study using trained and untrained assessors. Food Qual Pref

Lesschaeve I, Noble AC. 2005. Polyphenols: factors influencing their sensory properties and their effects on food and beverage preferences. Am J Clin Nutr 81(1 Suppl):330S-5S.

Lelhuaut L, Brossard C, Meynier A, Rousseau F, Llamas G, Bousseau B, Genot C. 2005. Sweetness and aroma perceptions in dairy desserts varying in sucrose and aroma levels and in textural agent. Int Dairy J 15(5)^485-93.

Leveen T. 2007. Functional Beverages: Market Evolution. Natural Products Marketplace [serial online]. Available from Posted 2007.

Ley JP, Blings M, Paetz S, Krammer GE, Bertram HJ, 2006. New bitter-masking compounds: hydroxylated benzoic acid amides of aromatic amines as structural analogues of homoeriodictyol. J Agric Food Chem 54(22):8574-9. •

Ley JP. 2008. Masking Bitter Taste by Molecules. Chemosensory Perception l(l):58-77.

Ley JP, Krammer G, Reinders G, Gatfield IL, Bertram HJ. 2005. Evaluation of bitter masking flavanones from Herba Santa (Eriodictyon californicum (H. & A.) Torr., Hydrophyllaceae). J Agric Food Chem 53(15):6061-6.

Lim J, Lawless HT. 2005. Qualitative differences of divalent salts: multidimensional scaling and cluster analysis. Chem Senses 30(9):719-26.

Loftsson T, Brewster ME. 1996. Pharmaceutical applications of cyclodextrins. 1. Drug solubilization and stabilization. J Pharm Sci 85(10):

Lord JB, 2000, New Product Failure and Success. In: A. L. Brody, J. B. Lord, editors. Developing New Food Products for a Changing Marketplace. Lancaster: Technomic Publishing Co, Inc. p55-86.

Lourenco R, Camilo ME. 2002. Taurine: a conditionally essential amino acid in humans? An overview in health and disease, Nutr Hosp 17(6):262-70.

167

Luckow T, Delahunty C. 2004. Consumer acceptance of orange juice containing functional ingredients. Food Res Int 37(8):805-14,

Macfie HJ, Bratchell N, Greenhoff K, Vallis LV. 2007. Designs to Balance the Effect of Order of Presentation and First-Order Carryover Effects in Hall Tests. J Sens Stud 4(2): 129-148.

MacQueen JB. 1966. Some Methods for Classification and Analysis of Multivariate Observations. Proc. Fifth Berkeley Symp. on Math. Statist, and Prob.281-297.

Mattes RD. 2007. Effects of linoleic acid on sweet, sour, salty, and bitter taste thresholds and intensity ratings of adults. AJP-Gastrointestinal and Liver Physiology 292(5):G1243.

Meilgaard M, Civille GV, Carr BT. 2006. Sensory Evaluation Techniques. CRC Press.

Mervis CB, Rosch E. 1981. Categorization of Natural Objects. Annual Reviews in Psychology 32(1 ):89-l 15.

Minor LJ, Pearson AM, Stine CM. 1966. Chicken Flavor Studies. J Agric Food Chem 14(4):416-9.

Mintel. 2009. Energy Drinks and Energy Shots-US.

Mintel. 2008. Functional Beverages-US August 2008. Mintel Reports.

Mobini S, Elliman TD, Yeomans MR. 2005, Changes in the pleasantness of caffeine-associated flavours consumed at home. Food Qual Pref 16(8):659-66.

Mojet J, Koster EP, Prinz JF. 2005. Do tastants have a smell? Chem Senses 30(1):9-21.

Moreau CP, Markman AB, Lehmann DR. 2001. "What is it?" Categorization flexibility and consumers' responses to really new products. Journal of Consumer Research 27(4):489-98.

Murphy GL, Ross BH. 1994. Predictions from uncertain categorizations. Cogn Psychol 27(2): 148-93.

Muether AT, Lee SY. 2005. Halo effect on bitterness and astringency by flavor attributes in soy protein isolate (SPI) model solutions [dissertation]. University of Illinois at Urbana-Champaign.

Nestrud MA, Lawless HT. 2008. Perceptual mapping of citrus juices using projective mapping and profiling data from culinary professionals and consumers. Food Qual Pref 19(4):431-38.

168

Orth UR, de Marchi R. 2005. Advertising's influence on product experience and purchase intention. Fruit Processing 6:372-76.

Ohki K, Nakamura Y, Takano T. 2004. The Japanese Nutritional Health Beverage Market. In: Anonymous Beverages in Nutrition and Health. Totowa: Humana Press, p377-387.

Packaged Facts, 2009. Functional Foods and Beverages in the U.S. 1-210.

Pages J. 2005. Collection and analysis of perceived product inter-distances using multiple factor analysis: application to the study of 10 white wines from the Loire Valley. Food Qual Pref 16(7):642-9.

Pangborn RM. 1960. Taste Interrelationships. J Food Sci 25(2);245-56.

Perrin L, Symoneaux R, Maitre I, Asselin C, Jourjon F, Pages J. 2008. Comparison of three sensory methods for use with the Napping® procedure: Case often wines from Loire valley. Food Qual Pref 19(1):1-11.

Ramos de Armas R, Gonzalez Diaz I-I, Molina R, Perez Gonzalez M, Uriarte E. 2004. Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides. Bioorg Med Chem 12(18):4815-22,

Rand WM. 1971. Objective criteria for the evaluation1 of clustering methods. Journal of the American Statistical Association 846-50.

Reid AW, Becker CH, REID AW. 1956. The use of cocoa syrups for masking the taste of quinine hydrochloride. J Am Pharm Assoc Am Pharm Assoc 45(3):151-2.

Reineccius GA. 2004. Flavoring Systems for Functional Foods. In: T. Wilson, N. J. Temple, editors, Beverages in Nutrition and Health. Totowa: Humana Press. p89-97.

Reissig CJ, Strain EC, Griffiths RR. 2009. Caffeinated energy drinks—A growing problem. Drug Alcohol Depend 9(1):1-10.

Risvik E, Mcewan JA, Colwill .IS, Rogers R, Lyon DH. 1994. Projective mapping: a tool for sensory analysis and consumer research. Food Qual Pref 5(4):263-9.

Rouseff RL. 1990. Bitterness in Food Products and Beverages. Amsterdam: Elsevier. 356 P-

Roy G, Roy GM. 1997. Modifying Bitterness: Mechanism, Ingredients, and Applications. CRC Press.

Rugg G, McGeorge P. 1997. The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Syst 14(2):80-93.

169

Saint-Eve A, Paci Kora E, Martin N. 2004. Impact of the olfactory quality and chemical complexity of the flavouring agent on the texture of low fat stirred yogurts assessed by three different sensory methodologies. Food Qual Pref 15(7-8):655-68.

Schiffman SS, Reynolds ML, Young FW. 1981. Introduction to Multidimensional Scaling: Theory, Methods, and Applications. New York: Academic Press. 413 p.

Schiffman SS, Sattely-Miller EA, Graham BG, Zervakis J, Butchko HH, Stargel WW. 2003. Effect of repeated presentation on sweetness intensity of binary and ternary mixtures of sweeteners. Chem Senses 28(3):219-29.

Schmidl MK, Labuza TP. 2000. Essentials of Functional Foods. Gaithersburg: Aspen Pub.

Smit HJ, Rogers PJ. 2002. Effects of'energy' drinks on mood and mental performance: critical methodology. Food Qual Pref 13(5):317-26.

Smith DM, Awad AC, Bennink MR, Gill JL. 1995. Cholesterol reduction in liquid egg yolk using P-cyqlodextrin. J Food Sci 60(4):691-4.

Soldo T, Hofmann T. 2005. Application of Hydrophilic Interaction Liquid Chromatography/Comparative Taste Dilution Analysis for Identification of a Bitter Inhibitor by a Combinatorial Approach Based on Maillard Reaction Chemistry. J Agric Food Chem 53(23):9165-71.

Soufflet I, Calonnier M, Dacremont C. 2004. A comparison between industrial experts' and novices' haptic perceptual organization: a tool to identify descriptors of the handle of fabrics. Food Quality and Preference 15(7-8):689-99.

Spiller MA. 1998. Caffeine. Boca Raton: CRC Press. 363 p.

Steinley D. 2004. Properties of the Hubert-Arabie adjusted Rand index. Psychol Methods 9(3):386-96.

Stevens JC. 1997. Detection of very complex taste mixtures: generous integration across constituent compounds. Physiol Behav 62(5): 1137-43.

Stone I-I, Sidel JL, 2004. Sensory Evaluation Practices. Academic Press,

Szejtli J. 1988. Cyclodextrin Technology. Boston: Kluwer Academic Publishers. 450 p.

Szejtli J, Szente L. 2005. Elimination of bitter, disgusting tastes of drugs and foods by cyclodextrins. Eur J Pharm Biopharm 61 (3): 115-25.

Szente L, Szejtli J. 2004. Cyclodextrins as food ingredients. Trends Food Sci Technol 15(3-4): 137-42.

170

Takeuchi T, Naae N, 1992. Stationary phase effect on enantioselectivity of dansyl phenylalanine in microcolumn liquid chromatography with y-cyclodextrin as mobile phase additive. J Chromatogr 595(l-2):121-4.

Tamamoto,L.C, Schmidt,S.J., Lee S. 2009. Sensory Profile of a Model Energy Drink with Varying Levels of Functional Ingredients-Caffeine, Ginseng, and Taurine. J Food Sci In Submission

Tamura M, Mori N, Miyoshi T, Koyana S, Kohri I-I, Okai H. 1990. Practical Debittering Using Model Peptides and Related Compounds. Agric Biol Chem 54(1):41-51.

Tang C, Heymann H. 2002. Multidimensional Sorting, Similarity Scaling and Free-Choice Profiling of Grape Jellies. J Sens Stud 17(6):493-509.

Tarea S, Cuvelier G, Sieffermann JM. 2007. Sensory Evaluation of the Texture of 49 Commercial Apple and Pear Purees. J Food Qual 30(6): 1121-31.

Tuorila H, Cardello AV. 2002. Consumer responses to an off-flavor in juice in the presence of specific health claims. Food Qual Pref 13(7-8):561-9.

Urala N, Lahteenmaki L. 2003, Reasons behind consumers functional food choices. Nutr Food Sci 33(4):148-58.

Viswanathan M, Childers TL. 1999. Understanding How Product Attributes Influence Product Categorization: Development and Validation of Fuzzy Set-Based Measures of Gradedness in Product Categories. J Market Res 36(l):75-94.

Vogler BK, Pittler MH, Ernst E. 1999. The efficacy of ginseng. A systematic review of randomised clinical trials. Eur J Clin Pharmacol 55(8):567-75.

Vuksan V, Sievenpiper JL. 2005. Herbal remedies in the management of diabetes: Lessons learned from the study of ginseng. Nutrition, Metabolism and Cardiovascular Diseases 15(3): 149-60.

Wansink B, Payne CR, North J. 2007. Fine as North Dakota wine: Sensory expectations and the intake of companion foods. Physiol Behav 90(5):712-716.

Ward JH. 1963. Hierarchical grouping to optimize a quantitative function. J Am Stat Assoc 58(301):236-44.

Warskulat U, Flogel U, Jacoby C, Hartwig MG, Thewissen M, Merx MW, Molojavyi A, Heller Stilb B, Schrader J, Haussinger D. 2004. Taurine transporter knockout depletes muscle taurine levels and results in severe skeletal muscle impairment but leaves cardiac function uncompromised. FASEB Journal 18(3):577.

Watson DH, 2003. Performance Functional Foods. Woodhead Publishing, Ltd. 300 p.

171

Williams EJ. 1950, Experimental designs balanced for pairs of residual effects. Australian Journal of Scientific Research 3(3):351-363.

Woolf A, Wright R, Amarasiriwardena C, Bellinger D. 2002. A child with chronic manganese exposure from drinking water. Environ Health Perspecl 110(6):613-6.

Wright R. 2008. Nutraceuticals Coast in the Beverage Market. Nutraceuticals World [serial online]. Available from Posted July 2008.

Yamauchi T, Markman AB. 2000. Inference using categories. Journal of Experimental Psychology Learning Memory and Cognition 26(3):776-95,

Yatabe Y, Miyakawa S, Ohmori H, Mishima H, Adachi T. 2009, Effects of Taurine Administration on Exercise. In: Anonymous Advances in Experimental Medicine and Biology: Taurine 7, New York: Springer New York. p245-252.

Yeomans MR, Mobini S, Chambers L. 2007, Additive effects of flavour-caffeine and flavour-flavour pairings on liking for the smell and flavour of a novel drink. Physiol Behav 92(5):831-9.

Yu J, Kim AK. 2009. Effect of Taurine on Antioxidant Enzyme System in B16F10 Melanoma Cells. In: Anonymous Taurine 7. New York: Springer. p491.

Yu KK, inventor; 1993 Method for removing bitter taste of Ginseng. Korea Patent 930,005,196 B. •

Zhao L, Kirkmeyer SV, Tepper BJ. 2003. A paper screening test to assess genetic taste sensitivity to 6-n-propylthiouracil. Physiol Behav 78(4-5):625-33.

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VISUAL SORT

Name of Group:

Group Cliaractcristics/Similaritics:

Comments:

Beverages in this Group

Name of Group:

Group Characteristics/Similarities:

Comments:

Beverages in this Group

APPENDIX C: TEMPLATE OF FUNCTIONAL BEVERAGE NAMES AND NUMBER CODES STICKERS USED IN THE TWO-STEP SENSORY SORTING METHOD

1 Arizona Pomegranate Green Tea

2 Bolthouse Farms Fruit Smoothie

3 Boost

4 Capri Sun Sport

5 Dannon - Danimals

6 Dannon - Light 'n Fit Smoothie

7 Dannon - Frusion

8 Dasani Flavored Water

9 Elements Energy

10 Ensure Shake

11 Fruit20

12 Full Throttle

13 Fuze "Refresh"

14 Fuze "Slenderize"

15 Fuze Green Tea

16 Gatorade Endurance

17 Gatorade Lemonade

18 Gatorade Original

19 Gatorade Rain

20 Glucerna

21 GoldPeak Iced Tea

22 Honest Tea

23 Lifeway Lowfat Kefir

24 Lipton Original White Tea

25MDX

26 Metromint

27 Minute Maid Fruit Falls

28 Naked Fruit Smoothie

29NOS

30 Pediasure

31 Powerade

32 Powerade - Advance

33 Powerade Option

34 Propel Fitness Water

35 Rockstar

36 Slimfast Optima

37 Snapple White Tea

38Sobe-NoFear

39 Sobe - Power

40 Sobe - Tsunami

41 Sobe Lean Energy Diet Citrus

42 Sobe Life Water

43 Stonyfield Farm Organic Smoothie

44 Sweet Leaf Tea

45 TAB Energy

46 Tazo Iced Tea

47 Trinity Water

48 Whitney's Yo on the i G o

49 Yoplait Go-GURT Smoothie

50 Yoplait Nouriche SiiperSmoothie

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APPENDIX D: TWO-STEP SENSORY SORTING METHOD SAMPLE SORTING SCORECARD

Functional Beverage Categories

Directions: Please read the categories and the characteristics of the categories. Then VISUALLY evaluate the beverages and place each beverage into a category (stickers are provided for this task). You may sample the beverages in any order and may go back and sample the beverages as many times as you would like to,

PLEASE MAKE SURE TO PLACE EACH BEVERAGE INTO A CATEGORY!!!!

Energj' Drinks

Characteristics: Contains stimulants such as caffeine and taurine; provides extra energy

Enhanced Waters

Characteristics: Contains minimal calories, lightly flavored, clear liquid

Nutritional Drinks

Characteristics: Contains many nutrients could serve as a meal-replacement beverage

Smoothies

Characteristics: Contains fermented dairy products; opaque

Sports Drinks

Characteristics: Mineral or vitamin enhanced; does not contain caffeine; labeling targets athletes

Teas

Characteristics: Contains tea or lea extracts

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APPENDIX E: DESCRIPTIVE ANALYSIS RECRUITMENT PRESCREENING QUESTIONNAIRE

Contact Information Name:

Email:

Work Address:

Work Phone/Home Phone:

Are you willing to continue with this study?

Are able to participate on all the scheduled dates?

Questionnaire:

1. Gender (check one):

a Male " p Female

2. Age Group (check one): a Under 21 a 21-30 a 31-40 a 41-50

3. Ethnicity (check one): a White a Black a Hispanic

Yes/No

Yes/No

a 51-60 a 61-70 a 71-80 a over 80

a Asian/Pacific Islander a American Indian/Alaska Native a Other (please describe)

4. Are you currently on a restricted diet? If yes, please explain. Yes/No

5. Do you have any of the following? • Dentures a Diabetes a Food Allergies

a Oral or Gum Disease a Hypoglycemia • Hypertension

6. If you have allergies please list:

177

APPENDIX E (cont.)

7. Are there any foods or beverages that you hate?

8. Do you consume products containing ginseng? Yes / No

If yes, what products do you consume?

9. Do you smoke? Yes / No

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APPENDIX F: DESCRIPTIVE ANALYSIS RECRUITMENT PRESCREENING TASTE IDENTIFICATION TEST

Name: Date:

SOLUTION TESTS Your task is to recognize the basic taste of each sample solution (sweet, salty, sour or bitter). Write in the blank which taste you perceive. When the sample tastes like water mark with a "0". If your recognition is questionable, write a question mark "?". Re-tasting is allowed.

For each sample, take the sample into the mouth in sips and move it around in such a way that it touches all parts of the tongue, Do not swallow the sample; use spit cups. Rinse between samples with spring water.

Sample Codes Basic Taste 347 , 734 562 523 . 923 279 485

PAPER TEST Place the piece of filter paper on your tongue, close your mouth, and wet the paper with saliva for 10 seconds. Do you perceive a taste? If so, what do you taste?

On a scale of 1-10 (ten being the strongest possible taste), circle the number that represents how strong the taste you perceive is (if you didn't perceive anything, leave this blank).

1 Very weak 2 3 4 5 6 7 8 9

10 Very Strong

179

APPENDIX G: INFORMED CONSENT FORM FOR SENSORY EVALUATION STUDIES

INFORMED CONSENT FORM FOR SENSORY EVALUATION PANELISTS

"EFFECTIVE MASKING AGENT TASTE TESTING"

You are invited to participate in a study involving sensory evaluation of ginseng solutions. The goal of this research is to establish the perceived bitter levels of ginseng solutions containing bitterness modifiers. These solutions will be evaluated using a hybrid descriptive analysis method. You will be asked to taste each sample and rank the samples by bitterness intensity. You will also be asked to taste each sample and rate the bitterness intensity of each sample. You are free to withdraw from the study at any time for any reason.

The study will be conducted at Bevier Hall Room # 376 (Sensory lab). We anticipate that there will be two evaluations over a span of two weeks. Each evaluation session will last about 10 minutes. Participation in the study will be voluntary.

Your performance in this study is confidential. Responses are coded to be anonymous and any publications or presentations of the results of the research will only include information about group performance.

You will be able to withdraw at any time during the course of the study. The experimenter(s) also reserve the rights to terminate the study of an individual subject at any time during the course of the whole study.

You are encouraged to ask any questions that you might have about this study whether before, during, or after your participation. However, specific questions about the samples that could influence the outcome of the study will be deferred to the end of the experiment. Questions can be addressed to Dr. Soo-Yeun Lee (217-244-9435, soolee@illinois.edu) or Lauren C. Tamamoto (217-333-9795, lhong3@illinois.edu). You may also contact the IRB Office (217-333-2670, irb@illinois.edu) for any question about the rights of research subjects. If you live outside the local calling area, you may also call collect.

I understand the above information and voluntarily consent to participate in the study described above. I have been offered a copy of this consent form.

D I am 18 years of age or older.

Signature Date

Print Name

180

AUTHOR'S BIOGRAPHY

Lauren Chiemi Tamamoto was born and raised in the sunny and winterless

Honolulu, Hawaii. She completed her Bachelors of Science degree in Food Science and

Human Nutrition at the University of Hawaii at Manoa in 2003. As a Rotary

Ambassadorial Scholar, Lauren attended the University of Queensland in Brisbane,

Australia and obtained her Master of Science degree in Food Studies specializing in

Public Health Nutrition in 2004, Lauren returned to Hawaii to dapple in the kitchen and

earned Certificates of Completion in Culinary Arts and Patisserie at the Culinary Institute

of the Pacific at Kapiolani Community College in 2005. She then ventured off to the

University of Illinois at Urbana-Champaign to pursue her Ph.D in Food Science

specializing in Sensory Science. During her career as a "professional student" Lauren

has been a teaching assistant for Introduction to Food Science and Human Nutrition and

Sensory Evaluation of Foods. She has also competed and been a finalist in the Institute

of Food Technologists Product Development competition and the Almond Board's

Innovation Contest. After graduation, Lauren will continue her career working as a

Research Scientist at Frito-Lay in Piano, Texas.

181