Exploring the Relationships Between Sensory, Consumer ...

Exploring the Relationships Between Sensory, Consumer, Volatile, and

Physicochemical Analyses and Their Impact on Flavor in High-Quality

Apples

by

Jordan R. MacKenzie

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Master of Science

in

Food Science

Guelph, Ontario, Canada

© Jordan R. MacKenzie, May, 2021

ABSTRACT

EXPLORING THE RELATIONSHIPS BETWEEN SENSORY, CONSUMER, VOLATILE, AND

PHYSICOCHEMICAL ANALYSES AND THEIR IMPACT ON FLAVOR

IN HIGH-QUALITY APPLES

Jordan Robert MacKenzie Advisors:

University of Guelph, 2021 Dr. Lisa M. Duizer

Dr. Amy J. Bowen

The purpose of this study was to further the understanding of flavor within apples. The

foundation of this research was based on a previous Apple Sweet Spot model created by Dr. Amy

J. Bowen at the Vineland Research and Innovation Centre. Apples used in the present study were

top performers in this developed model, with this research acting to further differentiate these

highly rated apples to determine which characteristics are driving liking among consumers.

Research was conducted through sensory descriptive analysis, a large-scale consumer evaluation,

and instrumental techniques such as aroma volatile and physicochemical measurements. By

combining these evaluation methods, it allowed for an understanding of sensory descriptors and

unique apple varieties that are liked or disliked by consumers. In addition, correlations were made

to specific volatile compound groups and other instrumental methodologies that will ultimately

serve a role in breeding programs to screen apples based on these desired characteristics.

iii

Acknowledgements

First and foremost, I would like to thank my co-advisors Dr. Amy Bowen and Dr. Lisa

Duizer who I worked most closely with, for their overwhelming support, guidance, life lessons,

and for encouraging me to put my personal needs first. I am grateful for the opportunity to

complete my degree under such caring supervision and I am extremely appreciative of the

patience that you have both shown to me.

I would also like to thank my committee member Dr. Loong-Tak Lim for the continued

support, enthusiasm, and knowledge that you have provided throughout the process, inspiring me

to think critically of project details and allowing me to excel in these areas of research.

To the Consumer Insights group at Vineland, thank you for welcoming me into your team

with open arms. I have learned many great lessons that I hope to carry forward in my career. A

special thank you to all of the sensory panelists for your commitment and enthusiasm expressed

each week. To Amy Blake, thank you for your positive attitude and never failing to greet me

with a smile, as well as encouraging me to step outside of my comfort zone on many occasions

to broaden my horizons and gain a deeper appreciation for sensory and consumer research. To

David Ly, thank you for all of your hard work and long hours preparing for apple panel days,

your positivity, and for always being a great friend through it all. To Jessica Tureček, thank you

for instilling a love for statistics that I was otherwise unaware (and afraid!) of. Lastly, thank you

to my apple brother, Min Sung (Kevin) Kim for the journey, problem solving, and many laughs

we shared along the way.

To Dr. David Liscombe, thank you for your continual support, guidance, and making

science fun! I looked forward to each day I was able to spend in your lab and was genuinely

enthused by the work that you have guided me through. To the rest of the biochemistry team, a

special thank you to Tom Hern, Rosalie Zielinski, and Kevin Hooton for your assistance and

support.

Thank you to Dr. Michelle Edwards for your teachings of statistics and providing office

hours where we were able to work out specific project details. To Dr. Gopi Paliyath, thank you

for your genuine interest in my research project and many teachings of everything there is to

know about apples. I am very grateful to have taken both of your courses.

A very special thank you to my fiancée, Sarah, for keeping me grounded, allowing me to

endlessly practice presentations and bounce ideas off of you, and your continuous love and

encouragement. To my parents Rob and Carrie in Cape Breton, thank you for your continuous

support both inside and outside of school, and always cheering me on each step of the way. To

my (future) in-laws Craig and Sally, thank you for your support and many discussions and taste

tests of apples. To the entire Thurtell family, thank you for turning Ontario into my home away

from home, and for all of the support and encouragement along the way. To my entire medical

team, thank you for making this possible and enabling me to continuously better myself while

putting me in the best position to succeed.

Lastly, to the rest of my family and friends both near and far, thank you all for believing

in me through this journey. I would not have been anywhere near where I am today without such

a network of support that you have all provided for me.

iv

Table of Contents

Abstract............................................................................................................................................ii

Acknowledgements........................................................................................................................iii

List of Tables.................................................................................................................................vii

List of Figures...............................................................................................................................viii

List of Abbreviations......................................................................................................................ix

List of Appendices...........................................................................................................................x

1 General introduction.....................................................................................................................1

2 Literature review...........................................................................................................................4

2.1 Apple breeding and creation of new apple varieties......................................................4

2.1.1 Identifying and maintaining a high-quality apple...........................................5

2.2 Understanding apple flavor............................................................................................8

2.2.1 Taste, aroma, and flavor..................................................................................8

2.2.2 Apple flavor..................................................................................................11

2.3 Evaluation techniques..................................................................................................13

2.3.1 Descriptive sensory evaluation.....................................................................13

2.3.2 Consumer sensory evaluation.......................................................................16

2.3.3 Instrumental analysis....................................................................................18

2.3.3.1 Physicochemical analysis...............................................................18

2.3.3.2 Aroma and flavor measurements...................................................19

2.4 Conclusions and future research..................................................................................21

v

3 Apple Flavor and Its Effect on Sensory Characteristics and Consumer Preference...................22

3.1 Introduction..................................................................................................................23

3.2 Materials and methods.................................................................................................26

3.2.1 Products.........................................................................................................26

3.2.2 Maturity determination and apple handling..................................................28

3.2.3 Trained sensory panel evaluation..................................................................28

3.2.4 Consumer hedonic evaluation.......................................................................31

3.2.5 Statistical analysis.........................................................................................33

3.3 Results..........................................................................................................................35

3.3.1 Descriptive analysis......................................................................................35

3.3.2 Creating a sensory map and formation of apple groupings..........................37

3.3.3 Consumer evaluation....................................................................................41

3.3.4 Defining consumer groups and mapping sensory properties........................41

3.3.5 Generating a preference map........................................................................44

3.3.6 Understanding an ideal apple........................................................................50

3.3.7 Demographics, purchase behavior, and consumption habits........................51

3.3.8 Visual evaluation..........................................................................................52

3.4 Discussion....................................................................................................................55

3.4.1 Understanding taste and flavor profiles of apples.........................................55

3.4.2 Consumer preference and ideal apples.........................................................58

3.4.3 Generation of a preference map....................................................................59

3.5 Conclusions..................................................................................................................61

vi

4 Implementation of Aroma Volatile and Physicochemical Measurement Techniques for the

Determination of Flavor Properties in Apple Fruit........................................................................64

4.1 Introduction..................................................................................................................65

4.2 Materials and methods.................................................................................................67

4.2.1 Products.........................................................................................................68

4.2.2 Maturity determination, handling, and storage.............................................68

4.2.3 Aroma volatile collection and analysis by GC-MS......................................69

4.2.4 Physicochemical evaluation..........................................................................71

4.2.5 Trained sensory panel evaluation..................................................................72

4.2.6 Data organization and statistical analyses.....................................................74

4.3 Results..........................................................................................................................77

4.3.1 Analysis of variance......................................................................................77

4.3.2 Regression analysis.......................................................................................77

4.3.3 Principal component analysis.......................................................................81

4.3.4 Generalized procrustes analysis....................................................................84

4.3.5 Multi-factor analysis.....................................................................................87

4.4 Discussion....................................................................................................................94

4.4.1 Flavor characteristics of volatile organic compounds..................................94

4.4.2 Other instrumental measurements responsible for taste and flavor............102

4.5 Conclusions and future research................................................................................103

5 General conclusions and future research..................................................................................105

References....................................................................................................................................108

Appendices...................................................................................................................................117

vii

List of Tables

Table 2.1 Characteristics associated with apple quality..................................................................6

Table 2.2 Summary of recent studies conducted using DA to describe apples.............................15

Table 3.1 Apple varieties selected for analysis across both years.................................................27

Table 3.2 Basic taste, mouthfeel, and aroma reference tray standards with recipes......................30

Table 3.3 Texture reference tray with weak and intense anchors..................................................31

Table 3.4 Mean intensity scores (0-100) from a 15 cm line scale.................................................36

Table 3.5 Year 1: Summary of correlations for sensory evaluation PCA.....................................38

Table 3.6 Mean liking scores by each consumer group for apples evaluated in Year 1................43

Table 3.7 Year 1: Summary of correlations for consumer evaluation PCA..................................44

Table 3.8 Predicted liking scores for Years 1 and 2......................................................................45

Table 3.9 Estimation of consumer satisfaction..............................................................................47

Table 3.10 A comparison of reach and frequency for apple varieties...........................................53

Table 3.11 List of the characteristics defined by consumers in the visual evaluation...................55

Table 4.1 Volatile organic compound list grouped based on chemical structure..........................69

Table 4.2 Basic taste, mouthfeel, and aroma reference tray standards with recipes......................74

Table 4.3 Statistically significant sensory attributes across volatile groups regression................78

Table 4.4 Year 2: Summary of PCA correlations for sensory attributes and volatile groups........83

Table 4.5 Year 3: Summary of PCA correlations for sensory attributes and volatile groups........84

Table 4.6 Year 2 and Year 3 correlations of sensory, volatile, and physicochemical data...........86

Table 4.7 Summary of MFA results in Years 2 and 3...................................................................88

Table 4.8 Volatile compounds and their established odor/flavor profiles.....................................95

Table 4.9 Summary of sensory attributes strongly correlated to a VOC group...........................100

viii

List of Figures

Figure 2.1 Schematic of aroma perception pathways....................................................................10

Figure 3.1 PCA generated from sensory DA data in Year 1.........................................................39

Figure 3.2 PCA generated from sensory DA data in Year 2.........................................................41

Figure 3.3 Preference map conducted on Year 1 data (Factors 1 and 2).......................................48



Figure 4.1 A MFA representation of Year 2 data (Factors 1 and 2)..............................................90




ix

List of Abbreviations

AHC - Agglomerative hierarchical clustering

ANOVA – Analysis of variance

CA – Controlled atmosphere

CATA – Check-all-that-apply

DA – Descriptive analysis

FA – Factor analysis

GC – Gas chromatography

GPA – Generalized procrustes analysis

GTA – Greater Toronto Area

ISO – International Organization for Standardization

KMO – Kaiser-Meyer-Olkin

MFA – Multi-factor analysis

MS – Mass spectrometry

O – Olfactometry

OAG - Ontario Apple Growers

PCA – Principal component analysis

PLS – Partial least squares

RV – Random-variable

SI – Starch iodine

SSC – Soluble solids content

TA – Titratable acidity

TURF – Total unduplicated reach and frequency

Vineland – Vineland Research and Innovation Centre

VOC – Volatile organic compounds

x

List of Appendices

Appendix 1: Consent to participate in research...........................................................................117

Appendix 2: Consumer evaluation example instructions............................................................118

Appendix 3: Series of questions asked during the consumer evaluation.....................................119

Appendix 4: Consumer evaluation visual preference paper ballot..............................................125

1

1 General introduction

Apples (Malus x domestica) are a staple in the diets of people around the world as they

provide a nutritional snack that can be enjoyed in a wide variety of ways, such as juice, cider,

sauce, pie, or simply as a whole fruit. This statement holds true in Canada, where in 2019, apples

were recognized as the largest marketed fruit produced at 368 thousand tonnes (39.4% of all fruit

production), as well as having the second highest farmgate value (i.e. the market value of a

product after subtracting the sales costs), contributing $240.0 million to the Canadian economy

(Statistics Canada, 2019). In addition to this, the consistent development of new apple varieties is

necessary as consumer expectations are dictating the demand for new and improved apple

varieties to be commercialized (Bowen et al., 2018). This continual push within the industry has

led to an increase of new product development within the Canadian fruit sector, of which 44% of

the growth has been focused on new variety and range extensions between 2015-2019 (Statistics

Canada, 2019).

In general, the breeding of an apple variety from initial cross to eventual establishment

within a market is a very long process and can take a research team anywhere between 15-20

years (Bowen et al., 2018). With the increasing knowledge of consumer attitudes and purchase

behaviors, apple breeders and farmers have begun to base the earliest developmental stages of an

apple on a business-to-consumer approach, as opposed to the traditional business-to-business

approach which focused primarily on increasing yield and improving disease resistance (Tesfaye

et al., 2012). Not only is this approach important for the satisfaction of consumers, but a wide

range of economic implications can be lessened, including the cost of maintaining crops, time-

management of farm employees, opportunity cost of agricultural space, and the replacement of

older heritage apple varieties for more profitable newer varieties.

With consumer appeal leading the shift within the apple industry, it is important for apple

breeders and farmers to understand what is driving consumer liking and to act accordingly when

developing new apple varieties. In apples, the decision-making process of a consumer is based

on product familiarity, past experiences, price, and visual appearance, as consumers are not able

to judge the taste, flavor, and textural experiences until after making their purchasing decision

(Sansavini et al., 2004; Yue and Tong, 2011). Once a purchase decision is made on a novel apple

2

variety, it is at this point that the overall acceptability of an apple variety will be evaluated, thus

dictating future purchase decisions based on this initial consumer experience.

Determination of consumer perception among apples has been established through

previous sensory descriptive analysis (DA) and consumer evaluations. For example, Bowen et al.

(2018) had identified two different groups of consumers, the largest representing 89% of the

tested population, who liked apples with a sweet taste, fresh red apple aroma, crisp and juicy

texture, and a lack of mealiness. Similarly, a second and smaller consumer group, representing

11% of the tested population liked apples with an acidic taste, fresh green apple aroma, crisp and

juicy texture, and a lack of mealiness. Interestingly, many studies have found that taste and

texture attributes play the largest role in the determination of consumer liking. These areas of

study have been extensively researched, with consistent findings that consumers typically like

either sweet or acidic tastes paired with crisp and juicy textures (Daillant-Spinnler et al., 1996;

Symoneaux et al., 2012). Although these taste and texture attributes have proven to be necessary

in the development of a consumer-centric apple, it is believed that flavor is what ultimately

differentiates the top performing apples on the market, and an understanding of flavor attributes

in relation to consumer liking is essential for a new variety to succeed (Yahia, 1994; Song and

Forney, 2007).

Although sensory and consumer evaluation are the most established methods to obtain an

understanding of consumer expectations and quality of an ideal apple, these methods may not

always be feasible due to numerous reasons including time, cost, and product availability.

Therefore, rapid and efficient methods should be taken into consideration while making breeding

selections across thousands of new prospective apple varieties. Previous research has shown that

indicators may be available through instrumental techniques such as aroma volatile and

physicochemical analyses. For example, unique aroma volatile organic compounds (VOCs) or

compound groups have been found to contribute characteristic aromas that can help to classify

the aroma properties of an apple. Additionally, physicochemical properties may serve as

potential indicators due to the related sweetness (i.e. soluble solids content [SSC]), or acidity (i.e.

pH, titratable acidity [TA]) of an apple.

The purpose of this research project was to ultimately identify the key flavor attributes

that are responsible for attraction or detraction of apple varieties among consumers. The

3

hypothesis was that these key flavor attributes are responsible for driving preference and are

dependent on each unique variety. Additionally, it was hypothesized that there will be

instrumental indicators which will serve to identify and connect these flavor attributes to unique

VOC groups within each apple. For the present research, this was carried out through sensory

DA, consumer evaluation, physicochemical analysis (i.e. pH, SSC, TA), and VOC analysis (via

gas chromatography-mass spectrometry [GC-MS]). The objectives of the present study served to

build upon previous information collected from Bowen et al. (2018), who identified an “Apple

Sweet Spot” in which apples within this specific region of an external preference map

represented the interests of the largest identified consumer group. The first phase of the project

was to determine a relationship between sensory and consumer evaluation. This was

accomplished by first determining the flavor attributes associated with different apple varieties

through sensory DA. Then, consumer evaluation and questionnaires were used to determine

which apple varieties consumers preferred and to identify which varieties they classify as their

ideal apple. Finally, these identified flavor attributes would then be used to determine which

attributes contribute to consumer liking. For the second part of the present research, VOCs were

identified, measured, and related to consumer liking. Then, additional instrumental

measurements such as pH, SSC, and TA, were used to provide additional insight into the

variability among taste/flavor perceptions.

4

2 Literature review

2.1 Apple breeding and creation of new apple varieties

Apples are an incredibly complex fruit. Their positive nutritional profile (i.e. high in

vitamins, minerals, dietary fibers, and antioxidants) has contributed to an ever-expanding role

within the global fresh fruit market (Musacchi and Serra, 2018). Between 1975 – 2005, the

number of apples produced internationally grew by 30% in Europe, and 300% in Asia, with

North America, South America, Africa, and Oceania all increasing their production by a modest

amount (Sansavini et al., 2004). These growth trends have continued, with fresh apples now

ranking as the 2nd highest fruit commodity on a global scale, with China producing 37 million

metric tons of apples, followed by the USA (4.11 tons), Turkey (2.89 tons), Poland (2.88 tons),

and India (2.20 tons) as the top five producers (Tsao, 2016).

Originally, apple varieties were naturally bred through open pollination and chance

seedlings (Iwanami, 2011). The earliest known experimentation to challenge the natural breeding

patterns of apples is credited to Thomas Andrew Knight (1759-1835), who deliberately bred

apple varieties via artificial hybridization to rid varieties of disease, thus improving overall fruit

quality and yield (Iwanami, 2011). Throughout history, traditional apple breeding programs were

driven by these same objectives; to develop a long-lasting, disease resistant (e.g. scab, mildew,

fire blight) apple variety with an appealing appearance and sensorial composition (Sansavini et

al., 2004; Iwanami, 2011). These efforts have persisted through modern times, where apple

growers and breeders continue to focus on the development of new technologies to aid in the

breeding, production, and postharvest qualities of apples (Song and Forney, 2007). Although

progress has been made in each of these areas, a balance of these three qualities within a singular

variety has proven difficult, as there are numerous other limitations (e.g. initial investment, field

trials, patents and intellectual property, and lack of consumer familiarity or knowledge) that halts

progress and leads to a weaker response in the commercialization of new varieties (Sansavini et

al., 2004).

One of the most progressive areas of apple research has been in the understanding of

disease resistance (Iwanami, 2011). These developments have led to an increase in the overall

yield of varieties, and as a result, the focus within the apple industry has now primarily shifted to

5

improving the overall quality aspects of new varieties in the eyes of a consumer (Jaeger et al.,

1998; Iwanami, 2011; Tesfaye et al., 2012). This recent push has changed the traditional

business-to-business level approach to a consumer-focused approach. Consumer preference

influences even the earliest stages of apple breeding and product development, in order to create

a high-quality apple that will be commercially successful (Tesfaye et al., 2012).

2.1.1 Identifying and maintaining a high-quality apple

A high-quality apple is crucial for consumer appeal and success on the market. Many

different definitions exist for what makes an apple “high-quality” on a global scale, as can be

seen in Table 2.1. Although the perspective of apple quality varies based on where along the

supply chain this term is used, this review will define apple quality in terms of edible/consumer

quality expectations (Musacchi and Serra, 2018). Therefore, for the purpose of this literature

review, apples of high quality will consist of the ideal internal (i.e. taste, texture, aroma/flavor,

nutritional value) and external (i.e. color, shape, size, absence of defects) characteristics as

defined by apple consumers (Musacchi and Serra, 2018).

The challenge with production of a high-quality apple is that apple consumers have

begun to set expectations for apples to exhibit the desired appearance, taste, and texture of an in-

season fresh fruit after months in post-harvest storage (Dixon and Hewett, 2000). For apple

breeders and growers, this presents a very difficult challenge, as the longevity of these quality

characteristics may not always be achievable. For instance, pre-harvest conditions are difficult to

control, as they can be impacted through various environmental, genetic, and agronomical

conditions (Musacchi and Serra, 2018). With this knowledge, apple farmers must consistently

monitor their product to ensure the ideal harvesting time prior to the apple reaching the market.

If the apples are not harvested within their ideal window, a lower quality fruit with less than ideal

internal and external characteristics will reach the market for purchase by consumers (Dixon and

Hewett, 2000; Song and Forney, 2007). Traditionally, pre-mature apples were harvested prior to

reaching their optimal maturity, as they would continue to ripen over the time spent in transit and

storage (Song and Forney, 2007; Musacchi and Serra, 2018). However, with modern technology,

chemical agents are now being introduced to the apple fruit pre-harvest, thus delaying harvesting

windows and allowing for the fruit to mature on the tree and become a higher quality product at

6

the time of harvest. With this knowledge, the best practices for growers to ensure longevity of

quality traits include shipping conditions, storage conditions, and maturity at time of harvest

(Song and Forney, 2007).

Table 2.1 Characteristics associated with apple quality.

Author (Year) Definition of quality in terms of apples

Song and Forney (2007) Appearance, color, texture, flavor, and nutritional value

Kouassi et al. (2008) External appearance, texture, and taste

Iwanami (2011) Crisp, juicy, sweet, and acid (as a sign of freshness)

Galmarini et al. (2012) Texture, visual, and flavor qualities with importance of juicy,

crunchy, and sweet

Tesfaye et al. (2012) Freshness, nutritional value, flavor

Corollaro et al. (2013) Shape, size, color, SSC, TA, penetrometer measurements

Sansavini et al. (2015) Appearance, sensory traits, storability, and shelf-life

Musacchi and Serra (2018) External (color, shape, size, absence of defects) and internal (taste,

texture, aroma, nutritional value, sweetness, acidity, shelf-life, lack of

defects) characteristics

Apples are a climacteric fruit, meaning that, as they mature, a noticeable increase of

ethylene hormone production will occur leading to an increase in respiration and ultimately the

onset of ripening within the fruit (Sung and Forney, 2007; Yang et al., 2013; Muche, 2016;

Musacchi and Serra, 2018). As defined by ISO 7563 (International Organization for

Standardization [ISO], 1998), ripening is a “process of development between physiological

maturity and the state of being ripe when the fruit or vegetable possesses its highest quality”. The

ripening process impacts the size, color, acid/sugar ratio, flavor, and texture of the fruit in a

desirable progression, thus leading to an ideal quality fruit prior to becoming overripe (Corollaro

et al., 2013). As apples continuously ripen, they begin to undergo senescence which will

accelerate the deterioration of the cellular structure (Beaudry and Watkins, 2001). To combat this

process, a number of ethylene inhibitors have been introduced, including 1-methylcyclopropene

(1-MCP), diphenylamine (DPA), aminoethoxyvinylglycine (AVG), and diazocyclopentadiene

(DACP) which all work to either inhibit volatile biosynthesis, or delay the action of ethylene

7

affecting the maturation of the fruit and will therefore extend the shelf-life of the apple (Fan et

al., 1998; Beaudry and Watkins, 2001; Bai et al., 2005; Yang et al., 2013; Muche, 2016).

Of the commercially available ethylene inhibitors, one of the most discussed within the

literature is 1-MCP. This chemical agent works by binding to ethylene receptors and decreasing

the affinity of the receptor to ethylene hormones, thus delaying the effects of ethylene on the

apple (Beaudry and Watkins, 2001; Bai et al., 2005). As a result of this competitive binding, 1-

MCP has been found to delay and decrease respiration due to a lower presence of ethylene, thus

providing better conditions for storage post-harvest including increased firmness, maintenance of

TA and color, as well as a decrease in physiological disorders (Beaudry and Watkins, 2001; Bai

et al., 2005).

Although the use of 1-MCP and other ethylene inhibitors seems like a step in the right

direction for the maintenance of a high-quality apple, there are also downsides to using these

chemical agents. First, with the constant push for natural products free of chemical-use, apples

exposed to 1-MCP have run into export issues, where international markets are wary of fruit

treated with 1-MCP (Mditshwa et al., 2017). Additionally, although 1-MCP is beneficial for the

external qualities of the fruit, it is detrimental to the overall taste and flavor of the apple (Yahia,

1994; Defilippi et al., 2005; Song and Forney, 2007). This is because ethylene production is

responsible for the development of VOCs within the apple (Defilippi et al., 2005). With this

process being delayed, there are also delays in respiration and aroma production (Beaudry and

Watkins, 2001; Defilippi et al., 2005).

Another common method to control post-harvest maturation of apples is controlled

atmosphere (CA) storage, as it enhances the preservation of overall fruit quality (Dixon and

Hewett, 2000). With this approach, the storage climate can be adjusted to maintain low

temperatures, low oxygen levels, and high carbon dioxide concentrations (Dixon and Hewett,

2000; Beaudry and Watkins, 2001). However, if apples are stored in this condition for too long,

the overall flavor and aroma characteristics of the fruit will begin to diminish (Dixon and

Hewett, 2000). Previous research has shown that these flavor and aroma changes become

noticeable to apple consumers after only six months of storage, as the amount of volatile

production decreases by 30-60% when exposed to these conditions (Dixon and Hewett, 2000).

Other changes found in long-term CA storage include a reduction of fatty acids when exposed to

8

low oxygen (Dixon and Hewett, 2000). This will resultantly diminish the number of esters, the

primary compound group contributing to a “fruity” aroma, by decreasing the available biological

precursors for ester production (Dixon and Hewett, 2000).

2.2 Understanding apple flavor

2.2.1 Taste, aroma, and flavor

Taste has developed within mammals through the evolutionary process to serve as a

mechanism in the detection of nutrient quality within a food source, as well as aiding in the

critical avoidance of environmental toxins (Chandrashekar et al., 2000; Huang et al., 2006; Jiang

et al., 2008). To classify as a primary taste, a perception must meet six eligibility criteria: the

taste has an ecological consequence, is generated through distinctive chemicals, acts to activate

specialized receptors, is detected through the gustatory nerves and processed in taste centers, is

unique and does not overlap with other primary tastes, and evokes a behavioral and/or

physiological response (Running et al., 2015). When introduced to the tongue or oral cavity,

tastes can be distinguished through six inherent basic taste modalities: sweet, bitter, umami, sour,

salty, and the recent recognition of oleogustus (Huang et al., 2006; Running et al., 2015; Challis

and Ma, 2016). Although the chemical pathway for all tastes are not fully understood, a clear

understanding of sweet, bitter, and umami exists in that they are regarded as the taste

mechanisms that dictate our acceptance for food (Temussi, 2009). This holds true when diving

deeper into the chemical processes of taste.

To begin, sweet taste serves as a tool to recognize sugars and has evolutionarily

developed as a mechanism to identify a natural source of energy (Temussi, 2009). This

recognition occurs by the binding of sweet compounds to T1R2-T1R3 receptors (Jiang et al.,

2008; Temussi, 2009). Similarly, umami taste has developed to serve as a mechanism to

recognize natural protein sources by identifying potential sources of amino acids and peptides

within food (Temussi, 2009). Umami taste is elicited by T1R1-T1R3 receptors, which are in the

same family of class C G-protein-coupled receptors as the sweet receptors (Temussi, 2009).

Bitter, on the other hand, has served an evolutionary purpose to help mammals detect foods with

toxic compounds, and thus provide a necessary role for the avoidance of foods (Chandrashekar et

al., 2000; Temussi, 2009). The receptors responsible for bitter taste perception include the class

9

A G-protein-coupled receptors, which cover a wide range of T2Rs, all responsible for detecting

numerous bitter or potentially toxic compounds (Chandrashekar et al., 2000; Temussi, 2009).

Salty taste relies on a number of ion channels which react synaptically through a series of action

potentials when introduced to a food stimulus (Mouritsen, 2015; Roper, 2015). Although the

specific channels of interest are not for certain, Roper (2015) identified that epithelial sodium

channels play a large role in the transduction of Na+ which ultimately leads to a salty perception

when stimulated. The least understood of the basic tastes is sour (Ye et al., 2015). Sour taste has

evolutionarily developed to act as a warning signal for acidic food sources that may be spoiled or

unripe and thus presenting a danger to consumption among mammals (Huang et al., 2006).

Huang et al. (2006) have identified the polycystic-kidney-disease-like channel of PKD2L1 as a

potential receptor for sour taste. Most recently, Ye et al. (2015) discussed that a potential

amplification pathway for sour taste may exist via intracellular acidification which serves to

excite the sour taste cells by blocking K+ channels, specifically KIR2.1, which may impact the

physiological sensitivity to sour-inducing chemicals (Ye et al., 2015). The last basic taste,

oleogustus, acts via an oral response to nonesterified, medium-chain and short-chain fatty acids,

aiding the detection of fat among food sources and serves an ecological purpose to ultimately

detect fermented or rancid food products (e.g. nonesterified fatty acids, sourness [short-chain

fatty acids], or irritants [medium-chain fatty acids]) (Running et al., 2015).

Umami, salty, and oleogustus tastes are uncommon in apples, with sweet, acid, and bitter

being the predominant taste characteristics (Passam et al., 2011). Sweetness in apples is due to a

combination of three sugars: sucrose, glucose, and fructose (Yahia 1994). The primary

component responsible for acid perception within apples is malic acid, although citric acid is also

found in smaller quantities (Yahia 1994). Sweet or acidic measurements alone do not serve as

indicators of a high quality apple, as most fruit-breeding programs seek to create a balanced ratio

of the two as they have been found to indicate an increased sweetness perception in consumers

(Diamanti et al., 2011).

Both sweet and acid concentrations can be measured through numerous instrumental

analyses including pH, SSC (measured as °Brix), and TA. However, due to the complexity of

apples and the differences among varieties, these instrumental measurements alone do not

provide adequate information to predict the perceived sweetness or acidity of an apple as

10

perceived by a consumer (Mehinagic et al., 2006). Bitter perception, although not commonly

found to be in high intensities (unless specifically in the apple skin), can be attributed to a higher

phenolic content in the apple (Yahia, 1994). This typically occurs in unripe apples, as the

phenolic content is higher in immature apples and will gradually decline as ripening occurs

(Yahia, 1994).

As defined by ISO 7563 (ISO, 1998), flavor is a term used to describe the combination of

gustatory (taste), olfactory (smell), and trigeminal (tactile and thermal) sensations that are

perceivable. This means that the six basic tastes are combined with aroma sensations as well as

other trigeminal perceptions (e.g. astringency, menthol, capsaicin, carbonation) to generate a

perceivable flavor (Lawless and Heymann, 2010). Aromas are perceived via two primary

systems known as the ortho- and retronasal olfaction systems, as seen in Figure 2.1 (Landis et

al., 2005; Blankenship et al., 2019). Orthonasal aromas are perceived directly from external

sources and are introduced to the olfactory system via inhalation through the nostrils, while

retronasal aromas are internally sourced via the mouth and back of throat when a stimulus is

present within the mouth (Blankenship et al., 2019). These two systems have the capacity to

recognize and differentiate between approximately 10,000 unique aroma sensations (Ulrich and

Olbricht, 2011). Trigeminal sensations act on the trigeminal nerve to produce a chemesthetic

reaction and are induced by a chemical stimulus within the mouth, nose, or eyes (Lawless and

Heymann, 2010).

Figure 2.1 Schematic of aroma perception pathways, detailing the difference between orthonasal

(directly through the nostrils) and retronasal (through the back of the mouth) olfactory systems

(Blankenship et al., 2019).

11

2.2.2 Apple flavor

When it comes to apples, taste and texture parameters are regarded as the predominant

qualifiers of consumer preference (Yahia, 1994). However, according to Song and Forney (2007)

and Yahia (1994), it may be flavor that is the most important factor in determining the overall

quality characteristics of the fruit. A unique flavor composition can help to characterize an apple

variety, allowing it to excel in the commercialization process.

Apples are comprised of more than 300 aroma VOCs, with different combinations of

these VOCs resulting in a variety of flavor perceptions, making apples a very complex natural

product (Yahia, 1994; Dixon and Hewett, 2000; Song and Forney, 2007; Aprea et al., 2012;

Nieuwenhuizen et al., 2013; Ting et al., 2015). These VOCs can be divided into two main

groups: primary and secondary VOCs. Primary VOCs are perceptible from the intact fruit and

are easily identifiable by sniffing the aroma of the apple prior to consuming the product (Yahia,

1994; Song and Forney, 2007). Secondary VOCs are released at the expense of tissue fracture,

whether it be through mastication or cutting the apple open (Yahia, 1994; Song and Forney,

2007). The perception of both VOC classifications is dependent on the chemical concentration

within the fruit, as well as the aroma perception thresholds of the individual person (Song and

Forney, 2007). The development of VOCs is dependent on the maturation cycle of apples, which

varies based on several factors and is specific to the fruit species and cultivar (Dixon and Hewett,

2000). The typical chemical profile of an apple is composed of aldehydes, alcohols, esters,

ketones, carboxylic acids, sesquiterpenoids, and terpenes (Dixon and Hewett, 2000; Song and

Forney, 2007; Aprea et al., 2012; Nieuwenhuizer et al., 2013; Espino-Diaz et al., 2016), with the

majority of these being represented by esters (78-92%) and alcohols (6-16%) (Dixon and Hewett,

2000; Aprea et al., 2012). Alcohols and aldehydes have been found to act as precursors for ester

synthesis as the apple ripens, and therefore also contribute to the overall aroma (Dixon and

Hewett, 2000; Song and Forney, 2007).

Of the approximately 300 recognizable compounds, only about 20 have been identified as

flavor impact compounds which are responsible for characteristic apple aromas (Yahia, 1994;

Dixon and Hewett, 2000; Song and Forney, 2007; Zhu et al., 2020). These impact compounds

can be characterised as a group of compounds, such as acetate esters which are linked to overall

12

apple aroma (Aprea et al., 2012), or as singular compounds, such as hexanal and (E)-2-hexenal

describing green apple-like aromas (Aprea et al., 2012). Aprea et al. (2012) also notes that

acetate esters are linked to pear, banana, and apple aromas, and are represented by a mixture of

the individual compounds butyl acetate, hexyl acetate, amyl acetate, isobutyl acetate, (z)-3-

hexenyl acetate, and butyl propionate. Another group discussed by Aprea et al. (2012) includes

lemon and grapefruit aromas being represented by butanoate esters. As an example from a whole

fruit perspective, a distinctive Fuji apple aroma has been shown to be distinguished by the

character impact compounds of ethyl butanoate, ethyl 2-methylbutanoate, 2-methylbutyl acetate,

ethyl hexanoate, and hexyl acetate (Song and Forney, 2007). Dixon and Hewett (2000) have also

identified specific apple varieties that are characterized by their ester type, including Calville

Blanc and Golden Delicious characterised by acetate esters, Belle de Boskoop, Canada Blanc,

and Richared characterised by butanoate esters, Reinette du Mans, Richared, and Starking being

represented by propanoate esters, and Starking again also being represented by ethanolic esters.

Similarly, Atkinson (2018) identified that phenylpropene VOCs, and specifically estragole,

which are commonly recognized as floral aromas, have been shown to characterise Spartan and

Ellison’s Orange apples with an aniseed-like aroma. Some of these character impact compounds

may be present in low concentrations, but due to their low aroma thresholds, a perceptible aroma

is expressed due to a high aroma intensity and/or aroma quality (Dixon and Hewett, 2000).

As we begin to understand the complexity of flavor, there are also external and

environmental sources that exist which modulate aroma volatiles within the apple. Some of these

include the growing region and climate, fruit maturity, and pre- and post- harvesting processes

(Zhu et al., 2020). This increased complexity of flavor makes it difficult to pinpoint flavor

profiles in natural food products as there are many internal and external variables that influence

the overall development of the fruit, thus leading to a change in chemical composition even

between fruit from the same variety and therefore not allowing a consistently reproducible

product (Hampson et al., 2000).

Unfortunately, apple flavors are not well understood, as the historical focus within the

industry has been to increase yield and disease resistance, then shifting to satisfy the optimal

taste and texture thresholds as defined by consumers, and ultimately neglecting the

understanding of flavor properties. This is partially due to the complexity of flavor, as it is a

13

dynamic experience for a consumer when eating the fruit (Ting et al., 2012). Not only do the

extrinsic and environmental factors play a role in the formation of aroma VOCs, but once the

apple is ready for consumption, there are many physiological factors that will alter the flavor

experience during mastication. These include structure deformation via chewing, thus leading to

an unregulated release of secondary VOCs within the mouth, the mixture with saliva, hydration

levels, and the inherent physiological differences among each unique consumer (Ting et al.,

2012). The consumer experience will also vary due to modulation of aroma VOCs influencing

the taste perception (Aprea et al., 2017). An example of this is reported by Aprea et al. (2017), as

they showed that when measuring sweetness, direct sugar quantification and SSC measurements

are the best indicators, however, aroma VOCs also play a large role in defining the sweetness of

the apple.

2.3 Evaluation techniques

2.3.1 Descriptive sensory evaluation

Descriptive analysis is the most commonly used method for providing a fully

encompassing description of the quantitative and qualitative characteristics of a food product

through the use of a trained panel of expert assessors (Murray et al., 2001; Lawless and

Heymann, 2010; Aprea et al., 2012; Corollaro et al., 2013). Sensory panelists are typically

screened and recruited based on their sensory acuity, and then trained based on the specific

project objectives (Murray et al., 2001). One of the most important segments in training is the

development of a sensory lexicon. This process is accomplished by the panelists being

introduced to a set of products which span any and all expected descriptive terms that they may

come across throughout the product testing stages (Murray et al., 2001; Lawless and Heymann,

2010). Sensory panels will typically use a consensus method to determine which lexical

attributes are most important and act to create a succinct list of these terms for an established

sensory lexicon (Aprea et al., 2012). As outlined by Lawless and Heymann (2010), these terms

should act to discriminate differences among products, be non-redundant and have no relations to

other terms, relate to consumer acceptance/rejection, relate to instrumental or physical

measurements, use a one-dimensional descriptive term, be precise and reliable, achieve

consensus from the sensory panel, be unambiguous, have an easily obtainable reference standard,

14

accurately portray the sensory profile, and they must ultimately relate to reality. A panel leader

will often introduce reference standards to help with panel agreement and with concept

alignment (Murray et al., 2001; Lawless and Heymann, 2010). Lexicons developed for DA will

ultimately measure various tastes/mouthfeels, textures, aromas/flavors, appearances, and sounds

of the product in question through quantitative intensity ratings (Murray et al., 2001; Chambers

IV and Koppel, 2013). By utilizing human assessment, DA provides the most accurate source of

data as the panelists are regularly trained and calibrated to act as an objective measuring unit and

can therefore be used as reference when calibrating instrumental methods (Murray et al., 2001;

Corollaro et al., 2013).

Sensory DA in apples has proven to be successful by highlighting key varieties and

sensory characteristics that are optimal for market success (Cliff et al., 2016). Texture, taste, and

flavor are the most commonly evaluated, with a summary of these attributes found in Table 2.2.

With sensory DA, intensity scores of these attributes can be analyzed to identify commonalities

within the tested varieties. For example, Bowen et al. (2018) identified four product groups

through a clustering analysis. These groups were discriminated based on their sensory profiles,

and included an aromatic-sweet group composed of apples with crisp, juicy, and sweet traits, an

acidic group defined by juicy and crisp textures with an acidic taste, a balanced group which had

no particular attributes standing out with high or low intensities, and a mealy group which was

characterized purely by the texture qualities of high mealiness and low juicy and crisp (Bowen et

al., 2018). Similar to Bowen et al. (2018), Aprea et al. (2012) highlighted five different groups of

apples based on their sensory descriptors; Group 1 included only Granny Smith apples which

were described as being the most herbaceous of the varieties, Group 2 was found to be less

herbaceous with notes of citrus aroma, Group 3 had almost no herbaceous aromas but were

defined as being high in quince, tea, and hay, Group 4 was the well-balanced group in this study,

while apples in Group 5 were found to be the fruity varieties with pear and banana aromas

(Aprea et al., 2012). Jaeger et al. (1998) used sensory DA data to correlate similar and dissimilar

terms to create profiles for ‘fresh’, ‘mid-point’, and ‘mealy’ apples. These results found two

main components which were responsible for discrimination between the sensory attributes, one

being characterised primarily by mealy texture, and the second being characterised by aroma and

flavor differences (Jaeger et al., 1998).

15

Table 2.2 Summary of recent studies conducted using DA to describe apples, including number

of panelists, number of apples, and the sensory attributes.

Author Number

of

panelists

Number

of

apples

Sensory attributes

Aprea et al.

(2012)

n=13 n=18 Almond, apple, banana, concord grape, cooked

apple, grapefruit, kiwi fruit, lemon, melon, Moscato

grape, overripe apple, pear, pineapple, quince, cut

grass, cucumber, hay, pumpkin, tea, tobacco, anise,

cloves, pepper, vanilla, acacia, camomile,

geranium, honey, orange blossoms, rose, violet

Corollaro et al.

(2013)

n=13,

n=14

n=29 Green flesh, yellow flesh, hardness, juiciness,

crunchiness, flouriness, fibrousness, graininess,

sweet taste, sour taste, astringency

Cliff et al.

(2016)

n=10 n=20 Crispness, hardness, juiciness, skin toughness,

astringency, sweetness, tartness, cooked apple

flavor, floral/perfume/spicy flavor, other fruit flavor

Amyotte et al.

(2017)

n=20 n=85 Acid, bitter, sweet, earthy, floral, fresh green apple,

fresh red apple, honey, lemony, oxidized red apple,

astringent, chewy, juicy, mealy, rate of melt, skin

thickness

Aprea et al.

(2017)

n=19 n=17 Sweetness, sourness

Bowen et al.

(2018)

n=10 n=63,

n=76

Oxidized red apple, earthy, hay, honey, floral,

lemony, fresh green apple, fresh red apple, sweet,

acid, bitter, astringent, skin thickness, crisp, juicy,

chewy, mealy, rate of melt

Although DA allows for a thorough understanding of descriptive properties, it also comes

with a downside. Sensory testing methods can be expensive, laborious, and time consuming to

complete, and for this reason, it is important to search for alternative methods to lower the

frequency of using sensory panels (Aprea et al., 2012; Chambers IV and Koppel, 2013; Ting et

al., 2015). These challenges can be overcome by pairing descriptive analysis data with consumer

and instrumental measurements to ultimately generate a prediction model to achieve the purpose

of the research project.

16

2.3.2 Consumer sensory evaluation

Consumer evaluation allows for the understanding of how much consumers like a food

product. Consumer studies are typically housed in a central location with anywhere between 50-

300 participants involved in the study (Daillant-Spinnler et al., 1996; Lawless and Heymann,

2010). Ideally, these panelists are selected based on the fulfillment of an equal representation of

the population and demographic that would be purchasing and/or consuming the product

(Meilgaard et al., 1999; Stone and Sidel, 2004; Hough et al., 2006; Lawless and Heymann,

2010).

Consumer testing is a common approach to help build an understanding of the potential

marketability and success of a product on the market (Lawless and Heymann, 2010). Unlike

sensory DA, the purpose of a consumer evaluation is to determine the degree of liking for a

product rather than providing a full description of the product. As described by Lawless and

Heymann (2010), there are two main approaches in conducting a consumer evaluation: the first

is to identify preference in comparison to another product, and the second is to determine

acceptance, where a consumer is asked to rate their level of liking for a product. Of the two,

acceptance testing is considered the best consumer evaluation practice, as it allows for a liking

rating while also allowing for future interpretations of preference from this data (Lawless and

Heymann, 2010). Difficulties exist in consumer studies, as liking among consumers is not

uniform across a population (Guinard, 2002). Therefore, it is often necessary to divide

consumers into groups, as drivers of liking differ based on segmentation within the market

(Guinard, 2002; Varela, 2014). In addition to this, consumer liking data is one-dimensional, and

allows for consumers to dictate whether or not they like the product but does not allow for them

to describe why this is the case (van Kleef et al. 2006). To combat these difficulties, multivariate

statistics can be used to generate groupings of the tested consumer population based on their

likes and dislikes of the product. Additional data can be collected through a check-all-that-apply

(CATA) questionnaire in which consumers are able to describe a product at the time of tasting,

as well as to highlight traits that they would use to define an ideal product (Dupas de Matos et

al., 2018). Lastly, it is possible to pair this data with sensory DA to create a prediction tool,

known as preference mapping, that can pair liking data with descriptive qualities of the products

(van Kleef et al., 2006).

17

Preference mapping is a tool to generate a product space which shows variables

contributing to consumer preference. This information can be used to optimize a product prior to

commercialization in order to satisfy the desires of a consumer. It is suggested that a minimum

of six products are used to generate a preference map, although Guinard (2002) recommends

implementing a minimum of ten. Internal preference mapping utilizes consumers and products to

create a principal component analysis (PCA) biplot. Due to the large number of datapoints in a

consumer evaluation, consumers are often grouped through a cluster analysis to simplify and

provide more clarity towards the preferences of the tested population (Guinard, 2002; van Kleef

et al., 2006). Similarly, external preference mapping uses a PCA biplot to map consumer

evaluation data and either sensory DA data, or instrumental analysis data (Guinard, 2002; van

Kleef et al., 2006). The sensory/instrumental data will be used to generate the biplot, and the

consumer preference data will then be related to the dimensions of the external preference map.

Clustering is also used for external preference mapping in order to allow the model to show the

most meaningful data (Guinard, 2002). Limitations of preference mapping exist, as the model

will not be able to explain all of the variance within the tested products and will only be able to

map a portion of the overall variance onto a number of meaningful dimensions (Guinard, 2002).

As described by Corollaro et al. (2013), the largest driving factor in consumer

consumption behavior is the eating quality of the fruit. In addition to this, Harker et al. (2003)

and Ting et al. (2015) have stated that texture is the most important in factor in determining fruit

quality. To align with both statements, we can see in several studies (Daillant-Spinnler et

al.,1996; Jaeger 1998; Bonany et al., 2014) that both texture as well as taste are the main drivers

of liking among consumers. Cliff et al. (2016) combined consumer evaluation and sensory DA

data to determine consumer liking based on two different preference maps, one for texture and

one for flavor. In the texture preference map, three groups of consumers were segmented (Cliff

et al., 2016). The largest group of consumers (82%) preferred apples with firm, crisp, hard, and

juicy textures. The second largest group (14%) liked apples that were less firm, crisp, hard, and

juicy while also having an increased astringency and tougher skin. The smallest group of

consumers (4%) preferred apples with a medium intensity of textural qualities while having a

low skin toughness and astringency (Cliff et al., 2016). In the flavor preference map, two

different groups of consumers were clustered (Cliff et al., 2016). Consumers in the first group

18

(88%) liked apples with sweet taste and floral, perfume, and spicy aromas (Cliff et al., 2016). In

the second group (12%), consumers preferred apples with a tart taste and cooked-apple aroma.

Jaeger et al. (1998) also conducted a consumer evaluation to generate an internal preference map.

Results of this study showed that across the two main dimensions, one represented the

differences among flavors and included sweet, red apple, and floral/fruity aromas serving as the

most preferred flavor attributes (Jaeger et al., 1998). On the second dimension of the preference

map, Jaeger et al. (1998) found that differences in texture were causing segmentation,

specifically the attributes hard, juicy, and crisp representing the most preferred apples.

2.3.3 Instrumental analysis

2.3.3.1 Physicochemical analysis

Physicochemical analyses can be used to determine the potential sweetness and acidity of

an apple variety, which are important factors in defining consumer preference (Daillant-Spinnler

et al., 1996; Jaeger et al., 1998; Harker et al., 2002). Techniques frequently used for

physicochemical analysis include measurements of SSC and TA. These instrumental practices

serve as an objective measurement and are often paired with objective sensory DA data and

subjective consumer evaluation data.

Soluble solids content is an instrumental measure to approximate sugar content in apple

fruit (Amyotte et al., 2017) and work has been conducted to examine the efficacy of SSC to

predict perceived sweetness of apples. Soluble solids content can be measured quickly and easily

by using a refractometer with juice extracted from the apple via compression of the fruit

(Corollaro et al., 2014; Ting et al., 2015; Aprea et al., 2017). However, results of previous

studies have indicated that SSC has no significant differences (p<0.05) across tested apple

varieties (Corollaro et al., 2014), or has a low correlation (r=0.41; Harker et al., 2002) to

sweetness and has instead been found to be correlated with fruity ester aromas (r=0.57; Ting et

al., 2015). Due to the lack of clarity achieved by SSC in predicting the sweetness of the fruit, it is

recommended to complement this instrumental data with an assessment by an expert trained

sensory panel in order to find meaningful predictions within the data (Harker et al., 2002).

19

Titratable acidity measurements act as a physicochemical predictor in determining the

concentrations of acids in a food, therefore serving as an indicator of acid taste. Similar to that of

SSC, recommendations for TA measurements in apples exist in which the analysis should be

conducted on apple juice which has been extracted through compression of a fruit sample and

then titrated using NaOH to an endpoint pH of 8.16 with the results being expressed as malic

acid equivalents per a defined volume of juice (Corollaro et al., 2014; Ting et al., 2015; Amyotte

et al., 2017). TA has been correlated (p<0.05) with acid/sour taste (r=0.86; Harker et al., 2002;

unreported correlation, Corollaro et al., 2014; r=0.98, Ting et al., 2015), astringency (r=0.89;

Ting et al., 2015), overall flavor (unreported correlation; Harker et al., 2002), apple flavor

(unreported correlation; Harker et al., 2002), juiciness (unreported correlation; Corollaro et al.,

2014), and a negative correlation to sweet (r=-0.88; Ting et al., 2015).

With the knowledge of these studies, and due to the complexity of human perception of

taste characteristics, it has been advised that instrumental data is not used alone, and should

instead be used in conjunction with sensory DA data (Harker et al., 2002). Although this may not

always be feasible, Harker et al. (2002) suggests that breeders may use TA to measure the

predicted acidity of apples but advises not to rely on SSC data.

2.3.3.2 Aroma and flavor measurements

Instrumental methodologies to evaluate aroma and flavor have been adopted starting in

the 1950s, with the introduction of GC and MS (Yahia, 1994; Delahunty et al., 2006). Since then,

more than 10,000 VOCs have been identified, with only a fraction of these contributing to the

overall aroma of a food product (Song and Liu, 2018). By using GC, it is now feasible to

separate individual compounds out of a mixture of VOCs, which can then be identified based on

their structural composition via retention times, quantified, and paired to a reference standard as

part of MS (Delahunty et al., 2006; Song and Liu, 2018). In addition to these two practices, it is

also possible and even encouraged to add an olfactometry (O) component to the previously

identified analysis methods. This GC-O or GC-O-MS system works by allowing the individual

VOCs to split within the GC detector, and then be directed to a port outside of the oven for a

human panelist to sniff the eluent in order to characterize the produced aroma by each individual

20

VOC, or to identify the threshold of the aroma (Yahia, 1994; Delahunty et al., 2006; Aprea et al.,

2012; Song and Liu, 2018).

In apples, VOCs are commonly extracted using a headspace collection technique, where

the fruit is left intact, cut, or macerated and then enclosed in an inert space (Song and Forney,

2007; Aprea et al., 2017) with either forced air (Rowan et al., 2009; Kumar et al., 2020) or a

vacuum (Mehinagic et al., 2006) being used to direct the VOCs for collection. The VOCs can

then be contained using an adsorbent resin trap (Mehinagic et al., 2006; Kumar et al., 2020), and

are eluted using chemicals such as diethyl ether (Rowan et al., 2009) or dicholoromethane

(Mehinagic et al., 2006; Kumar et al., 2020). This dynamic headspace collection process is an

ideal method found in the literature, as the technique allows for a good testing sensitivity and can

also be used on almost all fruits (Song and Forney, 2007). After this process, samples can be run

using GC, GC-MS, GC-O, or GC-O-MS to identify, quantify, and characterize each unique

VOC.

Identification of VOCs is typically conducted by using a reference database such as the

National Institute of Standards and Technology Standard Reference Database, or by utilizing an

authentic reference standard (Aprea et al., 2017; Kumar et al., 2020). However, the identification

of a singular VOC compound is typically not useful due to the complexity of a natural food

product, and the tested VOCs should therefore be grouped through multivariate statistics in order

to draw conclusions on the perceptible aromas in relation to other sensory attributes (Aprea et al.,

2012). Harker et al. (2002) used a PCA which identified two dimensions contributing 61.4% of

the variation within their model. Unfortunately, due to the low amount of variability attained

through the PCA, they were not able to infer any conclusions in relation to flavor. Aprea et al.

(2012) was able to identify 72 VOCs through solid-phase microextraction GC-MS within 18

unique apple varieties. This team also used PCA and hierarchical clustering analysis to conclude

that their “fruity” apple group was found to be high in acetate esters, in line with other literature

who have had similar findings (Plotto et al., 1999; Lopez et al., 2000; Mehinagic et al., 2006).

Relationships between sensory descriptors and volatile compounds had also been analyzed by

utilizing other multivariate statistical testing include partial least squares (PLS), generalized

procrustes analysis (GPA), and multi-factor analysis (MFA). Through the use of PLS, Aprea et

al. (2012) discovered that the compounds butyl acetate, hexyl acetate, amyl acetate, isobutyl

21

acetate, (Z)-3-hexenyl, and butyl propionate have the top variable importance in projection

scores across the apple, pear, and banana aromas, with different concentrations being seen across

each (Aprea et al., 2012). Du et al. (2010) used GPA to map the relationship between sensory

attributes and odor activity values of blackberries. Generalized procrustes analysis is an

intriguing methodology as it acts to scale, rotate, and translate the data for the best fit of each

individual dataset onto a common model (Chung et al., 2003). Ting et al. (2015) used MFA to

link sensory, volatile, and textural datasets to determine apple flavor. In addition, Lignou et al.

(2014) used MFA to analyze the relationship between sensory and instrumental properties of

cantaloupe melons. In accordance with Dehlholm et al. (2012), an MFA is able to combine

multiple quantitative (i.e. PCA) and qualitative (i.e. multiple correspondence analysis) analyses

to allow for the grouping of variables based on their likeness to each other. This is completed by

determining the relationship based on orientations and configurations of each dataset.

With this information, it solidifies the importance of aroma and flavor instrumental

measurements and their contribution to sensory and consumer datasets in order to properly

qualify and quantify specific aromas generated by VOCs as they relate to consumer preference

(Aprea et al., 2017).

2.4 Conclusions and future research

As stated in Section 2.1, the apple industry has recently begun to shift from a traditional

business-to-business approach which was primarily focused on disease resistance and fruit yield,

to now becoming a consumer-centric industry. This was a necessary change within the industry,

as the needs of a consumer do not necessarily align with those of apple growers and breeders

(Tesfaye et al., 2012). Recently, an emphasis has been placed on providing the highest quality

fruit in terms of taste, texture, and aroma/flavor. However, the intricacies of taste and texture

quality have become the forefront of this research, leaving aroma/flavor research to fall behind.

Therefore, the focus of the current research project is to expand on flavor research in apple fruit.

This will allow breeding programs to focus on these flavor quality components in the future to

create and select new apple varieties targeted for the desires of consumers.

22

3 Apple Flavor and Its Effects on Sensory Characteristics and

Consumer Preference

This chapter has been submitted to the Journal of Sensory Studies and adapted for this thesis.

Jordan R. MacKenziea,b, Lisa M. Duizera, Amy J. Bowenb

a Department of Food Science, University of Guelph, Guelph, ON, Canada

b Vineland Research and Innovation Centre, Vineland, ON, Canada

Author MacKenzie conducted the research, analyzed the data, and wrote the manuscript. Author

Duizer reviewed and edited the manuscript. Author Bowen received funding for the project,

oversaw the work, and reviewed and edited the manuscript.

Abstract

The focus within the apple industry is to identify varieties most preferred by consumers.

To help with this, it is necessary to emphasize the discovery of flavor perceptions responsible for

consumer preference in apples. The present study aimed to determine which flavor attributes are

associated with different apple varieties, determine which apple varieties consumers prefer, and

to determine which flavor attributes are contributing to consumer preference. Over two

subsequent years, an expert panel (n=10, n=15) evaluated 27 and 28 varieties, respectively.

Intensity ratings of taste, flavor, and texture characteristics of each apple variety were recorded.

This data was paired with an untrained consumer hedonic evaluation (n=226) using a subset of

apple varieties (n=16). Results revealed that two large groups of apple consumers exist. Group 1

(29%) emphasized the importance of texture, while Group 2 (49%) was primarily driven by

sweet taste, and honey and floral flavors with less focus on texture.

Practical applications

The results of this research provide insight into the positive and negative preference

drivers of apple consumers. By understanding flavors associated with consumer preference, the

information can be used as a tool to aid breeding programs in the creation of consumer-centric

apples that will be commercialized. Additionally, through the creation of an external preference

map, a point-of-reference has been created to serve as a predictor for upcoming apple varieties to

the Ontario apple industry.

Keywords

apple; flavor; descriptive analysis; consumer preference; preference map

23

3.1 Introduction

The primary focus of apple research is on the creation and identification of a high-quality

fruit. Musacchi and Serra (2018) defined two main avenues of quality parameters: 1) Appearance

(color, size, shape, and absence of defects), and 2) Eating quality (taste, texture, flavor, and

absence of defects). Similarly, Sansavini et al. (2004) described apple quality as defined by

appearance, sensory traits, storability, and shelf-life. Consumers are known to select apples based

on their previous experiences in relation to sensory attributes and internal characteristics of the

fruit (Jaeger et al., 2018). However, when looking into the sensory attributes of an apple, the

focus is consistently on producing an appealing taste and texture for the consumer.

Unfortunately, apple flavor is commonly overlooked, and when ideal taste and texture

parameters have been met, it is believed that the inclusion of preferred flavors will help to put

one variety ahead of another in the increasingly competitive apple market (Yahia 1994). In order

to achieve the identification of unique flavor characteristics that will drive the purchasing habits

of a consumer, it is common practice to conduct sensory evaluation techniques such as DA to

measure the intensities of each sensory attribute, while pairing this with measurements of

consumer liking.

Descriptive analysis is the optimal sensory evaluation method for food products as it is

practical for evaluating the perceived intensities of an established lexicon by independently and

objectively assessing each sensory attribute. Data collected from DA can be analyzed using an

analysis of variance (ANOVA) to identify if differences exist among products, such as apple

varieties. Multivariate statistical techniques can also be used. Agglomerative hierarchical

clustering (AHC) outlines the similarities among clusters of the varieties, and PCA define the

relationships of the characteristics of each variety (Aprea et al., 2012; Boumaza et al., 2010;

Bowen et al., 2018; Eggink et al., 2012; Iglesias et al., 2008). Limitations within DA exist. The

functional use of DA is to describe the intensity of the sensory properties of a tested product and

compare them to other products within the set. However, it does not tell you which properties are

most important or associated with consumer acceptance. In order to determine what is driving

the preference of a product, it is essential to pair this with other evaluation methods.

24

Hedonic consumer evaluation allows for the understanding of liking and preference

among tested products. With this approach, untrained consumer panelists taste and rate their

individual liking score for each product evaluated. However, there are also limitations that exist

within consumer evaluation. Differentiating the properties of liked or disliked products can be

challenging, and, without pairing the collected data with DA, there is no way to measure how

much these properties play a role in acceptance.

To optimize our understanding of preference among apples, it is essential to combine the

advantages of trained sensory and untrained consumer evaluations, to ultimately create a tool that

will allow a research team to best understand the sensory and consumer space. Fortunately, the

combination of sensory intensity scores of products with hedonic consumer ratings can be

completed using external preference mapping.

External preference mapping pairs the objective intensity evaluation scores of sensory

attributes with the subjective liking evaluation scores of consumer hedonic ratings to uncover the

true meaning of what is driving the preference within the sensory space for consumers (Lawless

& Heymann, 2010). External preference mapping works by creating regression models to

essentially build a mapping model of the consumer hedonic scores and the sensory space from a

PCA generated from the perceptual sensory product characteristics (van Kleef et al., 2006). This

same approach has been adapted within the apple industry and can be seen in recent literature

from Cliff et al. (2016), and Bowen et al. (2018). The development of the sensory space will

create opportunity for identification of apple varieties tailored to the desires of consumers based

on the newfound understanding of consumer liking. For example, a preference map created by

Bowen et al. (2018) evaluating approximately 80 apples over two years showed a clear

separation of apples with high liking versus low liking. The information from the preference map

was used to predict which sensory characteristics were driving or detracting liking among the

tested apple varieties and served as the forefront of the current research (Bowen et al.,

unpublished).

The current understanding of consumer liking among apples is that the primary indicators

of preference are based on textures (crisp, juicy, and lack of mealy), with the secondary factor

being taste (sweet or acidic). Flavor and appearance are minor indicators; however, they have not

been as extensively researched as the other sensory modalities (Daillant-Spinnler et al., 1996).

25

Literature suggests that consumers differ in which sensory properties impact their preference for

apples (Daillant-Spinnler et al., 1996; Jaeger et al., 1998). For example, Daillant-Spinnler et al.

(1996) highlighted two different consumer groups. One appeared to base their preference on

sweet tasting and hard textured apples, while the second group preferred apples to be juicy and

acidic. Similarly, Bowen et al. (2018) defined two consumer segments: the first group (89% of

consumers) was driven by sweet taste and fresh red apple aroma, with a juicy and crisp texture.

The second group (11% of consumers) was driven by acidic taste and fresh green apple aroma,

also with juicy and crisp textures. However, the future application of these results is limited, as

the diversity of the evaluated apples only allowed the researchers to understand the differences

between apples with high and low consumer liking. More work is required to identify the

differentiating characteristics of an apple that allow for the variety to be a top-performing apple

which stands out from the other highly liked varieties.

To uncover the differences between the top performing apples from Bowen et al. (2018),

the current research focuses on identifying secondary or tertiary characteristics contributing to

consumer preference. To highlight these potential characteristics, a subset of apples used in the

previous research from the Vineland Research and Innovation Centre (Vineland; Bowen et al.,

2018) were selected based on their location within the preference map, referred to as the “Apple

Sweet Spot”. Varieties within this realm were the most liked apples for the largest consumer

group (89%) and have proven to satisfy the texture, taste, and flavor expectations for consumers

within this group. The current research seeks to further define this largest consumer segment,

enabling the characterization of these sweet and flavorful apples while identifying key properties

that help to differentiate between liking among the most liked varieties. We hypothesize that

there are key taste and flavor attributes responsible for driving consumer preference in our target

population (89% of consumers) which are dependent on individual apple varieties. To

accomplish this, we had set forth three research objectives. The first objective was to determine

the flavor attributes associated with different apple varieties through sensory DA. The second

objective was to determine which apple varieties consumers prefer and find out what they

classify as their ideal apple through consumer evaluation and questionnaires. Finally, the last

objective was to identify flavor attributes that can be used as predictors of consumer preference.

26

3.2 Materials and methods

3.2.1 Products

Apples were sourced from Ontario growers whenever possible through the Ontario Apple

Growers (OAG; St. Catharines, Canada). Apples varieties not grown in Ontario were sourced

from local grocery retail. Inclusion of apple varieties for both years of the study were based on

the representation of varieties with the most Ontario market share, and the top selections from

Canadian breeding programs. Additionally, for Year 1 (2017-2018) of the study, apples were

primarily selected based on previous results of the defined “Apple Sweet Spot,” which identified

apple varieties that are liked by a large segment (89%) of the population (Bowen et al., 2018).

For Year 2 (2018-2019), apple varieties were selected based on a combination of results from

Year 1, and inclusion of additional varieties located in the “Apple Sweet Spot”.

This study collected two years of data to account for seasonal and environmental

differences. This allowed for repeatability measures to be tested across both years and provided

validation of the prediction model created. Results were based on information from 27 apple

varieties in Year 1, and 28 varieties in Year 2 with research being conducted at Vineland. The

majority of apple varieties obtained for this study were coded to anonymize the data and can be

found in Table 3.1.

Upon delivery to Vineland, apples were counted and examined to ensure the absence of

visual defects (e.g. bruising, injury, mold, etc.). They were then placed in plastic storage

containers as a single layer and stored on shelves in a designated apple cooler maintained at 2-

4°C through room cooling (Boyette et al., 1990). Apples were held in storage for a minimum of

seven days prior to any evaluation. This method was applied to allow for the standardization of

the internal ethylene concentration within apples, therefore slowing the maturation process

(Muche, 2016).

27

Table 3.1 Apple varieties selected for analysis across both years. All apples were assessed at

optimal maturity, with select apples being re-profiled if not originally profiled within three

weeks of consumer evaluation.

Profiled by DA Profiled by Consumers Re-profiled by DA†

Year 1

(2017-2018)

Year 2

(2018-2019)

Year 1

(2017-2018)

Year 1

(2017-2018)

Gala Gala Gala 55Cb

Ginger Gold Ginger Gold Granny Smith 60Cb

Granny Smith Granny Smith 55C 63Cb

55Ca 41H 58C 73Cb

56C 55C 60C 74C

58C 58C 61C 75Cb

60Ca 60C 63C 84Cb

61C 61C 70C 85Cb

63Ca 63C 73C 91Cb

64C 64C 74C

65C 65C 75C

68C 68C 84C

70C 70C 85C

73Ca 73C 91C

74C 74C 93C

75Ca 75C

80C 80C

81C 81C

82C 85C

84Ca 87C

85Ca 88C

86C 89C

88C 90C

89C 91C

91Ca 92C

92C 93C

93C 94C

94C

† indicates an apple that was not profiled within three weeks prior to consumer evaluation as we

know properties change through storage, apples were re-profiled by DA to compare the

differences at optimal maturity and at the time of consumer evaluation

C denotes a commercial variety (representing a commercially available variety)

H denotes a heritage variety (representing an older variety that is no longer commercially grown)

28

3.2.2 Maturity determination and apple handling

In order to test apples at their ideal maturity, starch iodine (SI) measurements were

applied in accordance with literature from Cornell University (Blanpied & Silsby, 1992). Apples

were removed 24 hours prior to evaluation to acclimatize to room temperature and then tested for

their current SI index to ensure that all apples were within their optimal range (5-7 SI) for

consumption.

The method used for testing the SI index included first cutting the apples in half through

the equator. One half was dipped into a solution containing 60 parts 0.1 N iodine solution (Fisher

Scientific, USA) and 40 parts milli-Q water (MilliporeSigma, USA) and then placed flesh-up on

a paper towel. To allow the solution to soak into the flesh and the starch pattern to develop,

samples were left for 2-3 minutes prior to maturity determination. This process was completed

upon receipt of apples, once weekly to observe the ongoing maturity status of the apple, and

within 24-hours of consumption.

Once an apple variety was determined to be at its ideal maturity range, apples were

removed from cold storage. To allow for acclimatization of samples, the apples were left at room

temperature (approximately 20°C) for 24-hours prior to any sensory evaluation and consumer

panel analyses.

3.2.3 Trained sensory panel evaluation

Trained sensory panelists were employed as part of an in-house sensory panel at

Vineland. The members of the panel are specialized in the evaluation of a variety of horticultural

products while using numerous sensory evaluation techniques and are well-practiced in DA

(Lawless & Heymann, 2010). Performance monitoring took place weekly to track the accuracy

and reliability of the results. The apple sensory lexicon used for this study was first established in

2013 and carried through for this study (Bowen et al., 2018). Training exercises were routinely

used to assess the sensory acuity of the panelists, focusing on their ability to perceive basic tastes

and mouthfeels (sweet, acid, bitter, astringent), textures (skin thickness, crisp, juicy, chewy,

mealy, rate of melt), and aromas/flavors (oxidized apple, earthy, hay, honey, lemony, floral,

grassy/vegetal, overall aromatic intensity) of this 18-attribute lexicon.

29

In Year 1, 27 apple varieties were profiled using DA in eight 1.5-hour sessions over four

months (October 2017 to January 2018), based on the maturity of each variety, by the trained

sensory panel (n=10, average). In Year 2, 28 apple varieties were profiled using DA in eight 1.5-

hour sessions over four months (October 2018 to January 2019), based on the maturity of each

variety, by a trained sensory panel (n=15, average).

A taste and aroma reference tray containing reference standards was provided to panelists

at the start of each session to help calibrate their senses prior to evaluation. Texture reference

trays were provided at the start of the profiling period in October and again in January for

recalibration of the textural properties. Specific descriptions and ingredient recipes for the taste

and aroma/flavor reference standards can be found in Table 3.2, and texture reference standards

in Table 3.3.

For testing, panelists evaluated the apples in the dedicated sensory tasting room at

Vineland at room temperature in white semi-isolated booths with red lighting to mask any

differences in color or other distinguishing properties of the apple. Panelists were asked to reset

their palate by rinsing their mouth with unfiltered water and/or eating unsalted crackers

(Premium Plus) between samples. Apples were freshly washed and cut prior to evaluation to

avoid oxidation of the apple and to delay disruption of apple volatiles when the apple sample was

cut (Ting et al., 2012). Apples were cut into 6-8 wedges based on size and were presented

monadically to panelists in a clear 2 oz cup in a randomized balanced design with randomized 3-

digit codes. Tastings were conducted by duplicating each variety to test repeatability, with two

wedges (skin-on) per repetition. Intensity ratings for each attribute were evaluated using

EyeQuestion software (Logic8, Netherlands), and used a 15 cm line scale with anchors of

“weak” to “intense”, indented at the 10% and 90% positions respectively.

30

Table 3.2 Basic taste, mouthfeel, and aroma reference tray standards with recipes.

Reference Preparation Method

Sweet 6.0 g sucrose + 400 mL applesauce†

Acid 1.0 g malic acid + 400 mL applesauce†

Bitter 0.10 g caffeine + 400 mL applesauce†

Astringent 0.90 g Kalum (potassium aluminum sulphate dodecahydrate) + 400 mL

applesauce†

Earthy 18 µL earthy (#11)‡ + 400 mL applesauce†

Honey 20 g honey§ + 400 mL applesauce†

Grassy/vegetal ½ pot cat grass + 600 mL filtered water

Soak 30 minutes, filter, + 1 mL ‘Green’ solution¶ + 400 mL filtered

water

Oxidized apple Cut one Red Delicious apple, allowing to oxidize for 30 minutes

Hay 270 µL hay (#38) ‡ + 600 mL filtered water

Floral 10 mL rose water‖ + 800 mL filtered water

Lemony 360 µL lemon extract + 800 mL filtered water

Overall aromatic intensity 40 mL applesauce†

† Mott’s Fruitsations unsweetened applesauce ‡ Le nez du vin “The Masterkit 54 aromas” § BillyBee pure natural pasteurized honey ¶ 500 mL filtered water, 9 µL green pepper (#30)‡ ‖ Cortas rose water

31

Table 3.3 Texture reference tray with weak and intense anchors.

Reference Description Anchors

Skin

thickness

Amount of force to bite through skin. Measured on

initial bite, flesh and skin

Ripe pear (weak)

Granny Smith apple

(intense)

Crisp Breaks apart in single step. Sound frequency.

Force of fracture when biting. Measured on initial

bite, flesh only

Banana (weak)

Carrot (intense)

Juicy Amount of liquid released when chewing.

Measured while chewing, flesh only

Banana (weak)

Watermelon (intense)

Chewy Time and number of chewing movements needed

to rend the sample prior to swallowing. Measured

while chewing, flesh and skin

Ripe pear (weak)

Unripe pear (intense)

Mealy Soft, dry, granular flesh. Mealiness is associated

with cells that separate and retain as opposed to

releasing juices. Measured while chewing, flesh

only

Watermelon (weak)

Bosc pear (intense)

Rate of melt Amount of product melted after a certain number

of chews. Measured while chewing, flesh only

Celery (weak)

Watermelon (intense)

3.2.4 Consumer hedonic evaluation

In Year 1, a pre-screened group of untrained apple consumers (n=226) were recruited for

a consumer hedonic evaluation over six 1-hour sessions (Toronto, Canada). Participants were

recruited from the Greater Toronto Area (GTA) through a market-research company (Decision

Point Research) and were selected based on their consumption frequency of apples, gender,

ethnic heritage, and age. Apples (n=16; see Table 3.1) were selected based on an initial analysis

of the Year 1 sensory data. Representative apples from each quadrant of the sensory map

generated from running a PCA on the DA results were selected to ensure sensory diversity, and

further selections were based on color diversity, familiarity, market share, acreage planted in

Ontario, and consumer liking ratings from a previous study (Bowen et al., 2018). Varieties

selected to go to the consumer study were reprofiled through DA if they had not been evaluated

by the trained sensory panel in the previous three weeks. This process was established in order to

32

capture any changes in sensory characteristics due to the natural variability that comes along

with maturation.

The consumers provided consent to participate in the study and were compensated for

their time (Appendix 1). Each participant was required to complete the following: 1) evaluation

of products based on the degree of liking for each variety with a selection of attributes they

would use to define each variety, 2) to describe their ideal apple, 3) complete a questionnaire

pertaining to demographic, purchase history, and consumption behavior, and finally, 4) a visual

exercise to determine the top and bottom three varieties that a consumer would choose to

purchase, with their reasons why.

For the first exercise, results were recorded using an iPad (Apple, USA) running

EyeQuestion (Logic8, NL) data collection software. Consumers were separated into pre-

numbered semi-isolated booths. Study conditions were conducted at room temperature, and

samples were evaluated blind under fluorescent commercial lighting.

Prior to tasting, apples were freshly washed and cut into 6-8 wedges based on size. One

wedge (skin-on) was placed in a clear 2 oz plastic cup labelled with a unique randomized 3-digit

code following a randomized balanced design made up of four sets of four products in each.

Upon tasting, panelists were instructed to monadically evaluate samples from left to right. They

were then asked to bite into the apple wedge and consume both skin and flesh of the apple. Two

questions were asked: 1) “How much do you like this apple?” and 2) “Check off all of the

attributes that describe this apple” (Appendix 2). To answer the first question, a 15 cm

continuous line-scale was provided with indented anchors of "dislike extremely" to "like

extremely" placed at 10% and 90% of the scale, respectively. Results of this testing were

automatically converted to a score out of 100, through the EyeQuestion (Logic8, NL) software.

The first sample served to each panelist was a Red Delicious apple which, unknowingly to the

panelist, served as a practice sample to avoid bias by an order effect (Wakeling & MacFie,

1995). Results of this apple were removed from the final consumer data analysis. Once each

apple sample was finished, panelists were asked to rinse their palates with filtered water and/or

eat an unsalted cracker before moving to the next sample.

33

Following the hedonic evaluation, participants were required to answer a questionnaire

regarding their consumption behavior, purchase habits, and demographic information (Appendix

3). Before exiting the evaluation room, a booth was setup containing each of the 16 apple

varieties. The apples displayed were selected to be free of blemishes and bruises, and uniform in

size. Each variety was placed on a white plate and labeled with a randomized 3-digit code and

consumers were asked to identify their three most and least liked apples based only on

appearance. The specific evaluation terms and instructions can be found in Appendix 4.

3.2.5 Statistical analysis

A 3-way mixed model ANOVA with 2-way interactions was conducted on the dataset

from DA. Qualitative variables of assessor, product, and replicate were paired with quantitative

variables, our sensory attribute intensities. A Tukey’s Honestly Significant Difference test and

Fisher’s Least Square Difference test were used for post-hoc analyses. In Year 1, there were no

violations of the test assumptions for the homogeneity of variance (Levene’s k-sample

comparison of variances). However, in Year 2, four attributes of the 18 violated these

assumptions and were subsequently subjected to a 1-way ANOVA using the Welch statistic and

Games-Howell post-hoc analysis. Following this, attribute intensity scores were averaged and

used to run a PCA with a covariance structure (n-1). This helped to create a sensory map, and

using factor analysis (FA), four and three factors for Years 1 and 2 respectively, were used to

explain the model. Agglomerative hierarchical clustering was then used to cluster the sensory

profiles of 27 (Year 1) and 28 (Year 2) apple varieties using Ward’s method of agglomeration

based on their dissimilarities. The PCA and AHC models created from sensory evaluation data

were used to define the sensory diversity of the apple set and identify similarly grouped apples,

information that was used for the selection of apples to be used for the consumer evaluation.

Consumer evaluation was conducted in Year 1 of the experiment only. A 1-way ANOVA

was used with Games-Howell post-hoc analysis and Welch statistic to differentiate the

differences of liking among products. Next, an AHC using Euclidean distance and Ward’s

method of agglomeration was used to determine that three consumer groups existed. Following

this, the ANOVA procedure as listed above was repeated on each of the three consumer groups.

Here, Groups 1 and 2 needed to be transformed as they had heterogeneous variances, while the

34

data for Group 3 did not need to be transformed. Once the three consumer groups were defined, a

PCA with covariance structure (n-1) was created by mapping the sensory intensity scores of

apples taken to the consumer evaluation (n=15). Factor analysis was used to determine that three

factors were providing statistically significant information to the dataset. Apples in Year 1 and

Year 2 that were not taken to the consumer evaluation were added as supplementary variables

and were superimposed onto the PCA.

By combining the results of PCA across three factors with the liking scores of the three

consumer groups, an external preference map was formed. This predictive model helped to

explain which factors influenced consumer liking by looking at positioning along the vectors

created within the sensory space. Supplementary data was overlaid onto this preference map to

visualize the predictive liking of varieties not included in the consumer evaluation. Additionally,

a contour plot was overlaid onto each of the biplots which helped visualize the liking-regions of

consumers.

To enable the consumers to describe each apple variety, and to understand what defines

an ideal apple for consumers, CATA questionnaire data was examined by measuring the

relationship between the products, assessors, preference data, and the survey responses

(Appendix 2). Terms used in the CATA varied from terms used in the lexicon used for DA. This

list of terms can also be found in Appendix 2. As determined by AHC, responses were split into

the three consumer segments. A list of must have and must not have attributes was formulated by

determining the average preference among assessors and products, with the percentage of

records corresponding to a checked or unchecked box. If the preference for a checked attribute is

significantly higher than the preference for an unchecked attribute, it is said to be a must have

attribute for this consumer group, with the reverse being true to define the must not have

attributes. Responses from the demographic questionnaire were subject to chi-square analysis to

further define the demographic profiles of each consumer group. Chi-square testing was

conducted by analyzing the difference in survey responses among the three consumer groups. A

Fisher’s exact test was used to determine the degree of significance across groups, and to

highlight significant differences that existed. Additionally, CATA profiles were generated for

each consumer group, but were not investigated further in this study.

35

Visual evaluation results were evaluated using a total unduplicated reach and frequency

(TURF) analysis to determine the percentage of reach for each variety and descriptor, and k-

proportion analysis to determine the percentage that a variety would be accepted based on visual

appearance alone. In this section, “reach” was defined as the apples that are most selected

(positively or negatively) by the consumer groups, and “frequency” represents how many times

an apple variety was selected as most likely to be purchased in comparison to the total amount of

times selected (most or least likely to purchase). The visual evaluation was divided into the three

previously defined consumer groups. The full list of questions from the questionnaire can be

found in Appendix 4.

Data analysis was conducted using XLStat versions 2018 and 2019 (Addinsoft, France).

A significance level of 5% was used, with the exception of 10% significance used for the

analysis of chi-square responses.

3.3 Results

3.3.1 Descriptive analysis

Significant product effects (p≤0.001) occurred across both product sets for all attributes

(oxidized apple, earthy, hay, honey, floral, grassy/vegetal, lemony, and overall aromatic intensity

aromas/flavors; sweet, acid, bitter, and astringent tastes and mouthfeels; and skin thickness,

crisp, juicy, chewy, mealy, and rate of melt textures) in both years, with the exception of hay in

Year 2 (p=0.100). Thus, hay aroma was not used for further analysis in Year 2. The mean values

for each attribute are listed in Table 3.4.

36

Table 3.4 Mean intensity scores (0-100) from a 15 cm line scale, standard deviations and level of

significance when comparing product effects across individual apple varieties for sensory

attributes in Year 1 and Year 2 sensory evaluation.

Sensory attribute Year 1 Year 2

Mean ±

Standard

deviation

p-value f-statistic Mean ±

Standard

deviation

p-value f-statistic

Oxidized apple 22.1 ± 16.79 <0.0001 4.3 25.7 ± 13.36 0.001 2.2

Earthy 12.5 ± 14.47 <0.0001 3.6 20.0 ± 12.20 <0.0001 3.4

Hay 23.0 ± 14.86 0.001 2.0 25.3 ± 11.34 0.100* 1.4

Honey 20.5 ± 18.95 <0.0001 5.0 25.0 ± 15.89 <0.0001 8.1

Lemony 15.7 ± 14.35 <0.0001 4.8 19.6 ± 12.81 <0.0001 5.3

Floral 18.7 ± 17.40 <0.0001 2.6 19.5 ± 13.66 <0.0001 4.3

Grassy/vegetal 18.1 ± 15.42 <0.0001 2.6 19.7 ± 10.82 <0.0001 3.3

Overall aromatic

intensity

26.3 ± 14.39 <0.0001 2.7 27.8 ± 10.95 <0.0001 4.1

Sweet 34.9 ± 16.35 <0.0001 7.2 32.3 ± 12.48 <0.0001 5.8

Acid 29.5 ± 16.70 <0.0001 10.8 28.7 ± 14.31 <0.0001 15.3

Bitter 19.0 ± 16.91 <0.0001 2.6 19.1 ± 10.60 <0.0001 6.4

Astringent 25.8 ± 17.20 <0.0001 3.3 25.7 ± 12.01 <0.0001 5.4

Skin thickness 56.6 ± 19.52 <0.0001 8.3 55.2 ± 18.25 <0.0001 8.9

Crisp 59.9 ± 17.55 <0.0001 15.4 51.5 ± 19.17 <0.0001 56.1

Juicy 54.1 ± 16.82 <0.0001 5.0 50.4 ± 17.45 <0.0001 15.7

Chewy 62.6 ± 16.29 <0.0001 5.1 60.8 ± 15.51 <0.0001 5.0

Mealy 18.1 ± 17.59 <0.0001 3.4 29.0 ± 20.08 <0.0001 7.1

Rate of melt 53.3 ± 19.58 <0.0001 5.2 57.2 ± 19.44 <0.0001 15.5

* indicates a product effect that was not significant at p<0.05

37

3.3.2 Creating a sensory map and formation of apple groupings

A sensory map was created using average intensity ratings for each attribute from DA.

The procedures for this section varied, and will therefore be written as Year 1, followed by Year

2.

In Year 1, an initial PCA was run on the full dataset. Kaiser-Meyer-Olkin (KMO) scores

indicating the level of sampling adequacy were observed. To obtain an accurate representation of

our sensory map, a cut-off of 0.50 KMO score was applied to sensory attributes, while the entire

model had a minimum KMO score of 0.70. Both threshold values were used in accordance with

Mooi and Sarstedt (2011), outlining the acceptable tolerance levels for sampling adequacy.

Using this approach, earthy flavor (KMO=0.38) was removed from our dataset as the KMO

value did not reach the specified threshold. This allowed the KMO value of the dataset to be

0.71. As discussed in Section 3.2.5, FA was used within the Statistical Package for the Social

Sciences software (2018, 2019; IBM, USA) to identify four factors correlating with the 17

remaining sensory attributes. A secondary PCA with a varimax rotation was used to reorient all

17 attributes to a correlated factor, explaining 84.8% of the total variation within the projected

model. Factor 1 (22.8% of variation) was positively correlated with acid, lemony, astringent,

grassy/vegetal, and bitter. Factor 2 (29.8% of variation) was positively correlated with mealy,

skin thickness, oxidized apple, and negatively correlated with crisp, and juicy. Factor 3 (15.8%

of variation) was positively correlated with chewy, and negatively correlated with rate of melt.

Factor 4 (16.3% of variation) was positively correlated with honey, overall aromatic intensity,

floral, sweet, and hay. A full summary of these correlations can be found in Table 3.5 and a

depiction of the factors related to flavor (Factor 1 and Factor 4) are shown in Figure 3.1.

38

Table 3.5 Year 1: Summary of correlations for sensory evaluation PCA.

Sensory attribute Factor 1

(22.8%)

Factor 2

(29.8%)

Factor 3

(15.8%)

Factor 4

(16.3%)

Oxidized apple 0.164 0.676 -0.218 0.378

Hay 0.077 0.026 0.125 0.698

Honey -0.433 -0.198 0.006 0.839

Lemony 0.916 -0.114 -0.013 0.052

Floral -0.307 -0.077 -0.178 0.766

Grassy/vegetal 0.708 0.064 0.218 -0.116

Overall aromatic intensity 0.287 0.325 -0.046 0.793

Sweet -0.553 -0.176 -0.155 0.742

Acid 0.926 0.040 0.047 -0.234

Bitter 0.632 0.348 0.041 -0.224

Astringent 0.725 0.260 0.249 0.106

Skin thickness 0.262 0.725 0.384 -0.399

Crisp -0.053 -0.937 0.285 0.076

Juicy 0.209 -0.750 -0.306 -0.071

Chewy 0.244 0.223 0.858 -0.157

Mealy 0.311 0.851 -0.001 -0.072

Rate of melt -0.137 0.277 -0.899 -0.083

Values in bold denote a strong correlation (r>0.6 when rounded to one decimal place)

39

Figure 3.1 PCA generated from sensory DA data in Year 1. This image is depicting Factors 1

(22.8%) and 4 (16.3%) for a total variation of 39.2%, as these are the factors related to apple

flavor.

Results from the Year 1 AHC showed that four groupings of apple varieties existed

within the dataset. To help differentiate the characteristics of these groups, the class centroids

(representing the projected means for each group) were collected and superimposed onto the

PCA as supplementary data. Apple varieties represented by Group A (n=8) were described as

having the highest rate of melt with a non-chewy texture. They also had low levels of lemony

and grassy/vegetal aromas, low acid, bitter and astringent tastes or mouthfeels. Varieties in

Group B (n=10) had the most balanced profiles as these varieties were highly perceived as

chewy apples with a low rate of melt. Apples in Group C (n=5) were described as having the

highest oxidized apple aroma, skin thickness and mealiness and were also found to be low in

crisp and juicy textures. Finally, apples in Group D (n=5) identified with having the lowest levels

40

of hay, honey, floral, and overall aromatic intensity aromas, as well as sweetness. Varieties in

Group D were also highest in lemony and grassy/vegetal aromas, and acid, bitter, and astringent

tastes or mouthfeels.

A sensory map for Year 2 of the study was conducted using the same procedure as Year

1. However, low KMO scores resulted in the removal of lemony (KMO=0.42), and when

retested, earthy (KMO=0.52), oxidized apple (KMO=0.48), rate of melt (KMO=0.45), mealy

(KMO=0.51), and crisp (KMO=0.41). This allowed for the KMO of the dataset to be above our

threshold at 0.73. The final PCA was therefore evaluating 11 of the 17 remaining attributes.

In Year 2, a FA identified three factors providing statistically significant information.

These factors described 86.9% of the total variation within our model, while correlating to the

remaining 11 sensory attributes. Factor 1 (49.1% of variation) was positively correlated with skin

thickness, chewy, acid, bitter, astringent, and grassy/vegetal, while being negatively correlated

with sweet, honey, and floral. Factor 2 (25.1% of variation) was positively correlated with juicy,

acid, and astringent. Factor 3 (12.6% of variation) was positively correlated with overall

aromatic intensity and chewy. A full summary of these correlations can be found in Table 3.4

and depiction of the factors related to flavor (Factor 1 and Factor 2) are shown in Figure 3.2.

Next, an AHC was conducted following the same parameters as Year 1. These results

identified four groups of apples that existed based on dissimilarity. These groups were

superimposed onto the sensory map formed by the PCA. Group A (n=10) was defined by

varieties high in honey, overall aromatic intensity, floral, and sweetness but were low in

astringent and acid. Group B (n=9) was defined by being high in juicy, with otherwise very

balanced attributes (having middling attribute intensities). Group C (n=4) was defined by being

low in sweet, honey, and juicy, with high skin thickness, acid, astringent, and chewy. Group D

(n=4) was defined by low overall aromatic intensity, floral, and acid.

41

Figure 3.2 PCA generated from sensory DA data in Year 2. This image is depicting Factors 1

(49.1%) and 4 (25.1%) for a total variation of 74.2%, as these are the factors related to apple

flavor.

3.3.3 Consumer evaluation

The initial ANOVA on the whole consumer dataset proved that the liking among

products was statistically significant (p<0.05). Heterogeneity was seen across liking for each

variety (p<0.0001) from the Levene’s test with a non-normal distribution. To avoid generalities,

and as stated in Section 3.3.2, consumer segmentation was applied to the dataset to enable a

more specific understanding of preference.

3.3.4 Defining consumer groups and mapping sensory properties

Results of the AHC identified three consumer segments from our initial group (n=226):

Group 1 (n=65) represented 28.9% of the population, Group 2 (n=110) was the largest of the

42

groups representing 48.7% of the tested population, and Group 3 (n=51) represented 22.6% of

the population. All three groups were not normally distributed (Groups 1 and 2: p<0.0001,

Group 3: p<0.001). Groups 1 and 2 were found to have heterogeneous variances (Group 1:

p=0.004, Group 2: p<0.0001) while Group 3 had homogeneous variances (p=0.146). Results of

the 1-way ANOVA conducted on each group found that Group 1 had a distribution of liking

scores ranging from 47.6 to 76.5 out of a possible 100. Similarly, liking scores of Group 2

ranged from 23.5 to 73.5 and results of Group 3 showed a very limited range of liking scores

spanning from 39.2 to 54.9. A summary of these liking scores can be seen in Table 3.6. Next, a

sensory map was created using the sensory intensity ratings from the sub-set of 15 apple varieties

taken to the consumer evaluation. Note that earthy was removed from this list of attributes, as it

was a non-significant contributor in the previous statistical analysis. Supplementary observations

were included and contained the remaining 21 apple varieties included in the original analysis,

with nine varieties that were profiled two times: once at ideal maturity, and once prior to

consumer evaluation (See Table 3.1).

Astringent, lemony, hay, and overall aromatic intensity did not provide significant

information, and resultantly an oblimin rotation with Kaiser normalization was used to reorient

the dataset to successfully have 15 of the 17 remaining attributes correlating to a factor. Hay and

overall aromatic intensity were the exceptions in this case as the correlations to a factor did not

exceed 0.6 when rounding to the nearest tenth. Results of the PCA showed that 82.4% of the

variability was captured within the model. Factor 1 represented 41.8% of the variability and was

primarily defined by acid, sweet, honey, grassy/vegetal, bitter, lemony, floral, astringent, and

chewy. Factor 2 represented 28.1% of the variability defined by juicy, skin thickness, crisp, and

mealy. Factor 3 represented 12.5% of the variability and was composed of rate of melt, oxidized

apple, and crisp. Correlations for each factor can be found in Table 3.7.

43

Table 3.6 Mean liking scores by each consumer group for apples evaluated in Year 1.

Apple variety

Group 1

(n=65; 29%)

Group 2

(n=110; 49%)

Group 3

(n=51, 22%)

Gala 70.0 58.6 43.4

Granny Smith 59.2 34.7 45.8

55C 71.3 73.4 45.9

58C 76.5 67.2 54.9

60C 76.2 62.3 51.3

61C 65.9 39.5 45.0

63C 47.6 23.5 43.7

70C 71.5 52.1 43.0

73C 72.9 64.3 41.9

74C 73.2 40.2 50.4

75C 62.7 45.7 50.2

84C 68.1 48.6 40.6

85C 64.8 51.7 42.2

91C 66.6 34.3 49.8

93C 70.0 52.2 39.2

44

Table 3.7 Year 1: Summary of correlations for consumer evaluation PCA.

Sensory attribute Factor 1 Factor 2 Factor 3

(41.8%) (28.1%) (12.5%)

Oxidized apple -0.164 -0.368 0.706

Hay 0.135 0.101 0.400

Honey -0.886 0.216 0.170

Lemony 0.828 0.296 0.178

Floral -0.672 0.229 0.101

Grassy/vegetal 0.839 0.086 0.060

Overall aromatic intensity 0.098 -0.141 0.493

Sweet -0.903 0.252 0.240

Acid 0.947 -0.025 0.090

Bitter 0.830 -0.271 0.174

Astringent 0.640 -0.102 0.318

Skin thickness 0.554 -0.811 -0.019

Crisp -0.134 0.790 -0.630

Juicy 0.156 0.892 -0.021

Chewy 0.608 -0.565 -0.429

Mealy 0.331 -0.771 0.444

Rate of melt -0.099 -0.038 0.882


3.3.5 Generating a preference map

A preference map was used to project the remaining apple varieties and sensory attributes

onto three dimensions. An initial preference map was run on each of the three consumer groups,

and it was determined that for consumer Group 3 (n=51) liking could not be significantly

differentiated among the apples (p=0.863). Therefore, only Group 1 (n=65) and Group 2 (n=110)

were included in the final iteration of the preference map (See Figure 3.1). Final predictive liking

score results of this test are shown in Table 3.8, where liking scores for Group 1 (n=65) ranged

from 50.7 (Apple 63Cb) to 77.2 (Apple 55Ca) for all apple varieties, and 50.9 (Skin thickness) to

83.5 (Crisp) for the sensory attributes. These values were scaled to be out of 100. Similarly,

Group 2 (n=110) represented a larger range of liking scores for the apple varieties ranging from

24.5 (Apple 63Ca) to 70.2 (Apple 55Ca), and 14.3 (Acid) to 85.2 (Sweet) for the sensory

attributes (See Table 3.8). A contour plot was generated, identifying three separate regions of

liking among consumers. These regions identified that 9 of the apple varieties and 10 of the

45

sensory attributes were predicted to satisfy 0-20% of the consumers when compared to an

average apple, 6 apple varieties and 2 sensory attributes satisfied 40-60% of consumers, and 20

apple varieties with 5 sensory attributes satisfied 80-100% of the consumers. This breakdown of

the contour plots can be found in Table 3.9, with a figure of the preference map with contour

plots overlaid in Figures 3.3, 3.4, and 3.5.

Table 3.8 Predicted liking scores for Years 1 and 2 of both apple varieties and sensory attributes

based on DA and consumer evaluation results.

Apple variety/Sensory

attribute

Predicted liking scores of

Group 1


Group 2

Gala 69.5 53.1

Ginger Gold 72.4 56.3

Granny Smith 60.2 29.8

55Ca 77.2 70.2

55Cb 73.7 67.9

56C 61.3 45.5

58C 73.3 67.5

60Ca 74.4 61.8

60Cb 73.6 57.4

61C 61.8 33.7

63Ca 52.0 24.5

63Cb 50.7 27.2

64C 68.4 50.3

65C 69.4 53.9

68C 76.2 64.6

70C 73.6 57.5

73C 73.0 60.2

74Ca 72.6 47.3

74Cb 70.6 45.7

75Ca 59.1 35.9

75Cb 59.6 45.1

80C 73.8 54.7

81C 74.4 57.4

82C 75.0 59.1

84Ca 71.1 54.0

84Cb 69.0 57.7

85Ca 73.0 54.8

85Cb 65.8 53.1

86C 67.5 40.1

88C 66.5 48.9

46

Table 3.8 Continued.

Apple variety/Sensory

attribute


Group 1


Group 2

89C 65.9 56.5

91Ca 70.1 42.8

91Cb 67.9 36.9

92C 61.5 25.8

93C 74.0 55.5

94C 73.8 50.8

Acid 53.9 14.3

Overall aromatic intensity 61.1 44.7

Astringent 55.5 25.0

Bitter 52.0 16.8

Chewy 56.1 23.5

Crisp 83.5 61.4

Floral 78.8 76.5

Grassy/vegetal 56.9 19.2

Hay 64.1 45.2

Honey 81.1 84.4

Juicy 76.2 50.7

Lemony 58.6 21.0

Mealy 51.0 31.4

Oxidized apple 60.4 52.5

Rate of melt 62.1 52.4

Skin thickness 50.9 23.2

Sweet 81.2 85.2

47

Table 3.9 Estimation of consumer satisfaction for both apple varieties and sensory attributes

when compared to an average apple, based on contour plot analysis.

0-20% satisfaction 40-60% satisfaction 80-100% satisfaction

Granny Smith 74Ca Gala

56C 74Cb Ginger Gold

61C 85Cb 55Ca

63Ca 89C 55Cb

63Cb 91Ca 58C

75Ca 91Cb 60Ca

75Cb Oxidized apple 60Cb

86C Rate of melt 64C

88C 65C

92C 68C

Acid 70C

Overall aromatic intensity 73C

Astringent 80C

Bitter 81C

Chewy 82C

Grassy/vegetal 84Ca

Hay 84Cb

Lemony 85Ca

Mealy 93C

Skin thickness 94C

Crisp

Floral

Honey

Juicy

Sweet

48

Figure 3.3 Preference map conducted on Year 1 sensory DA data (PCA) and consumer hedonic

scores. This figure represents Factor 1 (D1, x-axis) and Factor 2 (D2, y-axis), with contour plot

overlaid (Red = 80-100% satisfaction, Green = 40-60% satisfaction, Blue = 0-20% satisfaction).

Factor 1 is represented by lemony, grassy/vegetal, acid, bitter, astringent, skin thickness, and

chewy in the positive direction, and honey, floral, and sweet in the negative direction. Factor 2 is

represented by crisp and juicy in the positive direction, and skin thickness, chewy, and mealy in

the negative direction.

49




Factor 1 is represented by lemony, grassy/vegetal, acid, bitter, astringent, skin thickness, and

chewy in the positive direction, and honey, floral, and sweet in the negative direction. Factor 3 is

represented by oxidized apple and rate of melt in the positive direction, and crisp in the negative

direction.

50




Factor 2 is represented by crisp, and juicy in the positive direction, and skin thickness, chewy,

and mealy in the negative direction. Factor 3 is represented by oxidized apple and rate of melt in

the positive direction, and crisp in the negative direction.

3.3.6 Understanding an ideal apple

Results of the CATA questionnaire, where consumers were asked to describe and define

their ideal apple, are as follows:

Consumer Group 1 (n=65) showed significant differences between their liking of apples

(p<0.0001), as well as all sensory attributes (p<0.001). A sensory map indicating preference was

created using a two-factor PCA. A cumulative variability of 81.2% was represented by Factor 1

51

(57.0% of variability), and Factor 2 (24.2% of variability). Results indicated that an ideal apple

for Group 1 must have the CATA terms of thin skin with a sweet, crisp, juicy, or aromatic profile

and must not have thick skin or a sour taste.


(p<0.0001), as well as all sensory attributes (p≤0.0001). A sensory map indicating preference

was created using a two-factor PCA. A cumulative variability of 85.2% was represented by

Factor 1 (70.2% of variability), and Factor 2 (15.0% of variability). An ideal apple for Group 2

must have the CATA terms of thin skin with a sweet, flavorful, juicy, or crisp profile and must

not have thick skin or a sour taste. Additionally, a strong correlation was found for liking in

Group 2 with sweet taste (r=0.591), showing that liking among this group is heavily driven by

sweetness.


(p<0.0001), as well as all sensory attributes (p≤0.0001), with the exception of aromatic

(p=0.212), and off-flavor (p=0.689) attributes, meaning that aromatic and off-flavor were not

helping the panelists to discriminate the products. A sensory map indicating preference was

created using a two-factor PCA. A cumulative variability of 76.2% was represented by Factor 1

(44.0% of variability), and Factor 2 (32.3% of variability). An ideal apple for Group 3 must have

the CATA terms of thin skin with a flavorful, sweet, juicy, and crisp characteristics and must not

have thick skin or bland or sour characteristics.

3.3.7 Demographics, purchase behavior, and consumption habits

Demographic results from the chi-square for Group 1 showed significant differences

(p<0.10). This group typically had no children less than 19 years of age (p=0.05) and did not

have two or more children (p=0.10). This group was less likely to be Chinese (p=0.05) and were

more likely to be born in Canada (p=0.10) than not (p=0.05). From an education standpoint, this

group was less likely to have a bachelor’s degree (p=0.10). Consumers in this group were more

likely to have purchased Granny Smith apples (p=0.05) within the past 12 months. When

purchasing apples, this group did not typically purchase for everyone in their family equally

(p=0.05).

52

Group 2 chi-square results showed that these members do not have zero children (p=0.10)

and typically had one child less than 19 years of age (p=0.05).This group was less likely to be

Canadian (p=0.05), and more likely to be Chinese (p=0.05). Consumers in this group were more

likely to not be born in Canada (p=0.05) and more likely to have a bachelor’s degree (p=0.05).

This group was more likely to have purchased apple 58C (p=0.05), and less likely to have

purchased Granny Smith (p=0.05) within the past 12 months. Members of Group 2 were more

likely to equally purchase for everyone in their family, and not just for themselves (p=0.05).

Factors that impact the purchasing of apples for consumers in Group 2 were: no external damage,

being firm, skin color, and familiarity (p=0.10).

Individuals in Group 3 showed a significantly different response. They typically had two

or more children less than 19 years of age (p=0.10). Consumers in this group were more likely

to come from Canadian heritage (p=0.05), and less likely from East/South East Asian (p=0.05) or

other backgrounds (p=0.05). Additionally, these consumers were more likely to select that they

were born in Canada (p=0.05). This group was less likely to have a graduate degree (p=0.05).

Within this group, the consumers did not purchase apple 58C (p=0.05), or Gala (p=0.10) within

the past 12 months. Data does not reveal whom individuals in this group purchase apples for but

did identify that no external damage, firmness, skin color, and familiarity were not as important

when making their purchasing decisions in comparison to the other two consumer groups

(p=0.10).

3.3.8 Visual evaluation

The visual evaluation test mentioned in Section 3.2.4 compared apples that consumers

would be most likely to purchase versus apples they would be least likely to purchase based

solely on appearance, represented in terms of reach and frequency (See Table 3.10). In addition,

participants were asked to list their three reasons driving their decisions for both most and least

likely to purchase apples. Consistencies existed across each of the three groups, wherein

consumers were more likely to purchase apples that appear healthy, red, vibrant, and familiar. On

the contrary, consumers in each group were less likely to purchase apples that appeared

unhealthy or irregularly shaped (See Table 3.10).

53

Table 3.10 A comparison of reach and frequency for apple varieties used in the apple consumer

visual evaluation.

Apple variety Color† Group 1 Group 2 Group 3

Reach Frequency Reach Frequency Reach Frequency

Gala Red 35.9% 96.8% 42.1% 84.4% 25.5% 72.2%

Granny Smith Green 39.1% 74.3% 25.2% 59.6% 37.3% 50.0%

55Cb Yellow 9.4% 18.5% 9.3% 9.1% 2.0% 25.0%

58C Red 10.9% 12.5% 15.0% 46.5% 5.9% 50.0%

60Ca Red 28.1% 77.8% 9.3% 41.7% 21.6% 35.7%

61C Red 17.2% 78.9% 23.4% 80.0% 29.4% 66.7%

63Cb Red 10.9% 14.3% 6.5% 25.0% 15.7% 28.6%

65C Red 26.6% 73.9% 24.3% 61.3% 17.6% 80.0%

70C Red 25.0% 84.2% 21.5% 55.6% 11.8% 37.5%

73C Red 7.8% 34.8% 14.0% 45.5% 17.6% 46.2%

74Cb Red 14.1% 65.0% 17.8% 26.8% 17.6% 65.0%

75Cb Red 12.5% 80.0% 25.2% 79.2% 25.5% 56.5%

84Cb Yellow 12.5% 23.5% 15.9% 50.0% 17.6% 28.0%

85Cb Yellow 10.9% 28.1% 9.3% 24.4% 9.8% 27.3%

91Cb Red 26.6% 53.3% 27.1% 92.3% 31.4% 94.7%

93C‡ Red 12.5% 11.8% 14.0% 55.9% 13.7% 43.8% † Indicates the primary color of the apple

‡ Indicates a non-commercialized variety, involved in an active breeding program

To summarize these findings, Group 1 rated Granny Smith (a popular green apple) as the

variety with the highest reach (39.1%), with a frequency of 74.3% of these selections being most

likely to purchase this apple. This is consistent with results in Section 3.3.7, showing that

members of Group 1 had purchased Granny Smith apples in the past 12 months. Gala (a popular

red apple) had a high reach (35.9%) with the highest frequency of 96.8% of those being likely to

purchase this apple. Other notable results include a 12.5% reach for Apple 93C (a red apple, still

in a breeding program), with the lowest likelihood of purchase at 11.8%. Apple 73C (a popular

red apple, newer to commercialization) had the lowest reach of the group with 7.8%, and Apple

55Cb (a yellow apple, not commonly marketed) which was the most preferred apple in this group

only had a reach of 9.4% and a frequency of 18.5% of these consumers who are likely to

purchase this variety. The familiarity of varieties, as shown by Granny Smith and Gala, appeared

to drive the overall reach of the product, however, because Gala is a red apple, it scored a higher

frequency of being purchased.

54

In Group 2, Gala had the highest reach of 42.1%, with a high likelihood of purchase at a

frequency of 84.4%. Apple 91Cb (a red apple, newer to commercialization) also had a high reach

of 27.1%, with the highest frequency at 92.3%. Apple 55Cb, the most preferred apple in Group 2

when tasted, had a very low reach at 9.3%, and the lowest frequency of 9.1% of these consumers

being likely to purchase the apple.

Finally, results of Group 3 concluded that Granny Smith had the highest reach of 37.3%,

however it had a low frequency of 50.0%. Apple 91Cb had a high reach at 31.4% and a very high

frequency of 94.7%. Apple 55Cb had the lowest reach with 2.0%, and only a 25.0% likelihood

frequency of purchase. A summary of the reach and frequency values for each apple variety can

be found in Table 3.10.

In addition, a list of commonly used consumer-friendly attributes was given to the

consumers, and they were asked to identify which qualities of an apple that lead to a purchasing

decision. These results showed that apples that were healthy looking, red, vibrant, familiar, and

symmetrical were found to be acceptable by all consumer groups. Results also showed that

Group 1 was most accepting of larger apples (30% approval; Group 2 = 17% approval, Group 3

= 25% approval), Group 2 was not as accepting of green apples (18% approval; Group 1 = 30%

approval, Group 3 = 29% approval), and Group 3 was most receptive to apples that were

multicolored (35% approval; Group 1 = 27% approval, Group 2 = 28% approval). Attributes

leading to the least likely to purchase apples were universally agreed upon as unhealthy,

irregularly shaped apples. Group 1 was most accepting of apples that are yellow (20%

disapproval; Group 2 = 30% disapproval, Group 3 = 30% disapproval), as well as unfamiliar

apples (19% disapproval; Group 2 = 32% disapproval, Group 3 = 25% disapproval), and was not

receptive to apples that were too large (20% disapproval; Group 2 = 9% disapproval, Group 3 =

8% disapproval). Consistent with Group 2 was their lack of acceptance of green apples (23%

disapproval; Group 1 = 9% disapproval, Group 3 = 12% disapproval). A full summary of the

visual preference evaluation can be found in Table 3.11.

55

Table 3.11 List of the characteristics defined by consumers in the visual evaluation that would

make them most likely or least likely to purchase an apple variety, expressed as percentages for

each term.

Attribute Most likely to purchase Least likely to purchase

Group

1 (%)

Group

2 (%)

Group

3 (%)

Group

1 (%)

Group

2 (%)

Group

3 (%)

Bland/dull 57.8 65.4 58.8

Familiar 56.3 58.9 45.1 4.7 4.7 3.9

Green 29.7 17.8 29.4 9.4 23.4 11.8

Healthy 81.3 71.0 66.7

Irregularly shaped 3.1 2.8 0.0 39.1 29.9 23.5

Large 29.7 16.8 25.5 20.3 9.3 7.8

Multi-colored 26.6 28.0 35.3 18.8 19.6 21.6

Red 60.9 72.0 60.8 6.3 6.5 7.8

Small 10.9 16.8 15.7 10.9 12.1 15.7

Symmetrical 43.8 35.5 37.3 7.8 7.5 3.9

Unfamiliar 0.0 0.9 0.0 18.8 31.8 25.5

Vibrant 67.2 46.7 51.0

Yellow 14.1 18.7 13.7 20.3 52.3 56.9

3.4 Discussion

3.4.1 Understanding taste and flavor profiles of apples

This research hypothesized that key flavor attributes exist within apple varieties that are

responsible for driving consumer preference. To test this hypothesis, this study first determined

the attributes associated with different apple varieties through sensory DA. Results of DA

showed all attributes having a significant product effect (apart from hay in Year 2), indicating

that the trained panel was able to use the sensory lexicon to discriminate among the flavor

properties of each apple variety. Panel performance metrics were also assessed, and any problem

variables were further investigated for inclusion following the methods of Cliff et al. (2016).

Overall, it was found that the identification and removal of outliers did not influence the overall

product effect for these variables, and therefore all information remained in the dataset.

To further understand the relationships of these flavor attributes and their contributions to

taste and flavor perception within different apple varieties, the sensory maps created by PCA

56

from both years were investigated. The PCA of Year 1 showed four significantly contributing

factors. Factor 2 (29.8% of variation) and Factor 3 (15.8% of variation) were defined as

predominantly texture-loaded factors and accounted for 45.6% of the total variability within the

model. Alternatively, Factor 1 (22.8% of variation) and Factor 4 (16.3% of variation) were both

related to taste and flavor, accounting for approximately 39.2% of the variability within the

model (See Figure 3.1). Based on this large amount of variability being represented by taste and

flavor, this proves the importance of these sensory characteristics in achieving a complete

understanding of an apple variety. Observations of the PCA for Year 2 did not clearly delineate

between texture and flavor, as these models were represented by three factors with a combination

of texture, taste, and flavor attributes on each. It is speculated that this is due to the selection of

apples in Year 2, as they were chosen based on their differences in aroma volatile and texture

differences. The attributes positively correlated to Factor 1 (49.1% of variation; skin thickness,

chewy, acid, bitter, astringent, grassy/vegetal) of the PCA were later shown to be negative

drivers of preference based on the consumer evaluation. Additionally, the negatively correlated

attributes on Factor 1 (sweet, honey, floral) were all positive preference drivers in the consumer

evaluation, and further signifies the importance of this factor when describing the sensorial

composition of these apple varieties from a consumer liking perspective. Factor 2 (25.1% of

variation) of the Year 2 PCA showed a positive correlation to the attributes of astringent, juicy,

and acid. While not a focus of the present study, these results are consistent with previous

literature (Daillant-Spinnler et al., 1996; Bowen et al., 2018) who have identified the importance

of a secondary group of consumers who prefer juicy and acidic apples. Lastly, Factor 3 (12.6%

of variation) represented the attributes chewy and overall aromatic intensity in the positive

direction. With this information, the results of Year 2 can be simplified as Factor 1 representing

the positive and negative preference drivers of apple taste and flavor, and Factor 2 explaining

apples outside of the targeted “Apple Sweet Spot” which are preferred by a secondary group of

consumers who are not the target of this study (Bowen et al., 2018). The interpretation of results

from Factor 3 of the PCA are less clear, as this factor is also influenced in the positive direction

by honey, floral, and sweet, albeit to a lesser degree. Therefore, further investigation of the

attributes overall aromatic intensity and chewy may be required to determine their impact on

consumer liking. Additionally, a further understanding of the complexity of chewy is required as

it is influencing both Factors 2 and 3, which are linked to negative preference drivers and

57

positive preference drivers, respectively. The key factors representing flavor can be seen in

Figure 3.2.

In both years, apples were grouped into four clusters by AHC based on differences in

sensory profiles. When interpreting the results, apples were clearly separated by either their

differences in texture, or differences in taste and flavor. Apples primarily characterized by their

textural attributes can be seen in Groups B (n=10) and C (n=5) in Year 1, and Group B (n=9) in

Year 2 (See Section 3.2). The remaining groups were primarily described by their taste and

flavor profiles. In Year 1, Group A (n=8) and Group D (n=5) were found to have differing

profiles, with Group A having typically low lemony and grassy/vegetal aromas with low acid,

bitter, and astringent tastes while Group D had the highest intensities of grassy/vegetal aroma, as

well as overall aromatic intensity aromas, and low sweetness levels. Furthermore, when

segmenting apples by AHC in Year 2, Group A (n=10) consisted of apples high in honey, overall

aromatic intensity, and floral aromas with high sweet and low astringent and acid

tastes/mouthfeels. Group C (n=4) was the opposite of Group A, having low honey aroma and

sweet taste, with high acid and astringent tastes. Apples within Group D (n=4) were categorized

based on their low overall aromatic intensity and floral aromas, and acid taste. Based on the

established understanding of consumer preference among texture and taste attributes (Daillant-

Spinnler et al., 1996; Cliff et al., 2016; Bowen et al., 2018), it is therefore important to explore

the distinguishing characteristics of apple volatiles that will ultimately play a role in the

differentiation of apple flavor. By focusing on these properties, this research paper will therefore

place a lesser importance on the understanding of texture. Kim (2020) conducted a similar study

in parallel to this research focusing on texture qualities of the same sub-set of apples.

Additionally, we will still be considering the taste and mouthfeel attributes, as these can be

manipulated by flavor volatiles in a process known as odor-induced enhancement of taste

perception (Aprea et al., 2017). It is expected that this process will lead to a further

understanding of apple flavor, and which flavors are necessary to make an apple that is highly

liked by consumers.

58

3.4.2 Consumer preference and ideal apples

The next objective was to determine which apple varieties consumers prefer and to find

out what they would classify as their ideal apple. As the consumer evaluation results have

indicated, this research was able to identify three primary consumer groups. Although all

consumers were screened based on their consumption of apples prior to participating, consumers

in Group 3 (n=51) were found to dislike the majority of apples within the study. This group

provided no predictable differences among their liking scores, which ranged from a mere 39.2 to

54.9 out of 100 (See Table 3.6). It is expected that these consumers either fit into the smaller

consumer segment outlined by Bowen et al. (2018) which represented 11% of the population

who preferred acidic apples, that consumers in this group purchased apples for other purposes

(ex. juices, ciders, sauces, etc.), or that these consumers do not like eating whole apples. Thus,

this discussion section will primarily focus on the profiles of consumer Groups 1 (n=65) and 2

(n=110) that showed liking differences for the apples.

For Group 1 (n=65) liking was found to be heavily driven by texture. Preference among

these consumers was driven by the crisp attribute, then followed by sweet, honey, floral, and

juicy. This group was found to not dislike any apples within the selected varieties taken to

consumer testing, as their liking scores ranged from 47.6 to 76.5 out of a possible 100 (See Table

3.6). These high ratings were expected as many of the varieties evaluated were selected based on

their placement within the “Apple Sweet Spot” (Bowen et al., 2018). By using varieties in this

zone, we know that the texture of the tested varieties should fulfill the desired profiles of this

consumer group as previous results found that most consumers liked apples with crisp and juicy

characteristics (Bowen et al., 2018). Group 2 (n=110) was primarily driven by sweet taste within

the varieties with honey and floral flavors complementing this profile. Apple varieties that did

not meet the desired sweet and flavorful profile for this consumer group were reflected by lower

liking scores when sweetness intensity decreased. Scores ranged from as low as 23.5 to 73.4 out

of 100 (See Table 3.6).

When consumers were asked to describe their ideal apple after the tasting session, the

descriptors they provided matched their identified preference from the tasting experiment. For

example, the preference for consumers in Group 2 was found to be heavily driven by apples that

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were described as sweet by DA, and at the same time this group listed the sweet attribute as their

highest ranked must have attribute. However, as shown by the results from the visual evaluation

test, this may not always translate when deciding on the purchase of apples, as seen with the

highest predicted liking of an apple having the lowest visual acceptance of the group, and some

of the most recognizable varieties (ex. Granny Smith, Gala) having high reach and frequency

(See Section 3.3.8). All apples were presented blind in this study, meaning that visual familiarity

and recognition may have been impacted as consumers are known to make purchase decisions

based on their previous experiences and expectations with familiar variety names (Yue and

Tong, 2011). This is further shown with varieties that are either newer to the market or not yet

available commercially as some of these have much lower reach and frequency in comparison,

while having higher predicted liking among consumers. All three groups agreed with the fact that

apples must have a thin skin with a flavorful, sweet, crisp, and juicy profile. In addition, Group 1

was found to value crisp and juicy higher than the other two groups, consistent with the findings

of the preference map, and Group 2 was found to have a strong correlation with sweet, further

cementing the evidence suggesting that preference for this consumer group is heavily driven by

sweet taste and volatiles that enhance sweetness perception. A point of interest within consumer

Groups 1 and 2 is that Group 1 identifies an ideal apple as first being flavorful, surpassing their

appreciation of texture attributes. Group 2 values flavorful apples very highly, ranking second

only to sweetness. These results show the value of flavor across both consumer groups and

signify the importance of delving deeper into what makes an ideal and “flavorful” apple going

forward. Additionally, as the term flavorful is subjective and encompassing to all flavors, it is

important to pair this information with our DA results, allowing for the determination of which

flavors being portrayed in an ideal sense. To help pinpoint the definition of flavorful for each

consumer group, an ideal apple for each group can be projected onto the preference map,

indicating the respective flavors contributing to preference.

3.4.3 Generation of a preference map

The creation of a preference map through the combination of consumer liking data and

the sensory map from the subset of apples taken to the consumer evaluation allowed for the

further clarification of the second objective and the completion of the third objective by first

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identifying which apple varieties consumers prefer, and then identifying the key flavor attributes

that can be used as predictors of consumer preference.

When the general preference for consumer Groups 1 and 2 were imposed onto the

preference map, preference for both groups was loaded towards the negative direction on Factor

1 (taste/flavor), the positive direction on Factor 2 (texture), and differed on Factor 3. This

preference map showed that Factor 1 (41.8% of variation) accounted for the largest amount of

variability, and was defined by lemony, grassy/vegetal, acid, bitter, astringent, and chewy in the

positive direction (least preferred) on the axis, and honey, floral, and sweet in the negative

direction (most preferred). Interestingly, Factor 1 was primarily represented by taste and flavor

sensory attributes (excluding chewy). Factor 2 (28.1% of variation) was described in the positive

direction on the axis by crisp and juicy (most preferred), with skin thickness and mealy going in

the negative direction (least preferred). Consistent with previously generated preference maps

(Daillant-Spinnler et al., 1996; Bowen et al., 2018), these results were represented by the most

important texture attributes in terms of consumer preference. Factor 3 (12.5% of variation)

explained rate of melt and oxidized apple in the positive direction, with crisp being positioned

negatively along the axis. However, preference along Factor 3 is not as conclusive as Factors 1

and 2. With crisp influencing the direction of Factors 2 and 3, we can interpret this as a complex

sensory attribute. This is an especially important finding, as we know that consumers in Group 1

are primarily driven by crisp. However, due to the scope of this research study, the multi-

dimensionality of crisp was not investigated further (see Kim 2020).

In conjunction with Section 3.4.3, and most relevant to this research and the defining

properties of a flavorful apple, preference on Factor 1 is shown to be moving in the direction of

sweet, honey, and floral attributes while moving away from lemony, grassy/vegetal, acid,

astringent, bitter, overall aromatic intensity, and hay. This appears to be the case for both

consumer groups and helps to prove the importance of these flavor attributes in relation to

consumer preference. When cross-referencing this to Section 3.4.3, we can conclude that the

ideal flavors within our defined flavorful term would be comprised of the honey and floral

attributes. Factor 2 was most represented by texture attributes, and thus will not be discussed

further as it outside of the scope of this research project. Factor 3 was represented by oxidized

apple aroma in the positive direction along with rate of melt. On this third factor, Group 1 was

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heavily influenced by crisp in the negative direction, while Group 2 stayed almost neutral.

Consumers in Group 2 do value texture qualities, but once the juicy and crisp textures have

reached a sufficient intensity, they are then focused on the taste and flavor of the apple. In terms

of flavor, both groups were influenced in the opposite direction of oxidized apple aroma,

suggesting that this is a detractor of preference. Based on these results, it is important in future

flavor research to target aroma volatiles that contribute to the honey and floral perceptions within

apples while avoiding compounds contributing to the lemony, grassy/vegetal, overall aromatic

intensity, hay, and oxidized apple aromas. By further identifying compounds responsible for

these liked and disliked flavor characteristics, it is possible to develop apple varieties tailored to

the desires of a consumer, providing future breeding programs with clarity towards what will

make a highly competitive apple on the market, and potentially replace existing “good” varieties,

with “great” new varieties based on these subtle differences.

3.5 Conclusions

The rationale for this research study was to build on previous information from Bowen et al.

(2018), who showed the effectiveness of combining sensory and consumer evaluations to create

an external preference map. Results from Bowen et al. (2018) were used to preface this current

experiment, primarily using apple varieties from the “Apple Sweet Spot” to identify a hole in the

market when it comes to key taste and flavor attributes among apple varieties, to determine if

consumer liking segments exist amongst the most liked apples and what sensory attributes define

them.

By applying DA across two consecutive growing seasons, the sensory characteristics of 27

(Year 1) and 28 (Year 2) varieties were quantified based on their intensity ratings and enabled

the distinction of these taste and flavor characteristics among the varieties. This allowed for the

completion of our first objective, determining which flavor attributes are associated with

different apple varieties by showing that oxidized apple, earthy, hay, honey, lemony, floral,

grassy/vegetal, and overall aromatic intensity are all contributing to the flavor profiles of apples

within this study. In terms of our second objective, hedonic testing was completed by consumers

who regularly purchase fresh market apples. For varieties already known to be highly preferred

by consumers, this research was able to conclude that many subtle differences still existed

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among these top varieties. Consumers divide into two liking groups, with the largest groups

(49%) liking apples with sweet taste, and honey and floral flavors which allowed these varieties

to excel in comparison with other high-performing apples (when paired with crisp and juicy

textures). Thus, honey and floral flavors were shown to be drivers of liking and provided a

definition of the term “flavorful” used by the consumers when describing their ideal apples and

can serve as a target for future volatile chemical analysis.

Additionally, the second part of this research objective was completed by allowing the

consumers to respond through a questionnaire with what they envision as an ideal apple.

Consumers in Group 1 listed their ideal apple as being flavorful with juicy and crisp texture

properties, while consumers in Group 2 primarily wanted sweet and flavorful apples, with

texture not playing as important of a role in their definition of ideal. Our last objective, being

able to identify which flavor attributes can be used as potential predictors of consumer

preference was completed through a combination of sensory DA, a consumer hedonic evaluation,

and the formation of an external preference map. With this information, we can confirm our

hypothesis by highlighting honey and floral to be the key preference drivers of flavor when

paired with sweet, and the key detractors of preference to be the lemony, grassy/vegetal, overall

aromatic intensity, hay, and oxidized apple aromas when paired with acid, bitter, or astringency.

The future direction of this research is to integrate the results of this study into the apple

breeding program at Vineland and use the preference mapping tool to identify apples to advance

and commercialize with the ultimate goal of developing new consumer driven apples varieties

for the Ontario apple industry. This may include testing apple varieties currently on the market,

or varieties created within apple breeding programs. It may also serve to identify holes in the

industry such as an apple that targets the desires of a large proportion of the population or

targeting a niche segment that is untouched by other varieties. This can all be done by driving a

consumer-centric approach to the breeding of future apple varieties. Furthermore, the

methodology used in the present study can be used in other research programs globally to help

identify characteristics of apples, or other horticultural products, that consumers are seeking in

their local area. Finally, it is necessary to further develop an understanding of these key flavor

properties by understanding the volatile composition that is creating the aroma/flavor profiles

responsible for driving and detracting consumer preference. Based on results from this study,

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future research should focus on the identification of volatiles leading to attributes associated

positively with liking (honey, floral), while avoiding attributes negatively associated with

preference (lemony, grassy/vegetal, overall aromatic intensity, hay, and oxidized apple). Thus,

this research will play a pivotal a role in future breeding programs and the evolution of the fresh

apple market by allowing new varieties tailored to the desires of consumers onto the market

using a consumer-driven approach.

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4 Implementation of Aroma Volatile and Physicochemical

Measurement Techniques for the Determination of Flavor

Properties in Apple Fruit

This chapter is intended to be submitted to the Journal of Agricultural and Food Chemistry and

has been adapted for this thesis.

Jordan R. MacKenziea,b, Lisa M. Duizera, David K. Liscombeb, Amy J. Bowenb

a Department of Food Science, University of Guelph, Guelph, ON, Canada

b Vineland Research and Innovation Centre, Vineland, ON, Canada

Author MacKenzie conducted the research, analyzed the data, and wrote the manuscript. Author

Duizer reviewed and edited the manuscript. Author Liscombe oversaw the work and reviewed

and edited the manuscript. Author Bowen received funding for the project, oversaw the work,

and reviewed and edited the manuscript.

Abstract

A recent shift within the apple industry has shown the role that flavor plays in consumer

satisfaction. This research investigates instrumental methods of GC-MS and physicochemical

techniques (i.e. pH, titratable acidity, and soluble solids content) to identify taste and flavor

properties within apples. Apples varieties (n=27, n=29) were tested across two subsequent years

with volatiles (n=40) measured through GC-MS and physicochemical testing conducted on each

variety. Results were paired with sensory DA intensity ratings using a lexicon of taste and flavor

attributes (n=12). Ultimately, results revealed that positive preference drivers of sweet taste and

honey flavor can be linked to the pH, acetate, hexyl, and butyl esters, while floral flavor can be

linked to acetate esters and pH. Additionally, negative preference drivers within apples can be

linked to ethyl, propyl, and acetate esters, medium-chain aldehydes, fatty alcohols, primary

alcohols, ketones, and sesquiterpenoids as well as titratable acidity and TA/°Brix ratio.

Practical applications

Results of this research will allow apple growers and breeding programs to use sensory

and instrumental testing protocols to identify desirable characteristics within their fruit by using

the information to screen new and current apple varieties, thus allowing an understanding of how

the apple will perform on the market.

Keywords:

Descriptive analysis; physicochemical; volatile; flavor; apples

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4.1 Introduction

Apples are a complex fruit with many unique taste, texture, and flavor properties. Recent

technological advancements have allowed for the texture of apples to be consistently maintained

at a desirable level to consumers (Yahia, 1994; Ting et al., 2015). While these technologies may

be beneficial for the maintenance of firm and juicy textures, they may also be detrimental to the

production of volatile chemicals responsible for the creation of the intrinsic aromas of the apple,

thus leading to undesirable flavor outcomes (Ting et al., 2015). To allow for the optimization of

apple flavor within commercially available varieties, it is necessary to understand the flavors in

which consumers desire and use these characteristics to guide apple breeding programs to create

a flavorful apple fruit. To achieve this, a combination of sensory DA to determine the intensity of

each sensory attribute, VOC analysis to identify and quantify present chemical compounds

within the apple fruit through GC-MS, and physicochemical analyses (i.e. pH, °Brix, TA, and

TA/°Brix ratio) to measure the sweetness and acidity of the apple fruit is necessary.

Sensory analysis, and specifically DA, is considered the gold standard in measuring and

identifying perceptible taste and flavor characteristics of food products (Yahia, 1994; Ting et al.,

2015). A trained DA panel allows for an objective evaluation of a sensory lexicon containing

taste and aroma/flavor descriptors. Conclusions can be drawn from these results through an

ANOVA, showing whether true differences exist among the products for evaluated

characteristics (Du et al., 2010; Lignou et al., 2014; Ting et al., 2015; Kim et al., 2018; Bowen et

al., 2018; MacKenzie et al., unpublished).

In addition, multivariate statistics allow this data to be used alongside instrumental data

(ex. VOCs or physicochemical) via PCA (Lignou et al. 2014; Kim et al., 2018; Bowen et al.,

2018; MacKenzie et al., unpublished), GPA (Du et al., 2010), or MFA (Lignou et al., 2014; Ting

et al., 2015). These statistical methods are used to showcase the complexity of the product and

increase the amount of predictable variation by the generated model by identifying correlations

among variables. For this research, DA will be used to identify the flavor characteristics that

differentiate each individual apple variety. This information will be cross-referenced with

information from Vineland, who have shown with recently published research by Bowen et al.

(2018) that preference among apple varieties can be divided into two groups of consumers:

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Group 1 which represented 89% of the tested population liked apples with sweet taste, crisp and

juicy textures, and a fresh red apple aroma/flavor. Apple varieties falling within this zone are

classified as being in the “Apple Sweet Spot”, and thereby satisfy the liking needs of the

majority of the tested consumer groups. Group 2, representing 11% of the population preferred

apples with an acidic taste, crisp and juicy textures, and a fresh green apple aroma/flavor. Most

recently, MacKenzie et al. (unpublished) conducted a complementary study to this research to

further clarify apple varieties and sensory attributes with connections to consumer liking within

the defined Apple Sweet Spot. This research showed similarities on the basis of consumer liking,

where the tested population was divided into three consumer groups based on their preferences.

Group 1 (29% of the population) was primarily driven by the texture of the apple, with sweet

taste and honey and floral flavors coming as secondary/tertiary desires. Group 2 (49% of the

population) was primarily driven by sweet taste and honey and floral flavors. In this group, the

importance of texture qualities came as secondary preference drivers, if the intensities of texture

attributes allowed for the apple to be crisp, juicy, and not mealy. This research also identified a

third group (23% of the population) who regularly purchase apples but did not show any

indicators of preference. Interestingly, when the consumers in this study were asked about their

ideal apple, all three consumer groups identified the term “flavorful” as being the most important

driver of consumer liking. This research is similar to other studies highlighting the importance of

sweet and acidic tastes aligning with preference, as well as the preferred texture profiles of crisp

and juicy (Daillant-Spinnler et al., 1996; Symoneaux et al., 2012). However, the least understood

part of the puzzle remains as to which flavor properties are responsible for driving consumer

liking.

To fully understand the flavor characteristics of apples, it is necessary to evaluate an

apple variety at a chemical level. However, this is a difficult process as apples are complex fruit,

composed of over 300 identified VOCs (Dixon and Hewett, 2000). Based on research from

MacKenzie et al. (unpublished), which ran parallel to this experiment, it is now understood that

consumer liking is driven by honey and floral aromas, while grassy/vegetal, overall aromatic

intensity, hay, and oxidized apple aromas serve as detractors of consumer liking. The present

research uses GC-MS to evaluate the concentration of 40 pre-determined VOCs that may be

responsible for some of these attractors or detractors of liking. These VOCs have been selected

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based on previous literature of apple biochemistry, as well as preliminary testing to identify

compounds to include in the study. Additionally, the physicochemical properties of each variety

were collected to help further define the relationship between the chemical composition of the

apple and its respective taste/flavor.

As shown in MacKenzie et al. (unpublished), extrinsic factors (ex. appearance,

demographic, etc.) are not reliable indicators for consumer preference when compared to

intrinsic factors (ex. sensory attributes). Similarly, it is expected that additional intrinsic

properties such as VOCs or physicochemical qualities will lead to further discovery of impactful

flavors in relation to consumer liking and can serve a purpose within the apple industry to

identify potential chemical-breeding targets.

The aim of the current research was to determine the VOCs or other instrumental

measurements (ex. physicochemical data) that were related to both consumer liking and

disliking, to ultimately aid in the selection of high-quality apples to market competitively. The

hypothesis of this research was that key aroma volatiles and physicochemical properties exist

which are responsible for the creation of unique flavor perception and can be linked to consumer

liking. The identification of these VOC targets will decrease the need to run extensive sensory

and consumer research trials, as these are expensive, time-consuming, and labor-intensive

(Murray et al., 2001).

To achieve this hypothesis, two objectives were identified. First, VOCs responsible for

the creation of flavor perceptions in relation to consumer liking were identified and measured.

Second, other instrumental measurements (ex. physicochemical analyses) were used to provide

any additional insight into the variability among taste/flavor perceptions. These objectives will

be paired with prior results from MacKenzie et al. (unpublished) which had identified positive

and negative taste and flavor preference drivers in relation to consumer liking.

4.2 Materials and methods

The present experiment served as a complementary study to the data outlined in Chapter

3.

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4.2.1 Products

Apple varieties were sourced locally from the OAG (St. Catharines, Canada) whenever

possible. Additional apples were provided through Vineland and other Canadian breeding

programs to help identify the characteristics of novel apple varieties. When Ontario-grown

resources were unavailable, apples were sourced from local grocery retail.

Selection of apple varieties occurred across three experimental years. In the first year

(2017-2018), apples were chosen by incorporating varieties that represented a large segment of

the Ontario market share, leading apple varieties in Canadian apple breeding programs, or results

of previous research conducted by Bowen et al. (2018). Data collected in Year 1 was used to

identify which VOCs were present in apples and to refine method development. Apples tested in

Year 2 (n=27; 2018-2019) and Year 3 (n=29; 2019-2020) were selected based on results of Year

1. Results in Year 2 were used for further refinement of apples for testing in Year 3. Additional

apples tested included varieties (n=2) from Canadian breeding programs to provide insight into

their respective flavor characteristics, and in Year 3 a group of top-performing varieties derived

from the Vineland apple breeding program (n=5).

Apples delivered to Vineland were sorted and stored in a designated apple cooler. Apples

with visual defects (ex. bruising, injury, mold, etc.) were discarded, and the remaining apples

were placed into a plastic storage container as a single layer. The temperature of the cooler was

maintained at 2-4°C through room cooling as defined by Boyette et al. (1990).

4.2.2 Maturity determination, handling, and storage

Apple maturity and handling were consistent with the previously reported data from

MacKenzie et al. (unpublished) and relied on the SI index as described by Blanpied and Silsby

(1992). The SI index was routinely monitored and once an apple variety had reached its ideal

maturity, sensory and instrumental testing ensued. Additional handling parameters were used to

allow for volatile collection and for the analysis of the physicochemical properties of each

variety.

For handling of apples used in volatile collection, each apple was first cut into small

squares (skin on, excluding core) and weighed to a total of 100 g on a Quintix® scale (Sartorius,

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Germany). Then, samples were loaded into a glass tube and capped on each side to entrap any of

the released volatiles within the tube. This testing procedure was completed in quadruplicate for

each unique apple variety.

Handling of apples for the purpose of physicochemical analyses (ex. pH, °Brix, TA,

TA/°Brix) involved cutting the apple (skin on, excluding core) into pieces, and then placing

these into a HealthSmart® Juice Extractor (Hamilton Beach, USA) where juice was separated

from the solid content. This was completed in triplicate for each variety. For each individual

apple sample, the juice was poured into a 50 mL polypropylene tube (Fisher Scientific, USA),

labelled, and then centrifuged at 4700 rpm for 10 minutes using a Fiberlite™ F13-14 x 50cy

fixed angle rotor (Thermo Scientific, USA) and Sorvall™ Legend™ X1 centrifuge (Thermo

Scientific, USA). The supernatant (the clear liquid derived from the separation of solids and

liquids) of this juice was removed and placed into two 15 mL polypropylene tubes (Fisher

Scientific, USA), where each of the three replications were later tested in duplicate. At the time

of extraction, pH, and SSC measured in °Brix were both measured and recorded. The juice

samples were then frozen in a Forma™ -40°C Lab Freezer (Thermo Scientific, USA) for future

measurements of TA.

4.2.3 Aroma volatile collection and analysis by GC-MS

Aroma volatiles from fresh apples were collected, concentrated, and analyzed by GC-MS

as described for fresh nectarines by Kumar et al. (2020), with modifications to focus on apples.

Volatile compounds were identified by comparison to authentic standards listed in Table 4.1.

Table 4.1 Volatile organic compound list grouped based on chemical structure.

Compound group Volatile organic compound

Anisoles Estragole

Acetate esters 2-methylbutyl acetate

5-hexene-1-ol, acetate

Butyl acetate

Hexyl acetate

2-methylpropyl acetate

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Acetate esters (continued) Isopentyl acetate

Propyl acetate

Pentyl acetate

Z-2-hexen-1-ol, acetate

Methyl esters Methyl butyrate

Ethyl esters Ethyl propionate

Ethyl butyrate

Ethyl hexanoate

Propyl esters Isopropyl butyrate

2-methylpropyl butanoate

Propyl propionate

Propyl butyrate

Propyl hexanoate

Butyl esters Butyl 2-methylbutanoate

Butyl butyrate

Butyl propionate

Butyl octanoate

Butyl hexanoate

Amyl esters 3-methylbutyl hexanoate

Hexyl esters Hexyl 2-methylbutanoate

Hexyl hexanoate

Hexyl octanoate

Hexyl propionate

Fatty alcohols 1-hexanol

(R)-Sulcatol

3-hexen-1-ol

Ketones Sulcatone

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Medium-chain aldehydes 2-Hexenal

Hexanal

(Z)-3-Hexenal

Primary alcohols 1-butanol

1-pentanol

(S)-2-methyl-1-butanol

Sesquiterpenoids α-farnesene

4.2.4 Physicochemical evaluation

To gather other instrumental measurements on the apple varieties, a combination of pH,

°Brix, and TA analyses were conducted on each variety.

The pH was measured using an Accumet Basic pH meter (Fisher Scientific) and Accumet

probe (Fisher Scientific). Prior to evaluation, three standard buffer solutions were used (pH 4.00,

pH 7.00, pH 10.00; Fisher Scientific) to allow for calibration of the pH meter, thus generating

accurate measurements for each sample. Once this was complete, the probe was placed into the

apple juice, allowed to stabilize, and the value from the pH meter was recorded. Before moving

to the next sample, the probe was cleaned with MilliQ (MQ; MilliporeSigma, USA) water and

dried with a Kimwipe (Kimberley Science, USA). This was completed in triplicate for each

variety.

The SSC was measured and expressed as °Brix. Evaluation of each sample was

conducted in triplicate, using an Atago Pocket Refractometer (PAL-1; Atago, Japan). To perform

this analysis, 300 μL of apple juice was pipetted onto the lens of the refractometer and then

measured. Results were recorded and then the device was cleaned using MQ water

(MilliporeSigma) and a Kimwipe (Kimberley Science).

Titratable acidity was measured using an 848 Titrino plus (Metrohm, Switzerland) with

an 801 Stirrer attachment (Metrohm). Apple juice was thawed prior to evaluation and left at

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room temperature. Titrations were conducted creating a solution of 2 mL of apple juice sample

mixed with 50 mL MilliQ water (MilliporeSigma), then 0.1 N NaOH (Fisher Scientific) as a

titrant to reach an endpoint of pH 8.2 in the sample. Results were calculated using an established

equation from Nielsen (2019; see below) and expressed as g/L of malic acid. After each titration

was complete, the pH electrode was rinsed with MQ water (MilliporeSigma) and gently dried

using a clean Kimwipe (Kimberley Clark). The following equation was extracted from Fisher

Scientific Application Note 010 (2014) and was adapted to suit the malic acid milliequivalence,

as well as the %Acid being converted to g/L of acid by multiplying by a factor of ten. This

equation was then used to calculate the acid concentration for each apple juice sample:

%𝐴𝑐𝑖𝑑 =𝑚𝐿𝑠 𝑁𝑎𝑂𝐻 ∗ 0.1𝑁 ∗ 0.067 ∗ 100

2𝑔 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒

4.2.5 Trained sensory panel evaluation

Sensory panel evaluation data was collected using the DA method. These methods were

conducted at Vineland using their employed and trained in-house sensory panel. Details of the

training and purpose of this trained sensory panel can be found in MacKenzie et al.

(unpublished). Sensory attributes were used as part of an established 18-attribute lexicon which

included taste, flavor, and texture attributes. For the purpose of this study, only the

taste/mouthfeel (n=4) and aroma/flavor attributes (n=8) were taken into consideration. These

included sweet, acid, bitter, and astringent as tastes/mouthfeels, and oxidized apple, earthy, hay,

honey, lemony, floral, grassy/vegetal, and overall aromatic intensity for aroma/flavors.

In Year 2, apple varieties (n=28) were evaluated in eight individual 1.5-hour DA sessions

by the panelists (n=15, average). These sessions took place throughout the apple season, starting

in October 2018 and continuing until January 2019. Selection and profiling date of each apple

variety for DA was based on the maturity of each variety, as described in Section 4.2.2.

Similarly, in Year 3, DA was conducted across seven individual 1.5-hour sessions. Apple

varieties (n=29) were evaluated by the Vineland trained sensory panel (n=14, average). Sessions

in Year 3 took place from October 2019 to January 2020. The sensory DA data collected in Year

1 was not included in this study.

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A taste and aroma reference tray was provided to each panelist at the beginning of each

session to help calibrate their senses prior to evaluation. This reference tray included a reference

standard for each attribute used in the sensory lexicon with a standardized recipe that can be

found in Table 4.2.

Full testing procedures can be found in MacKenzie et al. (unpublished), with details

outlining the preparation protocols, room specifications, and tasting procedure. All samples were

tested in duplicate and presented in a randomized balanced design. Intensity measurements were

recorded by panel members using EyeQuestion software (Logic8, Netherlands). This software

allowed for panelists to rate the intensity of each sensory attribute using a 15 cm line scale with

anchors of “weak” denoted at the 10% mark of the scale, and “intense” at the 90% position of the

scale.

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Table 4.2 Basic taste, mouthfeel, and aroma reference tray standards with recipes.

Reference Preparation Method

Sweet 6.0 g sucrose + 400 mL applesauce†

Acid 1.0 g malic acid + 400 mL applesauce†

Bitter 0.10 g caffeine + 400 mL applesauce†

Astringent 0.90 g Kalum (potassium aluminum sulphate dodecahydrate) +

400 mL applesauce†

Earthy 18 µL earthy (#11)‡ + 400 mL applesauce†

Honey 20 g honey§ + 400 mL applesauce†

Grassy/vegetal ½ pot cat grass + 600 mL filtered water

Soak 30 minutes, filter, + 1 mL ‘Green’ solution¶ + 400 mL

filtered water

Oxidized apple Cut one Red Delicious apple, allowing to oxidize for 30 minutes

Hay 270 µL hay (#38) ‡ + 600 mL filtered water

Floral 10 mL rose water‖ + 800 mL filtered water

Lemony 360 µL lemon extract + 800 mL filtered water

Overall aromatic intensity 40 mL applesauce†

† Mott’s Fruitsations unsweetened applesauce ‡ Le nez du vin “The Masterkit 54 aromas” § BillyBee pure natural pasteurized honey ¶ 500 mL filtered water, 9 µL green pepper (#30)‡ ‖ Cortas rose water

4.2.6 Data organization and statistical analyses

Sensory data was extracted from EyeQuestion (Logic8, Netherlands) software and

exported into Microsoft Excel (Microsoft, USA). The mean intensity values of each unique taste

and flavor sensory attributes (n=12; oxidized apple, earthy, hay, honey, lemony, floral,

grassy/vegetal, overall aromatic intensity, sweet, acid, bitter, astringent) were assigned to each

corresponding apple variety in Years 2 and 3.

For analysis of the volatile data collected using GC-MS, as certain aromas are produced

based on a cocktail of chemical compounds, it was decided to group each of the aroma VOCs

75

based on their chemical composition instead of as individual chemicals. With reference to The

Human Metabolome Database (See hmdb.ca), the 40 individual compounds were split into 13

unique groups as outlined in Table 4.1. These included anisoles (n=1), acetate esters (n=9),

methyl esters (n=1), ethyl esters (n=3), propyl esters (n=5), butyl esters (n=5), amyl esters (n=1),

hexyl esters (n=4), fatty alcohols (n=3), ketones (n=1), medium-chain aldehydes (n=3), primary

alcohols (n=3), and sesquiterpenoids (n=1).

Prior to VOC analysis, the ion chromatogram generated in the MSWS 8 Bruker GC/MS

software (Scion Instruments) was observed. The first check in the process was to ensure that a

nonyl acetate peak (used to standardize the data) was present at a concentration of 2 ppm. If this

peak was not present, the replication was removed. Start and endpoint integration to measure the

area under the curve was used to calculate the concentration of each VOC in the chromatogram.

Once each concentration was established, VOC data was extracted and the raw data files were

uploaded to the online platform, MetaboAnalyst 4.0 (Chong et al., 2019). The uploaded data was

represented by the concentrations of each VOC and were then normalized by sum. Next, the data

was scaled by mean-centering and divided by the standard deviation of each variable. This

allowed for normalization of the data, and a clearer representation of the relationship of the

VOCs to each other. The final VOC results were represented in relative abundances, as the

relative concentration of each VOC to the total concentration of VOCs on a sample-by-sample

basis. For data analysis, all relative abundances within each VOC group were summed and used

going forward in the statistical analyses.

A one-way ANOVA was used to analyze combined sensory and VOC datasets. All apple

varieties (products) were assigned as qualitative variables, with the sensory attribute intensities

and VOC relative abundances being defined as the quantitative variables. Due to the inability to

measure the sensory traits of the identical apples for each variety, it was decided to use mean

intensity values for each sensory attribute while keeping each replication of a VOC separate for

the ANOVA. This led to no variance in the sensory attributes, and only a measurement of the

variance across VOC groups. In addition to an ANOVA, a normality test (Shapiro-Wilk) and test

of heterogeneity (Levene’s test) were also employed to determine the normality and

homogeneity of each variable. Next, a linear regression was conducted wherein dependent

variables (sensory attributes) were compared with independent variables (VOCs) to discover any

76

meaningful relationships among the datasets. A PCA was used to generate a map of the sensory

attributes and VOC groups. For this test, average intensity ratings of each sensory attribute were

paired with each of the three or four VOC sample replicates, dependent on variety. Results from

this analysis differed across years, and thus will be analyzed separately. In accordance with Mooi

and Sarstedt (2011), acceptable tolerance levels of KMO scores were established to ensure

accuracy of the sampling adequacy of the generated models. Thresholds were applied, sensory

and VOC groups with KMO scores higher than 0.500 were retained, as well as an overall KMO

score threshold of 0.700 was applied to the whole model.

Finally, physicochemical data including the instrumental measurements of pH, °Brix,

TA, and TA/°Brix ratio for each variety were averaged across replications and recorded into

Excel (Microsoft). This physicochemical data was paired with the sensory and VOC data, to

create a dataset encompassing each apple variety with relevant taste/flavor sensory data, relative

abundances of each VOC group, and physicochemical data.

To best understand the relationship between the sensory and VOC data, a GPA was

conducted. For simplicity of the results, and to allow for a clearer picture to be shown, the means

of each dataset were used and assigned to each corresponding apple variety. This analysis was

again divided into two individual years. In addition to this, and to generate a fully encompassing

map of the relationship between sensory, volatile, and physicochemical characteristics, the three

datasets were also combined for use in a MFA. To gather a better understanding of the model

and its relationship between sensory and VOC variables, the mean values for each of the datasets

were used and assigned to their corresponding apple variety, with the physicochemical data

included only as supplementary data. The reasoning for omitting the physicochemical data from

the GPA, and only including the physicochemical data as supplementary data in the MFA can be

found in Section 4.4.2.

Statistical analyses were conducted using XLSTAT (Addinsoft, France) and

MetaboAnalyst software (See metaboanalyst.ca). A significance level of 5% was used across all

tests.

77

4.3 Results

4.3.1 Analysis of variance

In Year 2, a Shapiro-Wilk normality test was conducted which showed that all taste and

flavor sensory attributes (oxidized apple, earthy, hay, honey, floral, lemony, overall aromatic

intensity, grassy/vegetal, sweet, acid, bitter, astringent) and VOC groups (acetate esters, anisoles,

methyl esters, methyl esters, propyl esters, butyl esters, amyl esters, hexyl esters, fatty alcohols,

ketones, medium-chain aldehydes, primary alcohols, and sesquiterpenoids) were not normally

distributed (p<0.05). Additionally, a Levene’s test of homogeneity of variance had shown that

among the VOC groups, acetate esters had homogeneous variances (p<0.057), with the

remaining VOCs having heterogeneous variances (p<0.05).

Results for the normality test differed in Year 3, where the sensory attributes of honey

(p=0.059) and overall aromatic intensity (p=0.061) aromas were found to be normally

distributed, while all other sensory attributes and VOC groups were found to not be normally

distributed (p<0.05). In addition, the Levene’s test of homogeneity of variance showed that all

groups of volatile compounds had homogeneous variances (p>0.05), except for ethyl esters

(p=0.012) which had heterogeneous variances.

In both Year 2 and Year 3, statistically significant product effects existed for all groups of

VOCs (p<0.0001). Therefore, these results prove that true differences exist among the apple

varieties and their respective VOCs.

4.3.2 Regression analysis

Regression analysis results for Years 2 and 3 are shown in Table 4.3. In Year 2,

significant differences (p<0.05) existed across all sensory attributes, apart from acid (p=0.080),

and astringent (p=0.193). When observing each VOC group, significant differences existed for

anisoles (hay, honey, floral, overall aromatic intensity, sweet, bitter), acetate esters (earthy),

methyl esters (grassy/vegetal, bitter), ethyl esters (oxidized apple, earthy), propyl esters (hay,

floral), butyl esters (oxidized apple, overall aromatic intensity), amyl esters (floral), hexyl esters

(earthy), and medium-chain aldehydes (oxidized apple, earthy, hay), primary alcohols (oxidized

78

apple, hay, honey, overall aromatic intensity, sweet), and sesquiterpenoids (oxidized apple,

earthy, grassy/vegetal, overall aromatic intensity). Significant differences (p<0.05) did not exist

for fatty alcohols or ketones.

Results of the linear regression in Year 3 showed that significant differences (p<0.05)

existed across all sensory attributes. Additionally, acetate esters (earthy), methyl esters (oxidized

apple, lemony, overall aromatic intensity, grassy/vegetal, acid, bitter, astringent), ethyl esters

(hay, lemony, acid), propyl esters (oxidized apple, hay), butyl esters (oxidized apple, earthy, hay,

overall aromatic intensity), amyl esters (earthy, hay, overall aromatic intensity), hexyl esters

(oxidized apple, earthy, hay, grassy/vegetal), fatty alcohols (overall aromatic intensity, bitter),

ketones (honey, lemony, sweet, acid), primary alcohols (oxidized apple, honey, floral, lemony,

overall aromatic intensity, grassy/vegetal, sweet, acid, bitter, astringent), and sesquiterpenoids

(earthy, hay, honey, floral, lemony, grassy/vegetal, sweet, acid) were shown to have significant

differences with their corresponding sensory attributes. However, anisoles and medium-chain

aldehydes had no significant differences across their respective sensory attributes.

Table 4.3 Statistically significant sensory attributes across volatile groups from linear regression.

VOC group Sensory attribute Year 2 (2018) Year 3 (2019)

p-value p-value

Anisoles hay 0.001

honey 0.000

floral <0.0001

overall aromatic intensity 0.024

bitter 0.050

sweet 0.003

Acetate esters earthy 0.015 0.039

Methyl esters grassy/vegetal 0.010 0.0132

lemony 0.001


oxidized apple 0.002

acid 0.002

79



p-value p-value

Methyl esters (continued) astringent <0.0001

bitter 0.000 0.013

Ethyl esters earthy 0.050

hay 0.000

lemony 0.039


acid 0.046

Propyl esters floral 0.003

hay 0.031 0.011


Butyl esters earthy <0.0001

hay 0.032

overall aromatic intensity 0.002 0.000

oxidized apple 0.022 0.002

Amyl esters earthy <0.0001

floral 0.040

hay 0.003


Hexyl esters earthy 0.000 <0.0001

grassy/vegetal 0.001

hay 0.003


Fatty alcohols overall aromatic intensity 0.006

bitter 0.035

Ketones honey 0.003

lemony 0.014

80



p-value p-value

Ketones (continued) acid 0.042

sweet 0.005

Medium-chain aldehydes earthy 0.000

hay 0.005


Primary alcohols floral 0.000

grassy/vegetal 0.000

hay 0.001

honey 0.022 <0.0001

lemony 0.002


oxidized apple 0.007 0.049

acid <0.0001

astringent <0.0001

bitter 0.002

sweet 0.008 <0.0001

Sesquiterpenoids earthy <0.0001 0.002

floral 0.001

grassy/vegetal 0.003 0.031

hay 0.004

honey 0.011

lemony 0.000



acid 0.003

sweet 0.012

81

4.3.3 Principal component analysis

An initial PCA of the Year 2 data indicated that low levels of sampling adequacy existed,

starting with a low KMO score (KMO=0.344) in the group of hexyl esters. Thus, it was decided

to remove this group from the analysis. This process was repeated for all sensory attributes and

VOC groups, until all scores remained above 0.500, as described in Section 4.2.6. Using this

process, methyl esters (KMO=0.481) and butyl esters (KMO=0.465) were subsequently

removed. Once this threshold was fulfilled, the overall robustness of the model was observed as

KMO=0.705, meaning that the overall sampling adequacy was ‘middling’, and therefore high

enough to proceed with the analysis. Results of the PCA with specific volatile groups removed

led to the creation of a four-factor model with 14 of the remaining 22 sensory attributes/volatile

groups loaded onto one of the principal components. To increase the number of variables

correlated to a component, a varimax rotation was used to reorient and optimize the model, thus

leading to 17 of the remaining 22 variables being correlated to a factor (r>0.6, when rounded).

These four principal components (factors) were shown to represent 85.8% of the variability

within the model. Factor 1 (32.8% of variability) was positively correlated with lemony, acid,

and astringent. Factor 2 (15.2% of variability) was positively correlated with acetate esters and

primary alcohols. Factor 3 (26.9% of variability) was positively correlated with hay, honey,

floral, overall aromatic intensity, sweet, and negatively correlated with bitter. Factor 4 (10.9% of

variability) was positively correlated with oxidized apple, earthy, ethyl esters, propyl esters,

medium-chain aldehydes, and sesquiterpenoids. Grassy/vegetal, anisoles, amyl esters, fatty

alcohols, and ketones were not strongly correlated to any of the four factors. A full summary of

these correlations can be found in Table 4.4.

In the Year 3 dataset, the initial PCA again indicated that low levels of sampling

adequacy existed, starting with a low KMO score (KMO=0.300) in the group of acetate esters.

Following the same procedure as Year 2, earthy (KMO=0.205), hay (KMO=0.333), hexyl esters

(0.375), butyl esters (0.325), anisoles (0.384), and oxidized apple (0.468) were subsequently

removed. The overall robustness of the model was observed as KMO=0.704, and therefore high

enough to proceed with the analysis. Results of the PCA with specific sensory attributes and

volatile groups removed led to the creation of a three-factor model with 15 of the remaining 18

sensory attributes/volatile groups loaded onto one of the principal components. To increase the

82

number of variables correlated to a component, a varimax rotation was used to reorient and

optimize the model, leading to 16 of the remaining 18 variables being correlated to a factor

(r>0.6, when rounded). These three principal components were shown to represent 87.4% of the

variability within the model. Factor 1 (47.9% of variability) was positively correlated with

lemony, acid, and astringent, while being negatively correlated to sweet. Factor 2 (30.4% of

variability) was positively correlated with honey, floral, overall aromatic intensity, and sweet,

while being negatively correlated to grassy/vegetal, and bitter. Factor 3 (9.2% of variability) was

positively correlated with methyl esters, ethyl esters, propyl esters, amyl esters, fatty alcohols,

medium-chain aldehydes, and primary alcohols. Ketones and sesquiterpenoids were not strongly

correlated to any of the three factors. A full summary of these correlations can be found in Table

4.5.

83

Table 4.4 Year 2: Summary of PCA correlations for sensory attributes and volatile groups.

Sensory attribute/VOC group Factor 1

(32.8%)

Factor 2

(15.2%)

Factor 3

(26.9%)

Factor 4

(10.9%)

Oxidized apple -0.181 -0.133 0.130 0.908

Earthy -0.081 -0.331 -0.132 0.751

Hay 0.126 -0.313 0.570 0.237

Honey -0.295 0.084 0.931 -0.006

Lemony 0.897 0.136 -0.081 0.153

Floral -0.169 0.006 0.871 0.121

Grassy/vegetal 0.358 -0.099 -0.479 0.463

Overall aromatic intensity -0.025 0.104 0.843 0.123

Sweet -0.423 -0.009 0.848 -0.184

Acid 0.955 0.049 -0.252 0.093

Bitter 0.292 -0.181 -0.626 0.263

Astringent 0.787 -0.015 -0.381 0.102

Anisoles 0.014 0.263 0.329 -0.054

Acetate esters 0.068 0.958 0.232 -0.059

Ethyl esters 0.181 0.011 0.016 0.749

Propyl esters 0.158 0.116 -0.023 0.674

Amyl esters -0.034 0.547 -0.011 0.085

Fatty alcohols 0.106 0.407 0.012 0.414

Ketones 0.136 0.329 0.081 0.457

Medium-chain aldehydes 0.220 0.235 0.020 0.725

Primary alcohols 0.057 0.802 -0.009 0.016

Sesquiterpenoids 0.022 0.025 -0.037 0.566


84

Table 4.5 Year 3: Summary of PCA correlations for sensory attributes and volatile groups.

Sensory attribute/VOC group Factor 1

(47.9%)

Factor 2

(30.4%)

Factor 3

(9.2%)

Honey -0.466 0.811 -0.239

Lemony 0.936 -0.127 0.224

Floral -0.199 0.893 -0.058

Grassy/vegetal 0.342 -0.662 0.120

Overall aromatic intensity 0.025 0.848 -0.100

Sweet -0.626 0.710 -0.190

Acid 0.942 -0.322 0.057

Bitter 0.162 -0.742 0.009

Astringent 0.732 -0.399 -0.105

Methyl esters -0.089 -0.129 0.672

Ethyl esters 0.231 0.033 0.911

Propyl esters 0.164 0.160 0.858

Amyl esters -0.066 -0.171 0.683

Fatty alcohols 0.063 -0.050 0.754

Ketones -0.092 -0.103 0.456

Medium-chain aldehydes 0.252 -0.033 0.748

Primary alcohols 0.236 -0.315 0.576

Sesquiterpenoids 0.265 -0.314 0.375


85

4.3.4 Generalized procrustes analysis

Results of a GPA in Year 2 highlighted three individual factors that best represented the

variation within the model. Factor 1 was responsible for 34.0% of sensory variation and 51.6%

of volatile variation. Factor 2 represented 32.5% of the sensory variation and 28.4% of the

volatile variation. Factor 3 represented 19.0% of sensory variation and 5.8% of volatile variation.

Within the three factors, a total of 85.5% of sensory and 85.8% of volatile variations were

accounted for. As a whole model, the GPA encompassed 41.6% of the variability on Factor 1,

30.4% on Factor 2, and 12.4% of the variability on Factor 3 for a total of 84.4% cumulative

variability.

Through further analysis, Factor 1 is strongly correlated in the positive direction by

honey, overall aromatic intensity, sweet, acetate esters, hexyl esters, and butyl esters (Table 4.6).

Variables negatively correlated to Factor 1 included grassy/vegetal and bitter. On Factor 2,

lemony, acid, astringent, ethyl esters, propyl esters, fatty alcohols, ketones, and medium-chain

aldehydes were all positively correlated. Factor 3 was represented by oxidized apple, earthy, and

floral all being positively correlated, with no VOC groups having strong correlations.

Results of the data in Year 3 were also represented across four individual factors as

shown in Table 4.6. Factor 1 accounted for 57.6% of the sensory variation and 30.7% of the

volatile variation. Factor 2 was primarily represented by the VOC groups, as it contained 2.9% of

the sensory variation and 38.4% of the volatile variation. Factor 3 represented 14.7% of the

sensory variation, and 11.3% of the volatile variation. And lastly, Factor 4 accounted for 11.0%

of the sensory variation and 5.9% of the volatile variation. Overall, 86.1% of the sensory

variability was accounted for in the model along with 86.4% of volatile variability. As a whole

model, the GPA encompassed 45.4% of the variability on Factor 1, 18.5% on Factor 2, 13.8% on

Factor 3, and 8.4% of the variability on Factor 4, for a total of 86.2% of the variation.

Further analysis of Year 3 showed strong correlations on Factor 1 in the positive direction

for lemony, grassy/vegetal, acid, astringent, medium-chain aldehydes, and sesquiterpenoids. In

the negative direction, Factor 1 was correlated with honey, floral, and sweet. Factor 2 was

negatively correlated with acetate esters, ethyl esters, propyl esters, butyl esters, and amyl esters.

86

Factor 3 was only correlated in the positive direction to oxidized apple, and Factor 4 was only

correlated in the negative direction to earthy.

Table 4.6 Year 2 (2018-2019) and Year 3 (2019-2020) correlations of sensory and volatile data

through GPA.

Variables

Year 2 Year 3

Factor 1

(41.6%)

Factor 2

(30.4%)

Factor 3

(12.4%)

Factor 1

(45.4%)

Factor 2

(18.5%)

Factor 3

(13.8%)

Factor 4

(8.4%)

Earthy -0.496 0.123 0.612 -0.114 -0.196 0.345 -0.688

Floral 0.533 -0.018 0.556 -0.604 -0.077 0.217 0.419

Grassy/vegetal -0.570 0.397 0.023 0.621 -0.039 -0.394 -0.387

Hay 0.067 0.000 0.467 -0.264 0.141 0.199 -0.345

Honey 0.704 -0.140 0.474 -0.784 0.156 0.232 0.310

Lemony -0.195 0.805 -0.228 0.761 -0.210 -0.213 0.403

Overall aromatic

intensity

0.599 0.092 0.467 -0.443 -0.115 0.399 0.365

Oxidized apple -0.229 0.234 0.870 0.219 -0.315 0.790 -0.207

Acid -0.357 0.758 -0.354 0.815 -0.070 -0.303 0.203

Astringent -0.456 0.616 -0.369 0.672 0.019 -0.357 -0.057

Bitter -0.640 0.195 -0.122 0.471 -0.047 -0.299 -0.472

Sweet 0.680 -0.380 0.358 -0.819 0.111 0.375 0.112

Acetate esters 0.828 0.415 -0.178 -0.506 -0.671 -0.467 -0.005

Amyl esters 0.310 0.280 -0.196 0.213 -0.612 -0.187 -0.273

Anisoles 0.415 0.151 0.026 -0.220 -0.046 -0.266 0.159

Butyl esters 0.576 0.489 0.132 -0.200 -0.690 0.206 -0.260

Ethyl esters -0.336 0.618 0.520 0.513 -0.633 0.229 0.250

Fatty alcohols -0.011 0.616 0.070 0.400 -0.525 0.084 -0.192

Hexyl esters 0.600 0.325 0.081 -0.475 -0.288 -0.001 0.232

Ketones 0.020 0.591 0.162 0.120 -0.403 -0.223 -0.287

Medium-chain

aldehydes

-0.233 0.733 0.394 0.609 -0.411 0.231 0.038

87


Variables

Year 2 Year 3

Factor 1

(41.6%)

Factor 2

(30.4%)

Factor 3

(12.4%)

Factor 1

(45.4%)

Factor 2

(18.5%)

Factor 3

(13.8%)

Factor 4

(8.4%)

Methyl esters -0.036 0.089 0.061 0.356 -0.372 0.421 -0.159

Primary alcohols 0.494 0.478 -0.339 0.543 -0.449 -0.258 -0.294

Propyl esters -0.236 0.610 0.475 0.332 -0.657 0.310 0.379

Sesquiterpenoids -0.174 0.374 0.357 0.617 -0.070 0.016 -0.100


4.3.5 Multi-factor analysis

An initial MFA was run on the sensory DA, VOC, and physicochemical datasets. As an

indicator of the strength of the relationship between each dataset, the random-variable (RV)

coefficient was observed as 0.793 for sensory data, 0.560 for VOC data, and 0.751 for

physicochemical data in Year 2, and 0.745 for sensory data, 0.561 for VOC data, and 0.770 for

physicochemical data in Year 3. Due to the low RV coefficient values for the VOC data, the

physicochemical data was assigned as supplementary data. This allowed for a better

understanding of the true relationship between sensory and VOC analyses.

Results of the final MFA showed that 84.3% of the total variation was captured by four

factors in Year 2 (Table 4.7). Figures 4.1 and 4.2 show plots of Factors 1, 2, and 3. Of these

factors, Factor 1 (33.9% of variation) represented honey, overall aromatic intensity, sweet,

acetate esters, butyl esters, and hexyl esters being strongly correlated in the positive direction,

and grassy/vegetal, bitter, and astringent opposing these in the negative direction. Factor 2

(28.9% of variation) represented lemony, acid, astringent, primary alcohols, TA (as a

supplementary variable), and TA/°Brix ratio (as a supplementary variable). Factor 3 (13.8% of

variation) represented oxidized apple, earthy, ethyl esters, propyl esters, and medium-chain

aldehydes in the positive direction. Factor 4 (7.7% of variation) did not have a strong correlation

88

to any of the sensory attributes or volatile compound groups but did have a strong positive

correlation to the supplementary variable of °Brix.

For Year 3, the final MFA captured 72.4% of the variation across three factors (Table

4.7). Plots of Factors 1, 2, and 3 can be found in Figures 4.3 and 4.4. On Factor 1 (33.0% of

variation), strong positive correlations existed for lemony, grassy/vegetal, acid, bitter,

sesquiterpenoids, and the supplementary variables of TA and TA/°Brix ratio. Alternatively,

Factor 1 was represented in the negative direction by honey, floral, sweet, acetate esters, and the

supplementary variable pH. Factor 2 (25.2% of variation) was represented in the positive

direction by acetate esters. Factor 3 (14.1% of variation) was composed of oxidized apple,

methyl esters, ethyl esters, propyl esters, fatty alcohols, and medium-chain aldehydes, all in the

negative direction.

Table 4.7 Summary of MFA results in Years 2 and 3, with strength of correlations found on four

factors (Year 2), and three factors (Year 3).

Year 2 Year 3

Variables Factor 1

(33.9%)

Factor 2

(28.9%)

Factor 3

(13.8%)

Factor 4

(7.7%)

Factor 1

(33.0%)

Factor 2

(25.2%)

Factor 3

(14.1%)

Oxidized Apple -0.117 -0.060 0.803 -0.202 0.146 -0.008 -0.684

Earthy -0.399 -0.093 0.578 -0.206 -0.148 -0.019 -0.274

Hay 0.048 -0.197 0.392 0.516 -0.239 -0.178 0.041

Honey 0.725 -0.346 0.389 0.397 -0.748 -0.424 -0.130

Lemony -0.325 0.778 0.124 0.333 0.729 0.456 0.106

Floral 0.548 -0.265 0.499 0.428 -0.599 -0.224 -0.238

Grassy/vegetal -0.555 0.366 0.188 -0.141 0.593 0.303 0.180

Overall aromatic

intensity 0.579 -0.104 0.412 0.492 -0.439 -0.144 -0.246

Sweet 0.704 -0.516 0.187 0.351 -0.794 -0.445 -0.237

Acid -0.496 0.787 -0.015 0.353 0.805 0.418 0.273

Bitter -0.644 0.273 -0.074 -0.187 0.448 0.276 0.190

Astringent -0.554 0.659 -0.047 0.185 0.667 0.325 0.323

89


Year 2 Year 3

Variables Factor 1

(33.9%)

Factor 2

(28.9%)

Factor 3

(13.8%)

Factor 4

(7.7%)

Factor 1

(33.0%)

Factor 2

(25.2%)

Factor 3

(14.1%)

Anisoles 0.402 0.125 0.107 0.158 -0.221 0.065 0.141

Acetate esters 0.797 0.582 -0.110 -0.071 -0.621 0.760 0.149

Methyl esters 0.007 0.092 0.072 -0.389 0.260 0.123 -0.675

Ethyl esters -0.266 0.291 0.821 -0.207 0.375 0.419 -0.718

Propyl esters -0.177 0.344 0.716 -0.190 0.205 0.399 -0.712

Butyl esters 0.550 0.540 0.139 0.035 -0.308 0.525 -0.332

Amyl esters 0.342 0.330 0.014 -0.288 0.070 0.504 -0.429

Hexyl esters 0.616 0.446 0.051 -0.323 -0.501 0.251 0.018

Fatty alcohols 0.060 0.426 0.520 -0.327 0.267 0.335 -0.628

Ketones 0.075 0.402 0.527 -0.222 0.007 0.348 -0.290

Medium-chain

aldehydes -0.164 0.432 0.786 -0.215 0.500 0.233 -0.636

Primary alcohols 0.506 0.577 -0.042 -0.319 0.419 0.497 -0.277

Sesquiterpenoids -0.109 0.185 0.520 -0.182 0.580 0.079 -0.221

TA1 -0.407 0.672 0.105 0.445 0.596 0.299 0.313

pH1 0.540 -0.484 -0.196 -0.225 -0.744 -0.261 -0.218

°Brix1 0.022 0.054 -0.136 0.572 -0.436 -0.194 0.049

TA/°Brix ratio1 -0.453 0.725 0.180 0.327 0.733 0.353 0.264

1Indicates a variable that was added into the data as supplementary data, therefore not altering

results

90

Figure 4.1 A MFA representation of Year 2 data for Factor 1 (33.9% of variability; x-axis) and

Factor 2 (28.9% of variability; y-axis). Instrumental data (physicochemical) is shown in red,

sensory data in green, and VOC data in purple. Factor 1 is correlated (r>0.6, rounded) with

honey, overall aromatic intensity (OAI), sweet, acetate esters, hexyl esters, and butyl esters in the

positive direction, and grassy/vegetal, bitter, and astringent in the negative direction. Factor 2 is

correlated (r>0.6, rounded) with lemony, acid, astringent, acetate esters, primary alcohols, TA

(as supplementary), and TA/°Brix ratio (as supplementary) which are represented in the positive

direction.

TA

pH

Brix

TA/Brix ratio

Oxidized AppleEarthy

Hay

Honey

Lemony

Floral

Grassy/vegetal

OAI

Sweet

Acid

Bitter

Astringent

Anisoles

Acetate esters

Methyl esters

Ethyl estersPropyl esters

Butyl esters

Amyl esters

Hexyl estersFatty alcoholsKetones

Medium-chain aldehydes

Primary alcohols

Sesquiterpenoids

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

F2

(28.9

0 %

)

F1 (33.90 %)

Variables (axes F1 and F2: 62.80 %)

instrumental sensory volatiles

91




honey, overall aromatic intensity (OAI), sweet, acetate esters, hexyl esters, and butyl esters in the

positive direction, and grassy/vegetal, bitter, and astringent in the negative direction. Factor 3 is

correlated (r>0.6, rounded) with oxidized apple, earthy, ethyl esters, propyl esters, and medium-

chain aldehydes, all in the positive direction.

TA

pHBrix

TA/Brix ratio

Oxidized Apple

Earthy

Hay

Honey

Lemony

Floral

Grassy/vegetal

OAI

Sweet

Acid

BitterAstringent

Anisoles

Acetate esters

Methyl esters

Ethyl esters

Propyl esters

Butyl esters

Amyl estersHexyl esters

Fatty alcoholsKetones


Primary alcohols

Sesquiterpenoids

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

F3

(13.7

7 %

)

F1 (33.90 %)



92




lemony, grassy/vegetal, acid, astringent, sesquiterpenoids, TA (as supplementary), and TA/°Brix

ratio (as supplementary) in the positive direction, and honey, floral, sweet, acetate esters, and pH

(as supplementary) in the negative direction. Factor 2 is correlated (r>0.6, rounded) with acetate

esters in the positive direction.

TA

pHBrix

TA/Brix

Oxidized appleEarthy

Hay

Honey

Lemony

Floral

Grassy/vegetal

OAI

Sweet

Acid

Bitter

Astringent

Anisoles

Acetate esters

Methyl esters

Ethyl estersPropyl esters

Butyl esters Amyl esters

Hexyl esters

Fatty alcoholsKetones


Primary alcohols

Sesquiterpenoids

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

F2

(25.2

3 %

)

F1 (33.01 %)



93




lemony, grassy/vegetal, acid, astringent, sesquiterpenoids, TA (as supplementary), and TA/°Brix

ratio (as supplementary) in the positive direction, and honey, floral, sweet, acetate esters, and pH

(as supplementary) in the negative direction. Factor 3 is correlated (r>0.6, rounded) with

oxidized apple, methyl esters, ethyl esters, propyl esters, fatty alcohols, and medium-chain

aldehydes in the negative direction.

TA

pH

Brix

TA/Brix

Oxidized apple

Earthy

Hay

Honey

Lemony

Floral

Grassy/vegetal

OAISweet

Acid

Bitter

Astringent

AnisolesAcetate esters

Methyl estersEthyl estersPropyl esters

Butyl esters

Amyl esters

Hexyl esters

Fatty alcohols

Ketones


Primary alcohols

Sesquiterpenoids

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

F3

(14.1

3 %

)

F1 (33.01 %)



94

4.4 Discussion

As previously stated, the hypothesis of this research paper is that key aroma VOCs exist

which are responsible for the creation of unique flavor perceptions and can therefore be related

to consumer liking. In order to explore this hypothesis, it is essential to build on previous results

identified in Chapter 2, which identified that sweet taste, and honey and floral aromas served as

positively influential drivers in consumer liking. With the present research, it is necessary to

identify the VOC(s) responsible for creating these perceptions, and to then discover additional

intrinsic properties of an apple that may be responsible for the creation of these desired

perceptions, which will be explored as the physicochemical properties of the apple.

4.4.1 Flavor characteristics of volatile organic compounds

The first research objective aimed to determine the VOCs responsible for the flavor

perceptions and relate those to consumer liking. Based on previous research, it is understood that

apples are composed of hundreds of individual VOCs which may or may not play a role in the

overall flavor of an apple (Dixon and Hewett, 2000; Ting et al., 2015). Along with this, Dixon

and Hewett (2000) have described over 20 VOCs known as character-impact volatiles. These

character-impact volatiles are the compounds that may be dominant contributors to the flavor

profiles of apples, whether it be through typical aroma/flavors, aroma intensity, or aroma quality

(Dixon and Hewett, 2000). As described by Dixon and Hewett (2000), these dominant VOCs can

be found in varying quantities across many differing apple varieties. For example, apple varieties

with yellow skin have been reported to produce primarily acetic acid esters, while red-skinned

varieties produce primarily butyric acid esters. More specifically, unique apple varieties have

been described in the literature to be characterized by single VOCs, or VOC groups, such as

Cox’s Orange Pippin, Elstar, Golden Delicious, Jonagold, and Delbard Jubilée (hexyl and butyl

acetates), Granny Smith, Nico, Paula Red, and Summer Red (ethyl butanoate and hexan-1-ol), or

Boskoop and Jacques Lebel (α-farnesene and hexyl 2-methylbutanoate) (Dixon and Hewett,

2000). Although these unique VOCs may be present in high concentrations in apples, it is

important to uncover the impact that these have on sensory perception and consumer liking.

To further understand the impact that these VOCs may have on flavor, two different

flavor databases (Flavornet, 2004; The Good Scents Company, 2021) were accessed, and the

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flavor profiles of each VOC used in this study were recorded. This information can be found in

Table 4.8. This table shows an individual VOC has a flavor profile composed of a wide variety

of different terms. These flavors will provide an apple with its own unique profile based on

presence of character-impact compounds, other VOCs, and their concentrations found within the

variety. The creation of these compounds varies from year-to-year, dependent on a combination

of the environmental, genetic, and agronomic factors, as depicted by Musacchi and Serra (2018),

thus ultimately influencing the VOC composition and the overall quality of each apple variety.

Table 4.8 Volatile compounds and their established odor/flavor profiles.

Volatile compound Compound grouping Flavor profile

Estragole Anisoles Sweet1, licorice1,2, phenolic1, weedy1,

spice1, celery-like1, anise2

2-methylbutyl acetate Acetate esters Sweet1, banana1, fruity1, estery1,

ripe1, juicy1, fruit/fruity1,2

5-hexene-1-ol, acetate Acetate esters N/A

Butyl acetate Acetate esters Sweet1, fruity1, banana1, tutti frutti1,

pear2

Hexyl acetate Acetate esters Fruit/fruity1,2, green1, fresh1, sweet1,

banana peel1, apple1, pear1, herb2

2-methylpropyl acetate Acetate esters Sweet1, fruit/fruity1,2, banana1, tutti

frutti, apple2, banana2

Isopentyl acetate Acetate esters Sweet1, fruity1, banana1, green1, ripe1

Propyl acetate Acetate esters Estery1, fruity1, ethereal1, tutti frutti1,

banana1, honey1

Pentyl acetate Acetate esters Fruity1, pear1, banana1, ripe1,

banana1, sweet1

Z-2-hexen-1-ol, acetate Acetate esters N/A

Methyl butyrate Methyl esters Fusel1, fruity1, estery1, dairy1, acidic1

Ethyl propionate Ethyl esters Ethereal1, fruit/fruity1,2, sweet1,

winey1, bubble gum1, apple1, grape1

Ethyl butyrate Ethyl esters Fruity1, sweet1, tutti frutti1, apple1,2,

fresh1, ethereal1

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Volatile compound Compound grouping Flavor profile

Ethyl hexanoate Ethyl esters Sweet1, pineapple1, fruit/fruity1,2,

waxy1, banana1, green1, estery, apple

peel2

Isopropyl butyrate Propyl esters Sweet1, fruity1, estery1, ethereal1,

pineapple1, ripe1

2-methylpropyl

butanoate

Propyl esters Sweet1, fruity1, pineapple1, apple1,

rummy1, bubble gum1, tutti frutti1,

fruit1, overripe fruit1, tropical fruit1

Propyl propionate Propyl esters Sweet1, tropical1, green1, fruity1

Propyl butyrate Propyl esters Sweet1, fruity1, tutti frutti1, bubble

gum1, pineapple1,2, green1, solvent2

Propyl hexanoate Propyl esters Pineapple1, fruit/fruity1,2, sweet1,

tropical1, fresh1, green1, juicy1

Butyl 2-methylbutanoate† Butyl esters Green1, fruity1, cocoa1

Butyl butyrate Butyl esters Sweet1, fruity1, ethereal1, tropical1,

rummy1, cherry1, ripe fruit1,

elderberry1, fatty1

Butyl propionate Butyl esters Fruity1, sweet1, banana1, tropical1,

tutti frutti1

Butyl octanoate† Butyl esters Buttery1, ethereal1, herbal1, fruit2

Butyl hexanoate Butyl esters Fruit/fruity1,2, pineapple1, green1,

waxy1, tutti frutti1, juicy1,

fermented1, fruity1

3-methylbutyl hexanoate Amyl esters Fruity1, sweet1, pineapple1, pungent1,

sour1, cheesy1

Hexyl 2-methylbutanoate Hexyl esters Green1, waxy1, fruity1, apple1,

banana1, woody1, tropical1, spicy1

Hexyl hexanoate Hexyl esters Sweet1, fruity1, green1, tropical1,

apple peel2, peach2

Hexyl octanoate Hexyl esters Green1,2, apple1, fruity1, berry1,

fresh1, waxy1, herb1,2, oil1

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Hexyl propionate† Hexyl esters Pear1, green1, fruity1, musty1,

overripe fruit1

1-hexanol Fatty alcohols Green1,2, fruity1, apple skin1, oily1,

resin2, flower2

(R)-Sulcatol† Fatty alcohols Sweet1, oily1, green1, coriander1

3-hexen-1-ol† Fatty alcohols Green1, leafy1, grass2, moss2, fresh2

Sulcatone Ketones Green1, vegetable1, musty1, apple1,

banana1, green bean1

2-Hexenal† Medium-chain aldehydes Sweet1, bitter almond1, fruity1,

green1,2, leafy1, apple1,2, plum1,

vegetable1

Hexanal Medium-chain aldehydes Green1, woody1, vegetable1, apple1,

grassy1,2, citrus1, orange1, fresh1,

tallow2, fat2

(Z)-3-Hexenal Medium-chain aldehydes Sharp1, green1,2, grassy1, cooked

apple1, apple skin1, leaf2

1-butanol Primary alcohols Banana1, fusel1

1-pentanol Primary alcohols Fusel1, fermented1, bready1, cereal1,

fruity1

(S)-2-methyl-1-butanol† Primary alcohols Roasted1, winey1, onion1, fruity1,

fusel1, alcoholic1, whiskey1

α-farnesene Sesquiterpenoids Fresh1, green1, vegetable1, celery1,

hay1, fatty1, tropical1, fruity1, wood2,

sweet2

1Data collected from TheGoodScentsCompany.com

2Data collected from Flavornet.org

†Represents odor information only for TheGoodScentsCompany.com

To determine which VOCs are responsible for unique flavor characteristics, results from

the PCA, GPA, and MFA statistical analyses were compared across both years. There were many

similarities among the tests which proves that unique flavor profiles do exist among apple

varieties.

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Positive drivers of consumer liking were described as sweet taste with honey and floral

aroma/flavors. Based on the results of this research, sweet taste is strongly correlated to acetate

esters, hexyl esters, and butyl esters. Similarly, honey flavor is also linked to acetate, hexyl, and

butyl esters, whereas floral flavor is associated only with acetate esters (See Table 4.9).

Comparing this to the established flavor databases as described in Table 4.8, common

terminology appears for these three different VOC groups. Acetate esters are commonly defined

as sweet, fruity, and ripe, while hexyl esters identify as green, apple, and fruity, and butyl esters

which similarly identify as being sweet, fruity and tropical. These flavor perceptions align with

the expected characteristics as perceived through sensory DA, and VOC quantification and

qualification through GC-MS. Although this is a promising relationship between a potential

correlation of consumer liking and internal VOCs, more information will be needed to justify

these relationships. As seen across both years, relationships between sweet, honey, and floral

with acetate esters are the only relationships shown to be reproducible across both Year 2 and

Year 3. This is hypothesized to be due to differences in the environmental conditions across the

two years. To justify this, the researchers reviewed growing seasons from the OAG (OAG, 2018;

OAG, 2019) who compose a yearly review of the Ontario apple season. In Year 2 of the study,

apples were said to have faced an increase in disease throughout the summer due to extreme

summer temperatures (OAG, 2018; OAG, 2019). Additionally, due to this unique year in

weather, approximately 10-50% of some apple varieties (ex. Honeycrisp) were lost due to drop

(apples falling from the tree prior to ripening; OAG, 2019). Although these environmental

conditions provided added stressors for apple farmers, the weather was said to provide

exceptional flavor to those apples that did make it to market (OAG, 2018). In contrast, the

growing season in Year 3 was said to have faced the wettest spring in the past 45 years (OAG,

2019). This spring weather led to more sprays for scab (a fungal disease; OAG, 2019). As

described in Section 4.1, these sprays may lead to a better overall texture quality in apples,

however they are also detrimental to the flavor, thus influencing the expected results in this study

(Yahia, 1994; Ting et al., 2015).

In addition to identifying the VOC groups that are linked to positive preference drivers

among consumers, it is also beneficial to observe the groups that are correlated to the negative

drivers of liking among consumers. Although these groups may not serve as the primary focus

99

for the top selections coming out of a breeding program, they may help with screening for

avoidance of the specific compound groups. For example, the sensory attribute of overall

aromatic intensity, which was correlated to acetate esters, hexyl esters, and butyl esters, which

were also previously reported to be indicators of positive consumer liking. It is expected that this

is due to a finding described in MacKenzie et al. (unpublished) where consumers described the

most important characteristic of an ideal apple is for it to be flavorful, yet it was found to be a

negative driver of preference when put into practice. Future research should explore this

relationship and aim to delve deeper into which of these specific overall aromatic intensity

flavors consumers like or dislike. Other negative preference drivers included oxidized apple

flavor, which was linked to ethyl esters, propyl esters, medium-chain aldehydes, and

sesquiterpenoids. Earthy flavor was correlated to ethyl esters, propyl esters, medium-chain

aldehydes, and sesquiterpenoids. Lemony flavor was linked to medium-chain aldehydes, ethyl

esters, propyl esters, fatty alcohols, ketones, acetate esters, primary alcohols, and

sesquiterpenoids. Acid taste was correlated to medium-chain aldehydes, ethyl esters, propyl

esters, fatty alcohols, ketones, acetate esters, primary alcohols, and sesquiterpenoids. Astringent

mouthfeel was linked to medium-chain aldehydes, ethyl esters, propyl esters, fatty alcohols,

ketones, acetate esters, primary alcohols, and sesquiterpenoids. Finally, grassy/vegetal flavor was

correlated to sesquiterpenoids and medium-chain aldehydes. Similar to the positive drivers of

liking, these results were not always found to be consistent across both years of the study, but the

trends noted in this research paper may serve as indicators of potential VOC groups that can be

responsible for consumer dislike among apple varieties. See Table 4.9 for a summary of these

findings.

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Table 4.9 A summary of sensory attributes which were found to be strongly correlated (r>0.6,

rounded) to a common factor with a VOC group, and the listed statistical analyses identifying a

relationship. Correlations are shown based on the corresponding year and factor.

Sensory attribute Statistical test

(ryear,factor)

Compound group Statistical test

(ryear,factor)

Sweet GPA (r2,1=0.680) Acetate esters GPA (r2,1=0.828)

MFA (r2,1=0.704) MFA (r2,1=0.797)

MFA (r3,1=-0.794) MFA (r3,1=-0.621)

Hexyl esters GPA (r2,1=0.600)

MFA (r2,1=0.616)

Butyl esters GPA (r2,1=0.576)

MFA (r2,1=0.550)

Honey GPA (r2,1=0.704) Acetate esters GPA (r2,1=0.828)

MFA (r2,1=0.725) MFA (r2,1=0.797)

MFA (r3,1=-0.748) MFA (r3,1=-0.621)


MFA (r2,1=0.616)


MFA (r2,1=0.550)

Floral GPA (r3,1=-0.599) Acetate esters MFA (r3,1=-0.621)

Overall aromatic

intensity

GPA (r2,1=0.599) Acetate esters GPA (r2,1=0.828)

MFA (r2,1=0.579) MFA (r2,1=0.797)


MFA (r2,1=0.616)


MFA (r2,1=0.550)

Oxidized apple PCA (r2,4 = 0.908) Ethyl esters PCA (r2,4=0.749)

MFA (r2,3=0.803) MFA (r2,3=0.821)

MFA (r3,3=-0.684) MFA (r3,3=-0.718)

Propyl esters PCA (r2,4=0.674)

MFA (r2,3=0.716)

Medium-chain

aldehydes

PCA (r2,4=0.725)

MFA (r2,3=0.786)

Sesquiterpenoids PCA (r2,4=0.566)

Earthy PCA (r2,4=0.751) Ethyl esters PCA (r2,4=0.749)

MFA (r2,3=0.578) MFA (r2,3=0.821)

Propyl esters PCA (r2,4=0.674)

MFA (r2,3=0.716)

Medium-chain

aldehydes

PCA (r2,4=0.725)

MFA (r2,3=0.786)

101


Sensory attribute Statistical test

(ryear,factor)

Compound group Statistical test

(ryear,factor)

Lemony GPA (r2,2=0.805)

GPA (r3,1=0.761)

Medium-chain

aldehydes

GPA (r2,2=0.733)

MFA (r2,2=0.778) GPA (r3,1=0.609)

MFA (r3,1=0.729) Ethyl esters GPA (r2,2=0.618)

Propyl esters GPA (r2,2=0.610)

Fatty alcohols GPA (r2,2=0.616)

Ketones GPA (r2,2=0.591)

Acetate esters MFA (r2,2=0.582)

Primary alcohols MFA (r2,2=0.577)

Sesquiterpenoids GPA (r3,1=0.617)

MFA (r3,1=0.580)

Acid GPA (r2,2=0.758) Medium-chain

aldehydes

GPA (r2,2=0.733)

GPA (r3,1=0.815) GPA (r3,1=0.609)


MFA (r3,1=0.805) Propyl esters GPA (r2,2=0.610)






MFA (r3,1=0.580)

Astringent GPA (r2,2=0.616) Medium-chain

aldehydes

GPA (r2,2=0.733)

GPA (r3,1=0.672) GPA (r3,1=0.609)


MFA (r3,1=0.667) Propyl esters GPA (r2,2=0.610)






MFA (r3,1=0.580)

Grassy/vegetal GPA (r3,1=0.621) Sesquiterpenoids GPA (r3,1=-0.617)

MFA (r3,1=0.593) MFA (r3,1=0.580)

Medium-chain

aldehydes

GPA (r3,1=0.609)

102

4.4.2 Other instrumental measurements responsible for taste and

flavor

The second research objective was to determine whether physicochemical analyses (ex.

pH, °Brix, TA, TA/°Brix ratio) can be related to flavor characteristics. Previous literature has

found that, in comparison to sensory DA, physicochemical properties of an apple are not reliable

indicators of sweet or acid tastes (Iwanami, 2011). These relationships can also be found in

Hoehn et al. (2003), who had attempted to predict consumer liking of apples based on the SSC

content (°Brix) and TA. Furthermore, Harker et al. (2002) determined that SSC was not a reliable

indicator of sweetness in fruit and recommended to pair this instrumental analysis with an expert

sensory panel to increase the reliability of the data. Hoehn et al. (2003) was able to show that

these intrinsic indicators were able to show correlations in some apple varieties, while others had

weak correlations.

As shown in the present research, the addition of physicochemical data into the sensory

and VOC datasets resultantly lowered the strength of the relationships of the sensory DA and

VOC groupings. When a trial of GPA with added physicochemical data was run, the

physicochemical data were highly correlated with each other (TA and TA/°Brix ratio, inverse to

pH) and the tastes of sweet (pH, °Brix) and acid (TA, TA/°Brix ratio). Similarly, when the

physicochemical data was added into the MFA, the RV coefficients, representing the strength of

the relationship, were lowered for the VOC dataset, while having strong correlations for the

sensory and physicochemical data, again representing the correlations between sweet and acid

tastes as well as TA, °Brix, TA/°Brix ratio, and pH. For these reasons, the physicochemical data

was used only as supplementary data in the MFA, and excluded from the GPA, thus not

influencing the results of the sensory DA and VOC groups. Interestingly, the MFA results of the

supplementary physicochemical data still managed to highlight potential connections between

the negative preference drivers, as TA and TA/°Brix ratio were found to be on the same factor

(Factor 2) as lemony, acid, astringent, acetate esters, and primary alcohols in Year 2, and on

Factor 1 in Year 3, with TA and TA/°Brix ratio found in the positive direction along with

lemony, grassy /vegetal, acid, astringent, and sesquiterpenoids, and pH being in the negative

direction along with the positive preference drivers of honey, floral, sweet, and acetate esters.

103

This information helps to explain the final objective, as we can relate pH with our positive

drivers of consumer liking. High concentration of TA, or a high TA/°Brix ratio can be linked to a

negative impact of consumer liking and should therefore be used as an indicator of consumer

dislike among apple varieties.

4.5 Conclusions and future research

The present research elaborates on the work of MacKenzie et al. (unpublished) and

Bowen et al. (2018) at Vineland. Bowen et al. (2018) combined sensory DA data with consumer

evaluation data to create an external preference map, which identified a preference region now

known as the “Apple Sweet Spot” (Bowen et al., 2018). A continuation of this research was

developed by MacKenzie et al. (unpublished), who targeted apples within this “Apple Sweet

Spot” to uncover the differences between “good” and “great” apple varieties through

distinguishing taste and flavor properties. MacKenzie et al. (unpublished) highlighted two groups

of consumers, one which primarily liked apples with sweet taste, and honey and floral flavors,

with texture coming as a secondary contributor to liking. A second group was also identified who

primarily liked apples based on their texture, with taste and flavor coming as a secondary factor

(MacKenzie et al., unpublished). The research described in this study examined whether the

instrumental measurements of VOCs or physicochemical data (pH, °Brix, TA, TA/°Brix ratio)

can be used as predictors of consumer liking.

To achieve this goal, two research objectives were identified. The first objective was to

identify VOCs responsible for the creation of flavors related to consumer liking, and the second

objective was to determine if the physicochemical analyses of pH, °Brix, TA, and TA/°Brix ratio

may add any additional information to these relationships.

Completion of the first objective was based on previous results from MacKenzie et al.

(unpublished), which identified sweet taste and honey and floral flavors to be responsible for

driving consumer liking. This first objective was conducted through the qualification and

quantification of each VOC by GC-MS. This data was then paired with sensory DA through

multivariate statistics and highlighted unique groups of VOCs that are responsible for preference

in apple varieties both in the positive and negative directions. The second objective was

completed through use of a variety of physicochemical analyses which included pH, °Brix, TA

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and TA/°Brix ratio. Results from both research objectives have shown that sweet taste and honey

flavor can be linked to acetate esters, hexyl esters, butyl esters, and pH, while floral flavor can be

linked to acetate esters and pH. In addition to these findings, the research has also found an

abundance of VOCs related to consumer dislike, including ethyl esters, propyl esters, acetate

esters, medium-chain aldehydes, fatty alcohols, primary alcohols, ketones, and sesquiterpenoids

as well as physicochemical indicators of dislike which were identified as TA and TA/°Brix ratio.

The findings of this research will be integrated into the apple breeding program at

Vineland and have the potential to be applied as screening parameters to identify apple varieties

that will likely satisfy the desires of apple consumers. This will ultimately save time, money, and

agricultural resources as the breeding program can instead be fast-tracked by the advancement of

the most promising apples from within the breeding program. By putting these screening

parameters into place, a breeding team will be able to highlight potentially unique and flavorful

apples, thus leading to a more consumer-centric approach when taking new apple varieties to the

Ontario market. In addition, as the chemical composition of all apple varieties share many

commonalities, these results can be applied universally within the apple industry and within

other apple breeding programs.

Future research should focus on the collection of additional datapoints through many

differing growing seasons. This will allow the determination of whether the unique growing

conditions described in the two seasons collected for this study are accurate, or whether they may

be outliers which could be causing discrepancies between years.

105

5 General conclusion and future research

The premise of this thesis was based on previous information determined by Bowen et al.

(2018), who combined sensory DA and consumer hedonic evaluations in order to generate an

external preference map to successfully map consumer liking across a large group of diverse

apples. To build on to this research, the aim of the present research acted to answer two

hypotheses. First, Chapter 3 hypothesized that individual apple varieties have characteristic taste

and flavor attributes responsible for driving consumer preference within the “Apple Sweet Spot”

as defined by Bowen et al. (2018). In Chapter 4, the hypothesis was that key aroma volatiles

exist which are responsible for the creation of unique flavor perception and that these can be

linked to consumer liking.

To test the first hypothesis, Chapter 3 set out three main objectives. First, sensory DA

was used to determine the flavor attributes associated with different apple varieties. This

objective was achieved by evaluating apple varieties across two consecutive growing seasons to

identify the intensity of sensory characteristics for 27 (Year 1) and 28 (Year 2) apple varieties.

Ultimately, this information allowed for the generation and understanding of unique taste and

flavor profiles of each individual apple variety. Second, hedonic consumer evaluation paired

with CATA, as well as questionnaires pertaining to consumer demographics and purchasing

attitudes and behaviors were used to determine which apple varieties consumers like. This was

conducted using a group of untrained consumers screened to be a diverse representation of the

population within the GTA (Canada) who regularly purchase and consume fresh-market apples.

By completing this objective, consumers were able to give a collective liking score for each

tested apple variety and allowed for the classification of consumer groups based on similarities in

liking. In addition to this, the CATA questionnaire allowed for an understanding of the properties

that consumers perceived while tasting the product, as well as the ideation of an ideal apple and

its’ respective characteristics. Other data gathered from questionnaires allowed for an

understanding of consumer profiles and to explore relationships between demographics,

consumer attitudes, and purchase behaviors. The last objective was to combine the sensory and

consumer evaluation data to identify key flavor attributes that can be used as predictors of

consumer liking. This was accomplished via generation of an external preference map focused

on the apples that have already been established as high performing varieties in respect to

106

consumer appeal. This preference map allowed for the separation and identification of unique

taste and flavor profiles of this subset of apples, and highlighted that when paired with juicy and

crisp textures, sweet taste and honey and floral aroma/flavors were the primary drivers of

consumer liking across two different consumer groups (Group 1, 29%; Group 2, 49%). In

addition to this, the preference map helped to identify taste and flavor characteristics such as

acid, bitter, astringent, lemony, and grassy/vegetal which all served as detractors of liking when

found in high intensity within a variety. This data also helped to show the relationship between

each of these consumer groups and their liking towards each individual apple variety as they

were projected onto a map of the sensory and consumer space.

Chapter 4 focused on two primary objectives. The first objective was to identify and

quantify the VOCs responsible for the creation of the liked flavor attributes of honey and floral

perception, while also seeking to define those VOCs responsible for perceptions related to

dislike: lemony and grassy/vegetal. Completion of this objective through GC-MS analysis

allowed for the understanding that the positive preference driver of honey flavor can be linked to

acetate esters, butyl esters, and hexyl esters, while floral can be related to acetate esters. The

negative preference drivers of lemony and grassy/vegetal were successfully linked to ethyl

esters, propyl esters, acetate esters, medium-chain aldehydes, fatty alcohols, primary alcohols,

ketones, and sesquiterpenoids. The second objective of Chapter 4 was to determine whether

physicochemical measurements would provide further insight into an association with positive or

negative preference drivers in respect to consumer liking. This objective was achieved through

TA, pH, and °Brix measurements. Measurements of pH showed relationships with positive

preference drivers (i.e. sweet, honey, floral), while TA and TA/°Brix ratio indicated a

relationship with negative preference drivers (i.e. acid, astringent, lemony). However, as

previously discussed, these relationships may not serve as reliable indicators based on other

results in the literature (Harker et al., 2002; Hoehn et al., 2003; Iwanami, 2011). However, these

results support the principles of the testing procedures, whereas a higher pH would lead to a less

acidic apple, potentially leading to a greater perception of sweetness and its related attributes of

floral and honey. In addition, a higher TA or TA/°Brix ratio indicates a higher level of acidity,

which has been shown to be a negative driver of preference.

107

Therefore, by answering both outlined hypotheses through the written objectives, it can

be concluded that individual apple varieties have unique taste and flavor profiles that are

responsible for consumer preference. Additionally, the sensory characteristics which serve as

attractors or detractors of preference can be explained using instrumental techniques such as GC-

MS or other physicochemical analyses. By understanding these objectives, breeding programs

can implement these instrumental indicators or principles as a means to predict consumer liking

using a much more efficient testing structure, ultimately limiting the necessity for sensory and

consumer evaluation until further along in the breeding process where a smaller group of

products can be evaluated in an efficient manner that is less time consuming, less expensive, and

not as laborious.

Future research should focus on the combination of sensory, consumer, and instrumental

datasets in order to generate predictive models that can help fast track the early stages of apple

breeding to be able to quickly screen apples that will succeed in the market, thus leading to

profitability for businesses, and satisfaction for consumers. If reliable sensorial markers can be

identified and implemented into apple breeding programs, this will pave the way for new apple

varieties with superior fruit quality tailored to the desires of consumers.

108

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Appendices

Appendix 1: Consent to participate in research, provided to each consumer

panelist

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Appendix 2: Consumer evaluation example instructions, with description of

apple followed by description of an ideal apple

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Appendix 3: Series of questions asked during the consumer evaluation

regarding consumption behavior, purchase habits, and demographic

information

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Appendix 3 Continued.

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Appendix 4: Consumer evaluation visual preference paper ballot

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