PERCEPTION OF FINISH IN
WHITE WINE
By
EMILY GOODSTEIN
A thesis submitted in partial fulfillment of
the requirements for the degree of
MASTER OF SCIENCE IN FOOD SCIENCE
WASHINGTON STATE UNIVERSITY
Department of Food Science
DECEMBER 2011
ii
To the Faculty of Washington State University:
The members of the Committee appointed to examine the thesis of EMILY SIMPSON
GOODSTEIN find it satisfactory and recommend that it be accepted.
___________________________________
Carolyn Ross, Ph.D., Chair
___________________________________
Jeffri Bohlscheid, Ph.D.
___________________________________
Marc Evans, Ph.D.
iii
ACKNOWLEDGEMENTS
I would first and foremost like to acknowledge my father, who passed away on
February 7th
, 2010. I think his excitement over me going to graduate school would only be
surpassed by his excitement at my graduating. I appreciate and am reminded every day of
his love, humor, support, and understanding, all of which has led me to this point. His
passing during my time at graduate school has made me realize the importance of expressing
how much you appreciate the people around you and to never miss the opportunity to thank
someone for their contribution to your life.
In that spirit, there are so many people at Washington State University that I would
like to acknowledge. First, I would like to thank my advisor Carolyn Ross for the
opportunity to come to graduate school and pursue a project involving flavor chemistry. Her
support throughout this endeavor has made this project possible. I would like to thank my
committee members, Jeff Bohlscheid and Marc Evans for their continual assistance which
included answering numerous questions and giving excellent advice. I would also like to
acknowledge Barbara Rasco, who helped me with editing and was always available for
advice and discussion on many different topics.
I would like to thank Karen Weller who was extremely helpful throughout my time at
WSU and contributed so much to making this project successful. I would also like to thank
Jodi Anderson for kindly answering all the questions I had for her both before and after I
arrived at WSU. I would also like to acknowledge Scott Mattinson who contributed a great
deal of time answering the many questions I had regarding the gas chromatography analysis
performed in my study. I would like to thank all the members of the Ross Lab. We
iv
experienced all the ups and downs of research together, and from panels to in the lab, there
was always a question to be answered or a problem to be solved.
To my best friends, Sean, Jesse, and Steve—I could not have made through this time
without them. They have seen me through all the different emotions I have experienced and
all the changes in my life over the past two years. Thank you to Sean, who has been patient
and helpful beyond what I ever would have expected from anyone. Thank you to Jesse, who
has been available to both talk and listen, and always makes me laugh with her right-on
remarks. Thank you to Steve, who has always helped me reason out obstacles with practical
advice.
Finally, I would like to acknowledge my mother, who has always been my number
one cheerleader. She has helped me forge ahead and always keeps me looking towards the
future.
v
PERCEPTION OF FINISH IN
WHITE WINE
Abstract
by Emily Goodstein
Washington State University
December 2011
Chair: Carolyn F. Ross
Wine finish is a sensory attribute associated with wine quality. Wine quality
as it relates to finish is currently an understudied area of sensory research. This study aimed
to perform both a trained and consumer evaluation of finish in white wines, with additional
support provided by the analytical analysis of commercially available unoaked and oaked
Chardonnay wines. The hypothesis of this study was that certain flavors such as coconut and
mushroom would take longer to finish compared to fruit and floral flavors. Model and base
wines were prepared through the addition of four chemical compounds; ethyl hexanoate
(fruity), linalool (floral), oak lactone (coconut), and 1-octen-3-ol (mushroom). Trained
panelists executed time intensity analysis of these four compounds for the aroma attributes of
fruity, floral, coconut, and mushroom. Model wine evaluation of single compounds showed
fruity flavor finished significantly earlier than all other flavors. Model wine evaluation of
multiple flavors in solution indicated that fruity flavor displayed the shortest finish, with
mushroom acting as a suppressor of other flavors. Normalization of model wine time
intensity analysis showed that data variation was largely attributed to the contrasting
relationship between fruity and mushroom flavors. Base wine analysis did not yield
significant differences in flavor finish length. A trend of floral flavor suppression by coconut
vi
flavor emerged when analyzing time intensity parameters for intensity maximum (Imax) and
area under the curve (AUC).
Consumer panel analysis of finish of commercially available Washington State
Chardonnays indicated that consumers were able to discern differences in finish length
between unoaked and high oaked wines. Consumers significantly preferred unoaked wines
over high oak wines and were willing to pay higher dollar values per bottle for unoaked
wines than high oak wines. Solid phase microextraction-gas chromatography-mass
spectrometry was utilized to quantify cis- and trans-oak lactone concentration of unoaked,
medium, and high oak Chardonnay samples. Increases in oak treatment corresponded to
increases in cis- and trans-oak lactone concentrations. This study provides the first rigorous
examination of wine finish and its relationship to both the presence of different flavor
compounds and consumer acceptance.
vii
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS .................................................................................................... iii
ABSTRACT ............................................................................................................................. v
LIST OF TABLES ................................................................................................................... x
LIST OF FIGURES ............................................................................................................... xv
CHAPTER
1. INTRODUCTION ................................................................................................. 1
2. LITERATURE REVIEW ...................................................................................... 3
Wine finish ................................................................................................. 3
Economic relevance ................................................................................... 3
Wine processing ......................................................................................... 4
Non-volatile components of wine .............................................................. 5
Volatile components of wine ..................................................................... 6
Volatiles to be analyzed by a trained panel ............................................... 7
Floral…………………………………………………………………..8
Fruity…………………………………………………………………10
Mushroom…………………………………………………………….10
Barrel aged/coconut…………………………………………………..11
Model wine .............................................................................................. 12
Flavor perception ..................................................................................... 13
Time intensity .......................................................................................... 14
Barrel aging of white wine ....................................................................... 17
viii
Gas chromatography-mass spectrometry analysis of oak lactone ........... 19
3. MATERIALS AND METHODS ......................................................................... 21
Model wine .............................................................................................. 21
Wines ....................................................................................................... 21
Trained panel ........................................................................................... 22
Trained panel demographics .............................................................. 22
Training sessions ................................................................................ 22
Trained panel formal evaluations....................................................... 24
Data analysis ...................................................................................... 25
Consumer panel ....................................................................................... 26
Consumer evaluation ......................................................................... 26
Data analysis ...................................................................................... 27
Gas chromatography-mass spectrometry (GC-MS) ................................. 27
4. RESULTS AND DISCUSSION .......................................................................... 29
Trained panel ........................................................................................... 29
Single flavor compound analysis in model wine ............................... 29
Multiple flavor compound analysis in model wine ............................ 31
Single flavor compound analysis in base wine .................................. 35
Multiple flavor compound analysis in base wine .............................. 36
Consumer panel evaluation of commercial Chardonnay ......................... 39
Gas chromatography-mass spectrometry ................................................. 40
5. FIGURES AND TABLES ................................................................................... 42
6. CONCLUSIONS AND FUTURE RESEARCH ............................................... 103
ix
7. REFERENCES .................................................................................................. 106
x
LIST OF TABLES
Page
1. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for single compounds in model wine samples
analyzed by a trained panel (n=10) ............................................................................ 42
2. Analysis of variance (ANOVA) of maximum intensity
(Imax) for single compounds in model wine samples analyzed
by a trained panel (n=10) .......................................................................................... 43
3. Pairwise comparison of maximum intensity (Imax) evaluations
for single compounds in model wine samples analyzed by a
trained panel (n=10) .................................................................................................. 44
4. Analysis of variance (ANOVA) duration (Tend) for single
compounds in model wine samples analyzed by a trained panel (n=10) .................. 45
5. Pairwise comparison of duration (Tend) evaluations for single
compounds in model wine samples analyzed by a trained
panel (n=10) ............................................................................................................... 46
6. Analysis of variance (ANOVA) of area under the curve
(AUC) for single compounds in model wine samples analyzed
by a trained panel (n=10) .......................................................................................... 47
7. Pairwise comparison of area under the curve (AUC) evaluations
for single compounds in model wine samples analyzed by a
trained panel (n=10) .................................................................................................. 48
8. Mean Separation of Maximum Intensity (Imax), Duration (Tend),
and area under the curve (AUC) for single compounds in model
wine samples analyzed by the trained panel (n=10) .................................................. 49
9. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for two compounds in model wine samples
analyzed by a trained panel (n=10) ............................................................................ 56
10. Analysis of variance (ANOVA) of maximum
intensity (Imax) for two compounds in model wine samples
analyzed by a trained panel (n=10) ............................................................................ 57
11. Analysis of variance (ANOVA) of duration (Tend) for two
compounds in model wine samples analyzed by a trained
panel (n=10) ............................................................................................................... 58
xi
12. Analysis of variance (ANOVA) of area under the curve
(AUC) for two compounds in model wine samples analyzed
by a trained panel (n=10) ........................................................................................... 59
13. Mean separation of contrasts of the four flavors analyzed in
two compound model wine solutions evaluated by a trained
panel (n=10) ............................................................................................................... 60
14. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for four compounds in model wine samples
analyzed by a trained panel (n=10) ............................................................................ 62
15. Analysis of variance (ANOVA) of maximum intensity
(Imax) for four compounds in model wine samples analyzed
by a trained panel (n=10) ........................................................................................... 63
16. Analysis of variance (ANOVA) duration (Tend) for four
compounds in model wine samples analyzed by a trained panel (n=10) ................... 64
17. Pairwise comparison of duration (Tend) evaluations for four
compounds in model wine samples analyzed by a trained
panel (n=10) .............................................................................................................. 65
18. Analysis of variance (ANOVA) of area under the curve
(AUC) for four compounds in model wine samples analyzed
by a trained panel (n=10) ........................................................................................... 66
19. Pairwise comparison of area under the curve (AUC) evaluations
for four compounds in model wine samples analyzed by a
trained panel (n=10) ................................................................................................... 67
20. Mean separation of duration (Tend) and area under the curve
(AUC) for four compounds in model wine samples analyzed by the trained panel
(n=10) ......................................................................................................................... 68
21. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for single compounds in base wine samples
analyzed by a trained panel (n=10) ............................................................................ 70
22. Analysis of variance (ANOVA) of maximum intensity
(Imax) for single compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 71
23. Analysis of variance (ANOVA) duration (Tend) for single
compounds in base wine samples analyzed by a trained panel (n=10)...................... 72
xii
24. Analysis of variance (ANOVA) of area under the curve
(AUC) for single compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 73
25. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for two compounds in base wine samples
analyzed by a trained panel (n=10) ............................................................................ 75
26. Analysis of variance (ANOVA) of maximum intensity
(Imax) for two compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 76
27. Analysis of variance (ANOVA) duration (Tend) for two
compounds in base wine samples analyzed by a trained panel (n=10)...................... 77
28. Analysis of variance (ANOVA) of area under the curve
(AUC) for two compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 78
29. Mean separation of contrasts of the four flavors analyzed in
two compound base wine solutions evaluated by a trained
panel (n=10) ............................................................................................................... 79
30. Analysis of variance (ANOVA) of time to maximum
intensity (Tmax) for four compounds in base wine samples
analyzed by a trained panel (n=10) ............................................................................ 81
31. Pairwise comparison of time to maximum intensity (Tmax)
evaluations for four compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 82
32. Analysis of variance (ANOVA) of maximum intensity
(Imax) for four compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 83
33. Pairwise comparison of maximum intensity (Imax) evaluations for
four compounds in base wine samples analyzed by a trained
panel (n=10) ............................................................................................................... 84
34. Analysis of variance (ANOVA) duration (Tend) for four
compounds in base wine samples analyzed by a trained panel (n=10)...................... 85
35. Analysis of variance (ANOVA) of area under the curve
(AUC) for four compounds in base wine samples analyzed
by a trained panel (n=10) ........................................................................................... 86
xiii
36. Pairwise comparison of area under the curve (AUC) evaluations
for four compounds in base wine samples analyzed by a trained
panel (n=10) ............................................................................................................... 87
37. Mean separation of time to maximum intensity (Tmax), maximum
intensity (Imax), and area under the curve (AUC) for four compounds
in base wine samples analyzed by the trained panel (n=10) ...................................... 88
38. Analysis of variance (ANOVA) table of time to maximum intensity
(Tmax) for three commercially produced Washington State
Chardonnay samples analyzed by a trained panel (n=10) ......................................... 90
39. Analysis of variance (ANOVA) table of maximum intensity
(Imax) for three commercially produced Washington State
Chardonnay samples analyzed by a trained panel (n=10) ......................................... 91
40. Analysis of variance (ANOVA) table of duration (Tend) for three
commercially produced Washington State Chardonnay
samples analyzed by a trained panel (n=10) .............................................................. 92
41. Analysis of variance (ANOVA) table of area under the curve
(AUC) for three commercially produced Washington State
Chardonnay samples analyzed by a trained panel (n=10) ......................................... 93
42. Analysis of variance (ANOVA) of consumer panel (n=60)
evaluation of Washington State Chardonnay finish with
timed finish perception .............................................................................................. 94
43. Pairwise comparison of finish time of Washington State
Chardonnays analyzed by a consumer panel (n=60) ................................................. 95
44. Mean separation of finish time (seconds) of Washington State
Chardonnays analyzed by a consumer panel (n=60) ................................................. 96
45. Analysis of variance (ANOVA) of consumer panel (n=60)
evaluation of Washington State Chardonnay finish with a
7-point hedonic scale ................................................................................................. 97
46. Mean separation of 7-point hedonic scale rating of Washington
State Chardonnays analyzed by a consumer panel (n=60) ........................................ 98
47. Coding of branched willingness to purchase responses determined
by the consumers (n=60) for the evaluation of the Washington
State Chardonnay ....................................................................................................... 99
xiv
48. Pairwise comparison of wines for branch willingness to purchase
response to Washington State Chardonnays by consumer panel (n=60) ................ 100
xv
LIST OF FIGURES
Page
1. Structure of coconut, floral, fruity, and mushroom compounds
utilized in the trained panel .......................................................................................... 9
2. Representative trained panelist time intensity evaluation of single
flavors in model wine................................................................................................. 50
3. Pearson‘s principle component (PC) analysis factor loadings and
factor scores from normalized time intensity data of single compounds
in model wine evaluation by a trained panel (n=10)................................................. 51
4. Comparison of standard data and normalized data from a representative
trained panelist‘s time intensity evaluation of the coconut flavor in
single compound model wine .................................................................................... 52
5. Comparison of standard data and normalized data from a representative
trained panelist‘s time intensity evaluation of the floral flavor in single
compound model wine .............................................................................................. 53
6. Comparison of standard data and normalized data from a representative
trained panelist‘s time intensity evaluation of the fruity flavor in single
compound model wine ............................................................................................... 54
7. Comparison of standard data and normalized data from a representative
trained panelist‘s time intensity evaluation of the mushroom flavor in
single compound model wine .................................................................................... 55
8. Pearson‘s principle component analysis factor loadings and factor
scores from normalized time intensity data of two compounds in model
wine evaluation by a trained panel (n=10) ................................................................. 61
9. Pearson‘s principle component analysis factor loadings and factor
scores from normalized time intensity data of four compounds in model
wine evaluation by a trained panel (n=10) ................................................................. 69
10. Pearson‘s principle component analysis factor loadings and factor
scores from normalized time intensity data of single compounds in
base wine evaluation by a trained panel (n=10) ........................................................ 74
xvi
11. Pearson‘s principle component analysis factor loadings and factor
scores from normalized time intensity data of two compounds in
base wine evaluation by a trained panel (n=10) ........................................................ 80
12. Pearson‘s principle component analysis factor loadings and factor
scores from normalized time intensity data of four compounds in
base wine evaluation by a trained panel (n=10) ........................................................ 89
13. Standard curve for cis-oak lactone in model wine (9% EtOH, 0.6%
fructose, pH 3.0) with three replicates ..................................................................... 101
14. Standard curve for trans-oak lactone in model wine (9% EtOH, 0.6%
fructose, pH 3.0) with three replicates ..................................................................... 102
1
CHAPTER 1
INTRODUCTION
Wine production represents a burgeoning industry in Washington State. Quality can be
influential in determining the economic success of a wine and the winery, many of which are
small businesses. Characteristic of high quality wine, wine finish refers to the lingering after-
flavor that occurs once wine has been swallowed or expectorated (Jackson 2000). Wine finish is
influenced by flavor, which is an important parameter of a wine sensory profile that can dictate
wine quality and/or consumer acceptance, thereby making wine flavor a topic of great interest to
researchers. Despite interest in wine flavor, wine finish has remained a largely unstudied topic.
The objectives of this study were to determine the impact of flavor compounds on white
wine finish. Evaluation of white wine was chosen over red wine. Although red wines typically
are associated with long finish, they are also associated with astringency. Because astringency
was not a sensory property of interest in the present study, the presence of this attribute could be
distracting to the panelist. This distraction could result in what is known as ―attribute dumping‖
(Lawless and Heymann 1998). ―Dumping‖ occurs when an obvious trait is not evaluated—
panelists attempt to evaluate the conspicuous trait by ―dumping‖ their thoughts and perceptions
onto another trait (Lawless and Heymann 1998). As this can cause misleading results, it was
preferable to utilize white wine for evaluation as it lacks the astringency of red wine that could
cause the ―dumping‖ phenomenon. In addition, astringency is known to be a challenging and
fatiguing sensory attribute to evaluate thus by excluding this from the study, the panelists were
able to focus on wine flavor.
In this study, a trained and consumer panel evaluated white wine finish. The objective of
the trained sensory panel was to perform a time intensity analysis of the finish of selected white
wine flavors in model wine and base wine to determine how different flavor compounds affected
2
and contributed to finish perception. These trained panelists evaluated ethyl hexanoate (fruity
flavor), linalool (floral flavor), oak lactone (coconut), and 1-octen-3-ol (mushroom). A
secondary objective of this panel was to determine the impact of presenting multiple flavor
compounds to panelists. It was hypothesized that compounds associated with fruit and floral
notes would finish earlier than compounds associated with coconut and mushroom notes. In
addition to a trained panel where panelists performed a time intensity analysis of four distinct
flavors, a consumer panel for the evaluation of overall finish was performed.
The consumer sensory panel evaluated commercial Chardonnay wines that were
subjected to different oaking regimes, corresponding to no oak, medium and high oak wines.
Consumers evaluated the length of finish, liking of finish, and their willingness to purchase the
wine. It was hypothesized that consumers would be willing to pay higher dollar amounts for
wines with a longer finish. Concurrent with the consumer panel, gas chromatography-mass
spectrometry (GC-MS) analysis of the wines was performed to quantify the concentration of oak
lactone in the Chardonnay wine. It was hypothesized that no oak, medium and high oak
Chardonnay wines would contain increasing concentrations of oak lactone.
This study of white wine finish yielded an idea of what flavors contribute to a
lengthening of wine finish perception. Since extended finish is an attribute that has been
associated with high quality wines, this study will allow wine producers to evaluate the presence
and methods to generate these flavors in their wines. By altering wine quality perception by
lengthening finish, economic success may be increased
3
CHAPTER 2
LITERATURE REVIEW
Wine Finish
Wine finish is defined by Jackson as the lingering taste following the swallowing of wine
(Jackson 2000). Lengthy finish is most notable in fine red wines but is also observed in
Sauvignon Blanc and Chardonnay white wine varieties (Amerine and Rossler 1983). Typically,
it is believed that certain flavors are associated with different lengths of finish. Fruity and floral
flavors are thought to have a shorter finish while oak, spice, and earthy flavors are thought to
have a longer finish. However, this is a relatively unexplored area and commonly accepted
theory is based largely upon conventional wisdom rather than scientific data.
Finish is an attribute closely related to quality. Longer finish is associated with higher
quality wines (Jackson 2002). High quality dictates the selling price and cache associated with a
particular wine. Studying the finish of different white wine flavors could indicate what flavors
are important determinants of perceived wine quality, and can therefore be related to economic
relevance.
Economic Relevance
The wine industry represents an important economic entity to many countries around the
world (Washington Wine Commission 2007). Historically, European countries such as France
and Germany have been major producers of white wine (Jackson 2000). But, in recent years,
there has been an increase in the number of countries attempting to create successful wine
industries. Wine production in Washington State has grown into an important economic
enterprise. The Washington Wine Commission reports that 12 million cases of wine are
produced annually in Washington State. This contributes approximately three billion dollars to
4
the Washington State economy and approximately 4.7 billion dollars to the national economy
(Washington Wine Commission 2007).
A significant portion of grapes grown in Washington State are white varietals, with
Riesling, Chardonnay, and Pinot Gris grapes ranking as the top three (Washington Wine
Commission 2007). Due to the economic importance of white wine production within
Washington State, it has become important to study factors that alter white wine quality. .
However, determining what factors affect white wine quality can be a difficult matter as wine is
a complex solution made up of non-volatile and volatile components (Jackson 2000). In order to
understand how these elements of physical composition arise, wine processing procedures must
be observed.
Wine Processing
The processing of grapes into a finished wine can have a distinct effect on white wine
flavor. The process begins by harvesting the grapes, either by hand or machine. Grapes are
prepared for crushing by removing the stems, which are a source of unwanted phenols. Grapes
are then crushed to form a must, a mixture of grape juice, skins, and seeds. Crushed grapes
inadvertently go through maceration. This is not always considered a desirable process for white
grapes, as it can lead to the collection of phenolic compounds in certain varietals (Jackson 2000).
In the context of grape must, phenols can lead to oxidative browning (Servili and others 2000).
However, maceration is thought to increase the amount of flavor compounds (Ramey and others
1986).
The must is then pressed and clarified, after which producers will make adjustments to
the juice composition to ensure optimal fermentation (Jackson 2000). Juice is either allowed to
ferment utilizing its naturally present microbial flora, or it is inoculated with a pure and/or
5
known strain of yeast to facilitate more successful fermentation results. At this point, certain
wine varieties may be stored in oak barrels. Barrel aging of wines is usually reserved for fine red
wines and certain white wine varieties such as Chardonnay and some Sauvignon Blanc. After
fermentation, modifications are made to the wines including sugar and acidity adjustment. Wine
is then stored in either stainless steel or oak barrels for aging before bottling (Jackson 2000).
Storage in oak barrels provides the wine exposure to flavor compounds that originate in the
wood and oxygen, both of which are thought to positively affect the wine (Wilker and Gallander
1988). Wine processing measures lead to the creation of the wine matrix which consists of a
combination of non-volatile and volatile components.
Non-volatile Components of Wine
Non-volatile wine components include sugars and acids which contribute to sweet and
sour tastes, respectively. The tastes created by the presence and proportion of these sugars and
acids have been described as the dominant contributors to white wine flavor and also a major
influence on the perception of wine quality (Zamora and others 2006). A study by Zamora and
others in 2006 discussed the importance of sweet and sour taste perception in white wine. In this
study, taste perception in solutions with tartaric acid to a pH of 3.0 and 3.8 and fructose at 11.1
mM and 38.9 mM were evaluated by panelists. The study found that tartaric acid acted as a
suppressor of fructose. Fructose also suppressed tartaric acid, but not as effectively. So, fructose
to tartaric acid ratio effects the perception of sweet and sour tastes (Zamora and others 2006).
Grape harvest is often timed in order to pick grapes when they have reached a desirable balance
between acid and sugar concentration (Lund and Bohlmann 2006). Acidity levels are higher
before grape maturation due to the development and storage of acids in storage vacuoles within
the grape mesocarp. As the grape matures, sugars such as glucose and fructose become more
6
abundant, while malic acids are utilized in metabolic processes (Combe 1992). Therefore,
appropriate timing of harvest can be highly influential in wine flavor and quality perception.
Polyphenolic compounds are also constituents of the non-volatile fraction of wine and
include compounds such as tannins and catechins, which contribute to astringent and bitter
sensations, respectively (Arnold and others 1980, Robichaud and Noble 1990). Research has
shown that perception of white wine quality is negatively influenced by increasing
concentrations of phenolic compounds due to significant increases in perceived astringency
(Arnold and Noble 1978). A 1978 study by Arnold and Noble showed that panelists found
significant differences when evaluating 25, 80 and 130 mg/L phenolic extract in model wine.
Perception of astringency increased significantly as phenolic concentration increased (Arnold
and Noble 1978). Although tannins and catechins are more typically characteristic of red wines,
flavonoid tannins have been noted to contribute to bitterness in Riesling wine varieties. A 2008
study by Etaio and others demonstrated that wine produced by cofermentation of Viura grapes (a
white variety) with Tempranillo grapes (a red variety) displayed decreased overall polyphenol
content of 6 abs280 and tannin concentration of 0.23 g/L when compared to Tempranillo wine
alone (Etaio and others 2008). Water, glycerol, and nonflavonoid phenols are also considered
non-volatile constituents of wine (Jackson 2000).
Volatile Components in Wine
Volatile compounds are numerous in white wine and other alcoholic beverages.
According to a 2001 review by Ebeler, over1300 compounds have been successfully dectected in
wine, beer, malt beverages, brandy, and distilled spirits. Major categories of flavor compounds
include terpenes, phenols, ethyl esters, acetate esters and lactones (Ebeler 2001). Different
compounds may be responsible for different perceived flavors. For example, monoterpenes are
7
associated with floral notes (Marais 1983) whereas oak lactones are associated with coconut
notes (Waterhouse and Towey 1994).
In a 1997 study by Guth, 44 odor active compounds were quantified in Scheurebe and
Gewürztraminer white wine varieties (Guth 1997). A 2003 study of Chardonnay wines utilizing
gas chromatography-olfactometry found 81 odor active volatile compounds and identified 61 of
these compounds (Lee and Noble 2003). Both studies examined odor active compounds,
meaning compounds that contribute to aroma. A compound that is not odor active can be present
but may not create noticeable impact on wine aroma.
Alcohols also represent a major volatile element present in wines. Ethanol is the main
alcohol present in wines, and is the product of yeast fermentation (Ebeler 2001). Ethanol plays a
number of important sensory roles in wine. It is responsible for burning sensations and also at
appropriate concentrations, sensations of sweetness. A 2006 study by Zamora and others
showed that increasing ethanol percentage from 4% to 12% led to larger sweetness intensity
ratings when panelists were evaluating model wine solutions (Zamora and others 2006). Other
alcohols are also present in wines including fusel alcohols (i.e. hexanol and 1-octen-3-ol) and
methanol (Jackson 2000). Although there are numerous volatile compound found within the
wine matrix, four compounds were chosen for analysis by trained panelists in the present study.
Volatiles to be Analyzed by a Trained Panel
Compounds were chosen for this study based on a variety of factors. Choices were based
on the Wine Aroma Wheel published by Noble and others in 1987. The Wine Aroma Wheel
categorizes aroma notes from general to specific terms, in order to provide a lexicon of
terminology with which wine aroma can be described (Noble and others 1987). The objective
was to utilize four compounds that represented four distinct areas of the Aroma Wheel. With
8
this in mind, a representative compound was found for floral, fruity, mushroom, and oak.
Compounds were also determined based on cited odor activity or presence in at least one white
wine variety. Molecular weight was taken into account to avoid the usage of extremely volatile
compounds that would be inappropriate for sensory evaluation.
Floral
Linalool (Figure 1a) is a volatile terpene (Clarke and Bakker 2004) associated with
green, floral and citrus notes (Burdock 20009). With a reported taste threshold of 5 ppm
(Burdock 2010), linalool was chosen due to notable floral qualities. The compound is found in
numerous white grape varieties, but present at highest quantities in Gewürztraminer (0.006-0.19
ppm), Sheurebe (0.007-0.370 ppm), and Morio-Muscat (0.16-0.28 ppm) cultivars (Schreier and
others 1977). Linalool has also been reported in Chardonnay, but at a lower concentration (0.1
ppm respectively) (Aldave and other 1993).
Linalool can be available in different forms within a grape, some of which are non
volatile (Ebeler 2001). These forms include the free, or volatile, linalool, oxidized linalool, and
glycosidically bound linalool (Wilson and others 1986). A 1986 study by Wilson and others
found that free linalool was present in equivalent amounts in the skin and juice of Muscat grape
varieties, while a lower concentration of free linalool was found in the grape pulp. These
findings were contrary to the popular theory that terpenes were generally a constituent of the
grape skin (Wilson and others 1986).
Research has also been performed to determine the impact of processing conditions and
different levels of grape maturation on linalool concentration. Marais and van Wyk
demonstrated that maturation caused significant increases in linalool concentration in Riesling
juice with concentrations increasing from 0.49 to 5.35 µg/L. While studying four maturation
9
Figure 1. Compound structures for a) linalool, b) ethyl hexanoate c) 1-octen-3-ol and d) oak
lactone
a)
b)
c)
d)
10
levels, which corresponded to 16.1, 18.8, 18.7, and 19.7 Brix, continuous increases in linalool
were seen in grapes in the first three levels (16.1-18.7 Brix), after which decline to 0.35 µg/L
was noted. Since continuous increases in linalool concentration were observed during grape
maturation, the researchers postulated that results were caused by the occurrence of hydrolytic or
enzymatic reactions. These reactions may have either released bound linalool or converted
precursor compounds into linalool during processing (Marais and van Wyk 1986).
Fruity
Ethyl hexanoate (Figure 1b) is an ethyl ester that is associated with fruity (Burdock
2009) and apple (Genovese and others 2007) notes. With a taste threshold of 10 ppm, ethyl
hexanoate was chosen due to its high odor activity in numerous white wine varieties. Odor
activity has been documented in Gewürztraminer and Scheurebe (Guth 1997), Fiano (Genovese
and other 2007), and Chardonnay wines (Lee and Noble 2003).
Ethyl esters are formed as a result of fermentation (Bardi and others 1998). During
fermentation, fatty acids are released and are esterified with alcohols (Nordstom 1964).
Synthesis of ethyl hexanoate is influenced by fermentation conditions (Nordstom 1964).
Anaerobic conditions resulted in the formation of 5 fold higher concentrations of ethyl hexanoate
than semi-anaerobic conditions (Bertrand 1968). These results were strain dependent to S.
oviformis and S. ellipsoideus. Also, utilizing strains of S. cerevisiae resulted in higher levels of
ethyl hexanoate than in fermentations utilizing S.uvarum yeast strains (Bertrand 1968).
Mushroom
1-octen-3-ol (Figure 1c) is an alcohol associated with mushroom notes with a recognition
threshold of 10 ppm (Burdock 2009). Linked with fungal growth on grapes, this compound was
chosen due to its distinct mushroom aroma and flavor (La Guerche and others 2006). A 2006
11
study by La Guerche and others examined the mushroom and earthy compounds found in grapes
with fungal growth. Although typically associated with Botrytis cinerea growth, 1-octen-3-ol is
also produced by four other types of fungi. Quantification by gas chromatography-mass
spectrometry (GC-MS) revealed the presence of 1-octen-3-ol in Gamay and Semillion grape
varieties, while gas chromatography-olfactometry (GC-O) analysis indicated that 1-octen-3-ol
was an odor active compound with mushroom aroma. Sensory testing determined that 1-octen-
3-ol had a low sensory odor perception threshold in three different media (water: 2 µg/L, model
wine 20 µg/L, red wine 40 µg/L). Researchers noted this as an important factor, as low
concentrations could lead to noticeable impact on wine. The study found that 1-octen-3-ol is
also able to withstand fermentation with minimal degradation and/or decreases in concentration
(La Guerche and other 2006).
In additional to being a result of fungal growth on grapes, presence of 1-octen-3-ol can
also be attributed to cork taint (Ezquerro and Tena 2005). Cork taint is a wine fault that
negatively impacts wine odor (Jackson 2000). While cork taint is typically associated with 2,4,6
trichloroanisole, a 2005 study by Ezquerro and Tena determined that 1-octen-3-ol is also a
contributing compound to this wine fault (Ezquerro and Tena 2005).
Barrel aged/Coconut
Oak lactone is an important volatile compound present in wines aged in oak barrels, with
characteristics of vanilla, wood, and coconut (Burdock 2009). Lactones are classified as cyclized
esters, and have different flavor qualities based on isomerization and carbon chain length (de
Mann 1999). Also known by the names whiskey lactone, 4-hydroxy-3-methyloctanoic acid
lactone, and 2(3H)-furanone, oak lactone has a reported taste threshold of 0.5 ppm (Burdock
2009).
12
There are four isomers of oak lactone (Figure 1d). These forms include naturally
occurring (4S, 5S) cis-oak lactone and (4S, 5R) trans-oak lactone, and non-naturally occurring
(4R, 5R) cis-oak lactone and (4R, 5S) trans-oak lactone (Brown and others 2006). A sensory
analysis of all four oak lactone isomers was performed by Brown and others in 2006 to
determine if sensory differences in aroma detection exist between the four isomers of oak-
lactone. Aroma detection threshold tests were performed by untrained panelists (n=33 to 43)
using red and white wine as the base for presenting the four isomers of oak lactone. In both red
and white wine, aroma detection thresholds were lower in naturally occurring cis-oak lactone
than in naturally occurring trans-oak lactone. Duo-trio tests were performed by untrained
panelists (n=36) to evaluate if there was a perceivable difference in aroma between oak lactone
enantiomers in wine. There was not a significant difference between naturally occurring and
non-naturally occurring trans-oak lactone enantiomers, but significant differences were observed
between naturally occurring and non-naturally occurring cis-oak lactone enantiomers. Duo-trio
testing comparing wines with naturally occurring cis-oak lactone to wines with naturally
occurring cis and trans- oak lactone (150 µg/L in white wine, 300 µg/L in red wine for all
isomers) did not yield significant results. The study concluded that cis-oak lactone has a greater
impact on wine aroma than the trans-oak lactone in red and white wine (Brown and other 2006).
Model Wine
Model wines act as a simplified wine matrix, containing only a limited number of
components. In the present study, trained panelists evaluated a model wine consisting of water,
ethanol, tartaric acid, and fructose. Tartaric acid and fructose were chosen based on their natural
presence in wine (Jackson 2000). Examples of model wine usage include the previously cited
study by Zamora and others in 2006, where sweet and sour perception was evaluated in the
13
presence of differing levels of ethanol. Model solutions contain 2, 4, or 12% ethanol combined
with 11.1 mM or 38.9 mM fructose, 3.0 or 3.8 pH (modified by tartaric acid) (Zamora and others
2006). A 1999 study by Kadim and Mannheim utilized model wine with 12% ethanol,
potassium hydrogen tartrate, and tartaric acid to 3.2 pH to observe the kinetics of phenolic
extraction from oak during the aging process. For the purpose of the current study, model wine
enabled panelists to isolate specific volatile compounds and determine their sensory effects.
Flavor Perception
Perception of flavor (i.e. strawberry, peach, mushroom) is often confused with taste.
Taste includes perception of bitter, salty, sweet, sour, and umami sensations. These perceptions
occur due to the presence of buds which line the tongue, epiglottis, and soft palate (Jackson
2002). Taste buds are receptors and their stimulation leads to signaling which travels to the
thalamus and cortex resulting in the sensation of taste (Jackson 2002). Flavor is considered the
result of retronasal stimulation (Meilgaard and others 2007). This occurs when volatile
compounds in the mouth and/or upper digestive tract are able to stimulate receptors in the nasal
passage. The stimulation of nasal receptors elicits the transmission of a signal to the brain,
specifically thought to be the orbitofrontal cortex, resulting in the perception of flavor (Jackson
2002). Some researchers feel that flavor should be defined as any perception originating in the
mouth (Meilgaard and others 2007). Meilgaard and others state that this would mean that taste
and flavor are combined under the same descriptor. Still, it is important to recognize that
different pathways are involved in eliciting these sensations. The focus of the current study will
be on flavor finish and not taste or mouthfeel finish. Panelists will be required to separate flavor
from taste and mouthfeel sensation when performing time intensity analysis.
14
Time Intensity
Time intensity is a method of sensory evaluation that allows the panelist to evaluate the
intensity of an attribute over a period of time. This method has been used to measure flavor
changes in chewing gum (Ovejero-Lopez and others 2005), evaluate astringency of alcoholic
beverages (Valentova and others 2002), and analyze differences in sweeteners (Bonnans and
Noble 1993). In wine, studies have been performed to analyze bitter and astringent sensations
caused by phenols (Robichaud and Noble 1990), astringency and sweetness
perception/interaction (Ishikawa and Noble 1995) and effectiveness of different palate cleansers
(Ross and others 2007).
The time period can be either fixed or unlimited based on the purpose of the study and/or
the discretion of the researcher. The data collected over a time period can then be evaluated
based on summary statistics which include Tmax, Imax, duration (Tend), and area under the curve
(AUC). Tmax indicates the time it takes to reach maximum intensity, whereas Imax indicates the
maximum intensity based on a scale of 0-100% low to high intensity. Duration indicates the
length of time a panelists takes to return to 0% intensity. Area under the curve (AUC) is an
integration which has a value that‘s interpretation can differ depending upon study objectives
(Meilgaard and others 2007). Values for increasing and decreasing inclines and their
corresponding areas are also generated. This is typically used as a computer based method and
the test requires that panelists be focused, understand the attribute in question, and understand
how to utilize the computer program. As a result of these requirements and the relative
complexity of time intensity tests, it is utilized in trained panels.
Due to the multitude of data points generated by the panelist, there is variation even
among trained panelists. Therefore, much discussion and research has been devoted to
15
determining the best method of analysis. The most basic analysis requires that the summary
statistics listed above be compared using analysis of variance (ANOVA) to determine significant
differences (MacFie and Liu 1992). However, in a 1992 paper, researchers MacFie and Liu
asserted that this method is insufficient as it does not take information from the entire curve.
MacFie and Liu proposed a normalization method that averages curves generated by all panelists
for a particular sample. Normalization occurs for both intensity and time as follows:
Intensity normalization:
Time normalization:
(MacFie and Liu 1992)
The intensity normalization equation compares the mean Imax value (Imax) to the
individual Imax value (Imax i). The time normalization also compares mean time values (tmax, tend,
tdec) to individual time values (tmax i, tend i, tdec i), but is more complicated as there are separate
equations to analyze the ascending (to Tmax) and descending (from Tmax) portions of the curve.
MacFie and Liu state that this method is most appropriate because it normalizes both variables
and preserves qualities of the curve created by the original data (MacFie and Liu 1992).
Principle Component Analysis (PCA) is also suggested by MacFie and Liu as a potential method
for analyzing data, particularly if great differences are seen in the data generated by the panelists.
PCA focuses on grouping of similar attributes and thus may aid in identifying similarities when
results seem quite dissimilar (MacFie and Liu 1992).
16
Time intensity studies have previously been performed with different types of alcoholic
beverages. A 2000 study by Piggott and others demonstrated the utilization of time intensity for
Scotch malt whisky. The objective of this study was to determine if time intensity could be used
as a viable testing method and determine the effect of barrel aging on a Scotch malt whisky
attribute. Five different barrel types were utilized and whisky was aged for 24, 30, 42, and 60
months. Panelists (n=13) evaluated the time intensity of sweetness over a 60 second time period
that consisted of a ten second in mouth holding time followed by swallowing and aftertaste
perception. Based on the data collected in this experiment, the researchers performed a
comparison of different data analysis methods. The researchers determined that no method of
analysis produced statistically significant results, which they felt was due to the extreme
variation observed between panelists. Utilization of the 1992 MacFie and Liu method previously
described provided what Piggott and others considered the most reliable results (Piggott and
others 2000).
Several studies have evaluated flavor perception over time with time intensity testing. A
1997 study by Mialon and Ebeler examined perception of vanillin and limonene compounds in
emulsions of varied oil to water ratios. Panelists evaluated flavor intensity for both in mouth
intensity and post expectoration intensity. Salivary flow was measured for each panelist, as it is
thought that factors that alter environment of the mouth may influence retronasal perception
(Mialon and Ebeler 1997). The two compounds did not display similar patterns of change as
the oil to water ratio in which the compounds were presented in was changed. Most notable,
duration of vanillin perception was heightened as oil content was raised. Significant differences
were observed when comparing duration of vanillin perception in the presence of 0% oil and
50% oil. Researchers felt this change may have been influenced by the salivary flow of
17
individual panelists. Limonene was perceived as less intense by panelists when oil content was
increased in the emulsion. The researchers attributed this to the nonpolar nature of the
compound (Mialon and Ebeler 1997). Time intensity studies provide unique methodology to
evaluate flavor perception. In the current study, flavor perception based on effects of barrel
aging procedures was be analyzed.
Barrel Aging of White Wines
Numerous studies have been completed regarding barrel aging of both red and white
wine. This is a topic of interest due to the unique flavor and aroma qualities oak barrels can
impart to finished wines. Barrel aging is related to the introduction/production of at least ten
volatile compounds. These compounds include furfural, eugenol, guaiacol, and oak lactone
(Towey and Waterhouse 19962). Additionally, profiling of flavor compounds derived from oak
barrels can give some indication of the origin and seasoning/treatment of the wood utilized
during aging.
Since oak lactone has been determined to have sensory impact on white wine, it is
important to determine factors that can lead to the increase or decrease of oak lactone throughout
the barrel aging process. A 1996 Towey and Waterhouse study examined the effect of the age of
the barrel on the volatile extraction and subsequent volatile composition of Chardonnay wines
aged in American, French, and Hungarian oak barrels. The researchers examined numerous
volatile compounds, including oak lactone. Oak lactone levels were highest in wines aged in one
year old barrels. Also, oak lactone were found at higher concentration in wines aged in new
barrels than in wines aged in barrels that were two years of age. Origin of oak also affected the
levels of oak lactone. New American and Hungarian oak barrels were found to lead to the
18
extraction of a lower concentration of volatiles than new French oak barrels (Towey and
Waterhouse 1996).
In an earlier Waterhouse and Towey study performed in 1994, GC was utilized to analyze
Chardonnay aged in American oak barrels and Chardonnay aged in French oak barrels. The
objective of this study was to determine whether or not ratios of cis and trans- oak lactone could
be utilized as markers of wood origin. The researchers stated that the ability to distinguish
between American and French oak had economic significance, as French oak was a more
expensive product. Measurements of alcohol content, pH, and titratable acidity did not differ
significantly between Chardonnays aged in American oak versus Chardonnays aged in French
oak. However, differences were observed when comparing levels of oak lactone found in
American and French oak aged wines. American oak aged Chardonnay typically had greater
levels of oak lactone, but researchers saw discrepancies among the data, with oak originating in
Oregon producing the lowest oak lactone levels of all oaks. Discrepancies in oak lactone levels
observed among wines exposed to American and French oak were not consistently observed.
Therefore, researchers deemed that this was not an appropriate parameter by which to distinguish
American and French oaks. Significant and consistent discrepancies were noted between
American and French ratios of cis/trans oak lactone. American oak had significantly higher
cis/trans oak lactone ratios (mean=6) than French oaks (mean=1.3). It was concluded that
analysis of cis/trans oak lactone ratios via GC was a viable method for determining wood origin
of barrels (Waterhouse and Towey 1994).
The oak barrel aging of wines impacts sensory characteristics by intensifying aroma notes
such as vanilla and oak. Oak extracts are thought to play a role in the intensification of wine
aromas. The objective of a 1992 study by Francis and others was to determine if wood origin
19
and processing contributed to the sensory qualities of oak wood extracts presented in a model
wine. Oak wood was collected from three French forests and one American forest. Three
seasoning treatments were evaluated: green, seasoned in the country of origin, and seasoned in
Australia. The oak was then either heat treated or not heat treated. Quantitative difference
testing was performed to analyze the aroma of the oak extracts. Country of origin was a
significant factor, with oak aromas in French oak being comparable or significantly more intense
than oak aromas in American oak. Green versus seasoned treatments were significantly different
for the aroma intensity of cedar, nutty, and raisin notes. Significant differences were also found
when comparing country of seasoning. There was a significant difference between the unheated
and heated treatments for the intensity of caramel, cedar, nutty, and raisin aromas. Oak origin,
seasoning, and heat treating created significant differences in aroma perception of oak extracts.
These differences can be observed by utilizing GC-MS.
Gas Chromatography-Mass Spectrometry Analysis of Oak Lactone
A method optimization for quantification of barrel aging compounds in wine using solid-
phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS) was
performed in a 2006 study by Carrillo and others. In this optimization, researchers altered
numerous factors and utilized model systems and red wine. The researchers explored the use of
four different types of SPME fibers, with divinylbenzene–carboxen–polydimethylsiloxane
(DVB–CAR–PDMS) determined to be of greatest use in the procedure. Optimal sodium
chloride content in headspace SPME vials was determine to be 30% (w/v) of the solution.
Finally extraction temperature and time were found to be significant factors when optimizing the
quantification of barrel aging compounds. Long time and high temperature were necessary to
elicit well-resolved peaks. The researchers recommended a 60 minute extraction carried out at
20
70° Celsius. With these parameters, the detection range for cis-oak lactone was 4-222 µg/L-1
(Carrillo and others 2006). Quantification of oak lactone by in a Towey and Waterhouse study
examining barrel aged Chardonnay resulted in a range of mean cis-oak lactone 89.2 – 188.9 µg/L
(Towey and Waterhouse 19962). This indicated that the method established by Carillo and other
displayed appropriate sensitivity for quantification of oak lactone in white wines.
Conclusions
Wine is a complex matrix and can thereby be influenced by numerous factors. Harvest
time, fermentation, and barrel aging are all factors that can significantly alter the composition of
either the non-volatile or the volatile fraction of white wines. By altering composition of the
wine, it is possible that wine finish is altered. Barrel aging provides an excellent example of this.
The barrel aging process is typically utilized on wine varieties such as Chardonnay which is
generally noted for having a comparably long finish for a white wine. Research shows that
barrel aging leads to the extraction of numerous volatile compounds, including oak lactone, a
compound associated with wood, vanilla, and coconut sensory attributes. Although research
correlating the production of oak lactone to wine finish has not previously been pursued, logic
dictates a potential connection between the enhanced finish of barrel aged white wines and
extracted compounds like oak lactone. Wine flavor and finish are important measures of quality.
Wine quality can determine economic success and therefore, the study of white wine finish is an
economically relevant issue.
21
CHAPTER 3
MATERIALS AND METHODS
Model wine
Panelists were presented with 9% EtOH, 0.6% fructose, 3.0 pH model wine. Model wine
was prepared with milliQ water, 200 proof ethanol (Decagon, Pullman, WA), D – (-)-fructose
(Sigma Aldrich, St. Louis, MO), and DL-tartaric acid (Sigma Aldrich, St. Louis, MO). Fruity,
floral, coconut, and mushroom were the descriptors used for 65.175 mg/L ethyl hexanoate, 43.1
mg/L linalool, 47.6 mg/L whiskey lactone, and 41.5 mg/L 1-octen-3-ol, respectively (Sigma-
Aldrich, St. Louis, MO). Concentrations were chosen based on reported flavor compound
threshold levels and bench testing. Flavor compound concentrations remained constant
throughout the panel.
Wines
Trained panelists and consumer panelists were given three different Washington State
Chardonnays. The wines evaluated were Columbia Crest unoaked Chardonnay (13.5% alcohol,
Paterson, WA 2008), Columbia Crest Horse Heaven Hills Chardonnay (13.5% alcohol, Paterson,
WA, 2008), and Columbia Crest Reserve Chardonnay (14.4% alcohol, Paterson, WA, 2008).
These wines corresponded to unoaked (not barrel aged), medium oak (70% oak barrel /30% steel
barrel aged), and high oak (100% oak barrel aged) treatments, respectively.
For the trained panel a base white wine was utilized. Carlo Rossi Chabli (9.5% alcohol,
Modesto, CA) was chosen as the base white wine. All wines, including the base wine and
Chardonnay wines, were purchased at the Pullman, Washington Safeway.
22
Trained Panel
Trained Panel Demographics
Ten panelists from the Washington State University School of Food Science community
participated in the study. Panelists were respondents to electronic advertisements. The panel
included 8 females and 2 males. Ages ranged from 24-62 with a mean age of 39.3 and a median
age of 31.5. All panelists reported drinking wine at least once a month with Chardonnay and
Riesling varieties being the most popular varieties. Panelists participated in a 24 sessions held at
the WSU Sensory Evaluation Facility. Non-monetary incentives were awarded at the end of
each completed session, with a final non-monetary incentive awarded for completion of the
trained panel.
Training Sessions
Training consisted of six one-hour sessions. On the first day of training, panelists were
introduced to the concept of wine finish with finish being defined as the lingering taste following
the swallowing of wine (Jackson 2000). Panelists sampled three commercial Chardonnays in
order to evaluate finish length, flavor, and intensity characteristics. The commercial wines
included two barrel aged Chardonnays and one unoaked Chardonnay. Wines were purchased
from Safeway (Pullman, WA) and included Toad Hollow Vineyards Unoaked Chardonnay
(13.9% alcohol, Healdsburg, CA 2009), Columbia Crest Horseheaven Hills Chardonnay (13.5%
alcohol, Paterson, WA 2008), and Toasted Head Chardonnay (14.5% alcohol, Woodbridge, CA
2009). Panelists had a group discussion about perceived finish differences between the wines,
and came to the consensus that barrel aged Chardonnays had longer finish than unoaked
Chardonnay.
23
The second training day included an introduction of the Wine Aroma Wheel (Noble and
others 1987). Panelists then evaluated four samples of EtOH, 0.6% fructose, 3.0 pH model wine.
Each sample contained one flavor compound (65.175 mg/L ethyl hexanoate, 43.1 mg/L linalool,
47.6 mg/L whiskey lactone, or 41.5 mg/L 1-octen-3-ol). Panelists were given the descriptors
fruity, floral, coconut, and mushroom to identify each compound. Tasting protocol for model
wine samples was discussed. Panelists were told to hold each sample in their mouth for 10
seconds before swallowing or expectorating the sample.
Panelists were given model wine without added flavor compounds at the beginning of the
third day. Panelists were instructed to taste the model wine so that they could differentiate
between model wine attributes/sensations and flavor in later evaluation sessions. Ethanol burn
and ethanol sweetness were discussed in order to aid panelists in overlooking these perceptions
during formal evaluation and concentrate on flavor perception. Familiarization with the four
flavor compounds continued. Panelists tasted four model wine solutions with single flavor
compounds. Once panelists felt they could comfortably identify each flavor in model wine, a
base wine sample (Carlo Rossi Chabli, 9.5% alcohol, Modesto, CA) was served as a reference.
Then, two base wine samples were served. Each base wine sample contained a single flavor
compound. Floral and coconut flavors were introduced in the base wine. Flavor identification
with descriptors (fruity, floral, coconut, and mushroom) was performed on the model and base
wine. The panel discussed flavor intensity, finish, and overall perception of samples.
The fourth training day began with the introduction of mushroom and fruity flavors in
base wine. Panelists were then given a 15-cm line scale marked with ten second intervals. High
and low intensity were represented by a vertical scale. Panelists were instructed to taste samples
of model wine with single flavor compounds as a group and record perceived finish at ten second
24
intervals that were verbally announced by the trainer. This exercise helped prepare panelists for
the computer time intensity evaluation they would perform in formal evaluations.
In-booth computer training began on the fifth day. Panelists evaluated Listerine strips
(Johnson and Johnson, New Brunswick, NJ) and cinnamon candies (Brachs, Round Lake, MN)
utilizing the Compusense (Compusense 5.2, Guelph, ON) time intensity program. A five minute
waiting period took place followed by evaluation of fruity, floral, coconut, and mushroom
flavors in model wine.
The final training session included a group discussion of a flavored model wine sample.
Topics including ethanol perception, flavor perception, and computer software usage were
discussed. Panelists repeated in-booth evaluation of fruity, floral, coconut, and mushroom
flavors in model wine with the time intensity program.
Trained Panel Formal Evaluation
Panelists performed a time intensity evaluation of a single flavor in model wine with
flavor compound mixtures of increasing complexity. Analysis began with model wines
containing one compound, followed by model wines with two compounds and then four
compounds. Panelists were given 2 minutes and 30 seconds to analyze each sample with two
minutes between each sample. Unsalted saltine crackers and milliQ water were provided for
palate cleansing between samples.
Two compound analysis included 6 different flavor solutions with two evaluations per
solution (i.e. evaluating floral flavor in a fruity and floral mix and then evaluating fruity flavor in
the same mixture). This led to twelve overall evaluations which, for clarity in later discussion,
25
are coded with the flavor being evaluated listed first:
1: Coconut and floral
2:Coconut and fruity
3: Coconut and mushroom
4: Floral and coconut
5:Floral and fruity
6:Floral and mushroom
7:Fruity and coconut
8:Fruity and floral
9:Fruity and mushroom
10:Mushroom and coconut
11:Mushroom and floral
12:Mushroom and fruity
Time intensity analysis of one compound, two compound, and four compound solutions was
repeated in base wine. Carlo Rossi Chablis (9.5% alcohol, Modesto, CA) was used as the base
wine.
Trained panelists performed a time intensity analysis of three commercially produced
Washington State Chardonnays. Time intensity parameters collected included time of maximum
intensity (Tmax), intensity maximum (Imax), duration (Tend), and area under the curve (AUC).
Data Analysis
Time intensity parameters including Tmax, Imax, Tend, and area were analyzed by XLStat
2011 (Addinsoft, Paris France) and Stata (StataCorp LP, College Station, Texas). Three-way
analysis of variance (ANOVA) with panelist, wine, and replicate main effects was performed
followed by Tukey‘s Honestly Significant Difference (HSD) test. Data were then normalized
following the Liu and MacFie method (1992). After normalization, principle component
analysis (PCA) was performed using mean time intensity parameters from normalized data.
26
Consumer Panel
Consumers (n=60) participated in a study to evaluate white wine finish. Consumers were
respondents to electronic and posted advertising. Demographic data indicated that the panel was
composed of 33 males and 26 females (1 panelist preferred not to answer) with 31 panelists age
21-29, 8 panelists age 30-39, 5 panelists age 40-49, 11panelists age 50-59, and 4 panelists age
60 and over (1 panelist preferred not to answer). Forty-six panelists were occasional white wine
drinkers (a few times per month), 8 panelists identified themselves as frequent white wine
drinkers (a few times per week), and 1 panelists identified themselves as a daily drinker of white
wine. Five panelists reported that they never drink white wine. Panelists reported drinking the
following white wine varieties: Riesling (51.7%), Chardonnay (50%), Sauvignon Blanc (31.7%),
Gewürtraminer (30%), Pinot Grigio (28.3%), and other (36.7%). The panel took place at the
WSU Sensory Evaluation Facility and consumers were given non-monetary incentives for their
participation. Compusense 5.2 (Guelph, ON) was utilized to perform sensory evaluation.
Consumer Evaluation
Panelists were asked to time how long they perceived wine finish and express this time in
seconds. To do this, panelists were instructed to sample the wine and then use the digital timer
(Fisher Scientific, Santa Clara, CA) provided to record the length of the wine after taste in
seconds. The experimental design was a complete balanced block with monadic presentation.
Panelists were given unsalted crackers, a cuspidor, and milliQ water. Panelists were instructed
to wait for thirty seconds in between samples and cleanse their palates with water and crackers.
Panelists were then asked to rate on a 7-point hedonic scale how much they liked the
wine finish with 1=dislike very much and 7 = like very much. Finally, panelists rated their
willingness to purchase each wine using a branched question format. Each panelist was first
27
asked if they would be willing to pay ten dollars for a bottle of the wine they had sampled. If the
response was positive, the dollar amount increased by two dollars (up to 14 dollars). If the
response was negative, the amount decreased by two dollars (down to 6 dollars).
Data Analysis
Data were analyzed by XLStat 2011 (Addinsoft, Paris France). Two-way analysis of
variance with panelist and wine main effects was performed followed by Tukey‘s HSD. Branch
willingness to purchase data was converted to numerical data that represented dollar ranges to
allow for analysis by an ordered probit model (Version 10, Stata Corp, College Station, TX).
Gas Chromatography Mass Spectrometry (GC-MS)
Methods were chosen based on a 2006 optimization performed by Carillo and others.
GC-MS was performed to analyze the wines utilized in the consumer panel. A standard curve
was made utilizing model wine with increasing levels of whiskey lactone (Sigma Aldrich).
Model wine was 9% ethanol, 0.6% D – (-)-fructose, DL-tartaric acid, adjusted to pH 3.0. The
standard curve was prepared by diluting a 10 mg/L whiskey lactone (Sigma Aldrich) stock
solution to concentrations of 0.1, 0.5, 1.0, 1.5, 2.0, and 3.0 mg/L whiskey lactone in model wine.
Samples were prepared by placing 5 mL of sample in 10 ml SPME vials. Sodium
chloride (J.T. Baker, Austin, TX) was added to achieve 30% (w/v) salt concentration. 1-
dodecanol was added as an internal standard by adding 0.5 µL of 250 mg/L 1-dodecanol solution
for a final concentration of 0.025 mg/L to samples of model wine with whiskey lactone or
Chardonnay. Samples were stirred for 5 minutes after the addition of the internal standard.
A divinylbenzene–carboxen–polydimethylsiloxane (DVB–CAR–PDMS) fiber was
utilized in combination with a CTC Combi-PaL autosampler (Zwingen, Switzerland) to extract
28
sample volatiles. Pre-incubation occurred for 10 minutes at 70° C. Extraction time was 1 hour
at 70° C without agitation.
An Agilent GC 6890N chromatograph with MS 5975 mass spectrometer was utilized to
analyze wine samples. A HP-5MS (30.0 m x 250 µm x 0.25 µm) column was used and splitless
injection (using helium carrier gas) was performed at 260°C with a 7 minute desorption time.
Temperature program began at 40° C with a hold of 3 minutes. After the initial 3 minute at 40°
C, temperature ramped at 7° C per minute to a maximum temperature of 230° C followed by a
hold time of 10 minutes, for a total program time of 40.14 minutes. The mass spectrometer
detector collected data using SCAN mode (33 m/z to 300m/z). The NIST mass spectra library
(version 2.0d) was used to confirm compound identities.
29
CHAPTER 4
RESULTS AND DISCUSSION
Trained Panel
Single Flavor Compound Analysis in Model Wine
Analysis of a variance (ANOVA) evaluation of solutions containing a single flavor
compound in model wine (Table 1) determined that there were no significant differences in time
to maximum intensity (Tmax) for flavor (coconut, fruity, floral, and mushroom) or replicate
evaluation. No significant interactions for panelist*flavor was found. However, a significant
panelist effect (p<0.0001) was observed which was not unexpected. Previous research has
indicated that panelists create characteristic time intensity curves which, while reproducible for
each panelist, often result in dissonance amongst panelists (Noble 1995, van Buuren 1992). In
the statistical model used in this study, panelists were treated as fixed effect. O‘Mahony states
that this is typical in trained panel evaluations, as the results apply only to the panel, not to the
population at large, since trained panelists are purposefully selected and trained and therefore
may not represent the entire population (O‘Mahony 1986).
Lack of significant differences in Tmax between flavors may be due to the super-threshold
concentrations of the individual flavor compounds. Super-threshold concentrations could cause
flavors to be perceived at saturation levels very quickly, leading to limited Tmax variation. This
would be consistent with the principle of Rmax from the Beidler Model of stimulus response.
Rmax is the value corresponding to maximal response to a stimulus (Meilgaard and others 2007).
It is possible that all flavors were presented at, or above, Rmax and therefore panelists perceived
Tmax similarly.
30
Panelist (p<0.0001) and flavor (p=0.003) significantly impacted the perceived maximum
intensity (Imax) in solutions containing a single flavor compound in model wine (Table 2). A
significant panelist*flavor interaction (p=0.016) indicated that variation among panelists
influenced the variation among flavors. Comparison with Tukey‘s HSD (Table 3) showed a
significant difference between fruity and mushroom flavors (p=0.002). Mean separation (Table
8) indicated that panelists perceived mushroom to have a higher mean Imax than fruity. For these
studies, concentrations of flavor compounds were selected to be similar in intensity. Significant
intensity differences in only one of six possible comparisons of flavor compound intensity
indicated that the selected concentrations were reasonably similar.
Significant differences in duration (Tend) (Table 4) were found for panelist and flavor
(p<0.0001) for single flavor compounds in model wine. There was significant panelist*flavor
interaction (p=0.033) indicating that variation between panelists caused some of the variation
between flavors. However, the strength of the significance for the flavor main effect indicated
that not all of the observed variation was a result of panelist interaction. Pairwise comparison
(Table 5) followed by mean separation (Table 8) showed that fruity flavor finished significantly
earlier than coconut (p<0.0001), floral (p=0.001), and mushroom (p<0.0001) flavors. Fruity
flavor finished 29.9 to 47.1 seconds earlier than the other flavors. This result supports the
hypothesis that fruity flavor would finish earlier than coconut and mushroom flavors. The
hypothesis that floral flavor would finish earlier than coconut and mushroom flavors is supported
numerically (second shortest mean finish time), but it is not supported through statistically
significant results. Mean Tend values show that flavors finish in the following order: fruity, floral,
coconut, and mushroom. This order is reflected in Figure 2 which shows the time intensity
curves from a representative panelist for each flavor.
31
Area under the curve (AUC) data from single flavor compounds in model wine (Table 6)
showed significant panelist (p<0.0001) and flavor (p=0.000) main effects. No significant
interactions were observed. Pairwise comparison (Table 7), followed by mean separation
(Table 8) showed that fruity flavor had a significantly smaller AUC than floral (p=0.023) and
mushroom (p=0.000).
PCA analysis of normalized single flavor compounds in model wine was also performed
(Figure 3). The PCA analysis indicated that variation in the coconut flavor was best described
the Tmax parameter. Variation in mushroom and floral flavors was best described by the
remaining parameters of Imax, Tend, and AUC. Additionally, with PC1 accounting for 71.6% of
variation, the majority of the variation observed among the flavors is a result of the relationship
of fruity and mushroom flavors. This reflects patterns seen in the standard data analysis, which
showed fruity flavor having significantly lower values for Imax, Tend, and AUC than mushroom
flavor.
The effect of data normalization is demonstrated in Figures 4, 5, 6, and 7. Normalization
allowed averaged curves evaluated by a standard ANOVA procedure to be compared to
mathematically altered (normalized) curves. Normalized curves took into account the behavior
of all the trained panelists, but unlike averaging, still allowed the characteristics of an
individual‘s evaluation to be represented (MacFie and Liu 1992).
Multiple Flavor Compound Analysis in Model Wine
Panelists evaluated solutions containing two flavor compounds in model wines. Panelists
were asked to evaluate a specific flavor of the two flavors presented. For the parameter Tmax
(Table 9), there was a significant panelist effect (p<0.0001), which then translated to a
32
significant panelist*flavor interaction (p=0.001). However, flavor and replicate were not
significant.
There were significant panelist (p<0.0001) and flavor (p<0.0001) effects for the
parameter Imax (Table 10), without any significant interactions. Pairwise comparison of the
flavor solutions indicated that the Imax of solution 12 (see p. 26 for code) was significantly larger
than solution 1, 3, and 9. Solution 11 was significantly larger than solution 9. This indicated
that maximum mushroom intensity (in the presence of fruity and floral flavors) was perceived
are more intense than fruity and coconut flavor in two compound evaluations. The Imax of
solution 4 was significantly larger than solutions 1 and 9, indicating that floral flavor in the
presence of coconut flavor had a greater maximum intensity than coconut flavor evaluation in the
presence of floral flavor and fruity flavor evaluation in the presence of mushroom flavor.
Mushroom flavor may have acted as a suppressor of fruity flavor in this instance. This may be
similar to suppression effects caused by Brettanomyces aroma compounds 4-ethylphenol and 4-
ethylguaiacol (Bramley and others 2008). In a 2008 study by Bramley and others, a base wine
without added Brettanomyces aroma compounds was perceived to have higher fruit aroma than
wines with added 4-ethylphenol and 4-ethylguaiacol (Bramley and others 2008).
Significant panelist (p<0.0001) and flavor (p=0.001) effects were seen for Tend evaluation
in two compound model wine solutions (Table 11). A significant panelist*flavor interaction
(p=0.003) was observed, indicating that panelist contributed to some of the variation seen among
flavors. Pairwise comparison showed solutions 2, 4, 11, and 12 as significantly larger than
solution 8. Solution 8 was had the smallest mean Tend of all 12 evaluations, indicating that fruity
(which also had the smallest mean Tend in single compound evaluations) may be further
suppressed by the presence of floral flavor.
33
Finally, area under the curve for two compounds in model wine was analyzed (Table 12).
Significant panelist (p<0.0001) and flavor (p<0.0001) were observed with no significant
replicate effect or interactions. Pairwise comparison shows that solution 12 area was
significantly larger than area for solutions 7, 8, and 9. Solution 2 area was significantly larger
than solution 8 area. Solution 2 and 11 areas were significantly larger than solution 9 area. This
indicates that fruity flavor perception may be suppressed by the presence of addition flavors,
particularly mushroom. However, it may also be seen (as described above) as a result of having
lower mean Tend values without significantly higher Imax values.
Contrasts of the four overall flavors (coconut, floral, fruity, and mushroom) were
performed. No significant differences were observed for the parameter Tmax. Mushroom and
floral flavors had significantly higher Imax values than coconut and fruity flavors. Fruity flavor
had a significantly shorter Tend and area under the curve than coconut, floral, and mushroom.
Principle component analysis was performed on the normalized data for two compounds
in model wine solutions (Figure 8). Solutions 3 and 6 were best described by the parameter
Tmax, while Imax described solution 7 and 8. Tend described solution 2. Area describes variation
for solution 4, 11, and 12. PC1 represented 64.7% of observed variation. The opposing
relationship of fruity perception and mushroom perception accounted for part of the PC1
variation, which is a similar outcome to the observations from the standard data analysis
Solutions with four flavor compounds in model wines were evaluated by the trained
panelists. For the parameter Tmax (Table 14), a significant panelist effect (p<0.0001) was
observed. No significant main effects were found for the parameter of Imax when trained
panelists evaluated four flavor compounds in model wine (Table 15). Significant panelist
(p<0.0001) and flavor (p=0.022) effects occurred for Tend (Table 16). Pairwise comparison
34
(Table 17) followed by mean separation (Table 20) indicated that floral flavor Tend was
significantly (p=0.017) longer than fruity flavor. Floral flavor had the longest Tend of all four
flavors. This result differs from the single compound flavor solutions, in which floral flavor
finished before coconut and mushroom flavors. These differences may be explained by effects
caused by the presence of the additional flavors in solution. For instance, flavor enhancement, or
the increase in perception of one flavor cause by the presence of other flavors (Meilgaard and
others 2007), could cause elongated perception of floral flavor not observed in single compound
model wine solutions.
The concept of flavor enhancement increasing floral flavor perception is further
supported by results for the parameter AUC for the four compound model wine solutions (Table
18). There were significant panelist (p<0.0001) and flavor (p=0.032) effects. Pairwise
comparison (Table 19) followed by mean separation (Table 20) showed that floral flavor had a
significantly larger (p=0.021) AUC than fruity flavor, and the largest mean AUC of all four
flavors. The large AUC for floral flavor indicated a larger percentage of flavor perception. As
with differences in Tend, enhancement effects caused by the presence of the additional flavors
could create significant differences between fruity and floral that did not exist when the flavors
were evaluated alone.
PCA of normalized data for four compounds in model wine (Figure 9) further explained
variation among flavors. Coconut and floral flavor variation were best described by the
parameters Imax, Tend, and Area. Mushroom flavor variation was best described by Tmax.
Coconut and floral flavor have similar characteristics, but variation in the data can be attributed
to the relationship of coconut and floral flavor to fruity flavor. The antagonist relationship
between floral and fruity flavor reflects differences observed in standard data analysis.
35
Single Flavor Compound Analysis in Base Wine
Base wine solutions with single flavor compounds were evaluated by the trained
panelists. With the exception of significant panelist effect (p=0.039) for Tmax (Table 21) and
significant panelist effect (p<0.0001) for Imax (Table 22), Tend (Table 23), and AUC (Table 24),
no significant differences were found among flavors for any of the time intensity parameters.
This could be the result of the panelists adjusting to the complexity of the base wine (compared
to the model wine solutions). Non-significant results could also be the result of flavor
suppression caused by flavor compounds already present in the base wine. Suppression occurs
when the presence of one or more flavor compounds lessens the perception of another flavor
compound (Meilgaard and others 2007). A time-intensity evaluation of suppression and
enhancement effects on flavor was performed in a 1993 study by Bonnans and Noble. In the
study the effect of sweet and sour tastes on fruit flavor was observed. Bonnans and Noble found
changes in fruity flavor maximum intensity and duration, both of which were perceived to be
higher in sweet and sour taste solutions (Bonnans and Noble 1993). Similar work observing
changes in time intensity parameters when flavors are compared has not been completed.
PCA analysis of normalized data from single compound in base wine (Figure 10)
evaluation further described the relationship of flavors and time-intensity parameters. Variation
was explained by the relationship between fruity and floral, as was seen in both standard and
normalized four compound in model wine evaluations. Variability in coconut flavor perception
could be attributed to Tend. Tmax and AUC described variation in floral flavor. Mushroom flavor
variation was best described by Imax.
36
Multiple Flavor Compound Analysis in Base Wine
Trained panelists evaluated two compounds in base wine. Aside from a significant
panelist effect (p<0.0001), no significant effects or interactions were observed for Tmax (Table
25). Significant panelist (p<0.0001) and flavor (p<0.0001) effects and significant panelist*flavor
(p=0.006) interactions were observed for Imax (Table 26). Pairwise comparison showed that
solution 4, 5, 6, 8, 10, 11, 12 had significantly higher Imax values than solution 1. This indicated
that coconut flavor intensity may have been suppressed by floral flavor. Solution 11 had a
significantly higher Imax value than solution 9.
Analysis of the parameter Tend in two compounds in base wine evaluations (Table 27)
showed a significantly panelist effect (p<0.0001) and a significant panelist*flavor interaction
(p=0.041). However, there was not a significant flavor effect. Area under the curve (Table 28)
analysis showed a significant panelist (p<0.0001) and flavor (p=0.001) effect with a significant
panelist*flavor interaction (p<0.0001). This indicated that variation among panelists contributed
to the variation observed in flavors. Pairwise comparison of solutions indicated that solutions 4,
10, and 11 had significantly larger area under the curve than solution 1. Coconut perception may
be masked or suppressed by floral flavor perception.
Contrasts were performed for overall coconut, floral, fruity, and mushroom flavors
(Table 29). No significant differences were observed between flavors for Tmax or Tend.
Significant differences were observed for the parameter Imax, with mushroom and floral flavors
having significantly higher mean Imax values than coconut flavor. Mushroom flavor had a
significantly higher Imax value than fruity flavor. Mushroom flavor also had significantly larger
area under the curve than coconut and fruity flavor.
37
Principle component analysis was performed on normalized data for two compounds in
base wine solutions (Figure 11). The parameter Tmax best described solutions 2 and 3.
Parameters Imax, Tend, and area best described solutions 4, 5, 6, 10, 11, and 12.
Four compounds in base wine were analyzed by trained panelists. Significant panelist
(p<0.0001) and flavor (p=0.023) effects were observed for the parameter Tmax (Table 30).
Pairwise comparison (Table 31) followed by mean separation (Table 37) showed significant
differences between the evaluation of coconut and fruity flavors (p=0.035) and coconut and
floral flavors (p=0.038). Suppression and/or enhancement may have caused significant
differences among flavors for Tmax that were not observed in single compound base wine
solutions.
A similar suppression/enhancement issue may have also occurred for the parameter Imax
(Table 32). Significant main effects for panelist (p<0.0001) and flavor (p=0.052) were
observed. A significant interaction for panelist*flavor (p=0.033) was also observed, indicating
that variability amongst panelist contributed to some of the variability observed between flavors
Pairwise comparison (Table 33) followed by mean separation (Table 37) indicated that floral
had a significantly higher mean Imax than coconut flavor (p=0.033). Floral flavor may be
enhanced by the flavor compounds creating the fruity, coconut, and mushroom flavors or other
flavor compounds that were preexisting in the base wine. Alternatively, coconut flavor may
have been suppressed by the presence of additional flavor compounds. This explanation would
also explain the significantly higher Tmax observed in coconut evaluations. Panelists may have
had to wait for the effects of other flavor compounds that were suppressing coconut to lessen
before being able to observe peak coconut flavor.
38
Significant differences were not observed for Tend in the four compounds in the base wine
(Table 34) with the exception of a significant panelist effect (p<0.0001). Panelists may not have
found differences due to suppression from pre-existing flavors in the base wine. Significant
differences for the main effects of panelist (p<0.0001) and flavor (p=0.008) for the parameter
area were observed (Table 35). Pairwise comparison (Table 36) followed by mean separation
(Table 37) showed significant differences (p=0.004) between floral and coconut evaluation.
Coconut AUC was 1679.8 as compared to floral area AUC which was 2587.05. Differences in
AUC between coconut and floral flavor further supported the explanation that coconut was
suppressed in the four compound solutions. Coconut‘s decreased AUC indicated that a
suppressed perception occurred for the panelists.
PCA analysis of normalized data for four compound solutions in base wine (Figure 12)
indicated that the parameters Imax, Tend, and AUC best characterized floral flavor variation.
Variation in the mushroom flavor was best explained by the parameter Tmax. The relationship
between floral and coconut explained part of the PC1 variation and reflected differences
observed in the standard data analysis.
Trained panelists evaluated three commercially available Chardonnays. Aside from a
significant panelist effect, panelists did not find any significant differences for Tmax (Table 38),
Imax (Table 39), Tend (Tabel 40), or AUC (Table 41). Of these parameters, Tend was of greatest
interest. Mean Tend values were 91.611, 96.056, and 110.389 for unoaked, medium oak, and high
oak samples, respectively. These results indicated that the data followed the hypothesized
numerical trend for the data, but it failed to yield statistically significant results.
39
Consumer Panel Evaluation of Commercial Chardonnay:
Consumers found significant differences (p=0.051) in the length of finish when
evaluating three commercially produced Washington State Chardonnays (Table 42). A
significant panelist effect (p<0.0001) was observed, but this can be accounted for by several
factors including physiological differences in taste perception among panelists and the
complexity of the testing procedure for untrained panelists. Pairwise comparison with Tukey‘s
HSD determined there was a significant difference (p=0.059) in the perception of finish between
Unoaked Chardonnay and high oak Chardonnay (Table 43). The mean separation of the three
Washington State Chardonnay wines (Table 44) indicated that Unoaked Chardonnay‘s mean
finish of 45.8 seconds was significantly shorter than the high oak Chardonnay with a mean finish
of 53 seconds. The medium oak Chardonnay had a mean finish length of 51.817 seconds and
was not significantly different from either the high or no oak Chardonnay samples. The increase
in finish length of the wines with increased oak exposure and significantly longer finish time of
the high oak Chardonnay wine supports the study‘s hypothesis. These differences may have
resulted from the addition of non-volatile and volatile components attained from barrel exposure
(Towey and Waterhouse 1996).
The consumer panel also evaluated how much they liked the aftertaste/finish of the
Washington State Chardonnay on a seven-point hedonic scale (Table 45). Significant
differences were found between the wines (p=0.059). Pairwise comparison with Tukey‘s HSD
of the three Washington State Chardonnay wines determined a significant difference (p=0.046)
between unoaked and high oak wines. Mean separation data (Table 46) showed that unoaked
Chardonnay had a higher mean score than high oak Chardonnay, indicating that the aftertaste of
the unoaked Chardonnay was preferred. These results support those found in a 2011 study
40
(Stump and others) which also found that consumers preferred unoaked Chardonnay
significantly more than high oak Chardonnay. Significant panelist effect was also observed
(p<0.0001).
Willingness to purchase response data were coded so that dollar value ranges
corresponded to a value (Table 47). Significant differences (p=0.036) in willingness to purchase
were found between the unoaked and high oak Chardonnay samples (Table 48). Panelists were
willing to pay higher dollar amounts for a bottle of unoaked Chardonnay than high oak
Chardonnay. Willingness to pay higher prices for unoaked wine was counterintuitive since
barrel aged wines tend to be more expensive and regarded as higher quality than unoaked wines
(Stump and others 2011). However, Stump and others found similar results, with consumer
panelists willing to pay $0.67 more per bottle for unoaked Chardonnay than highly oaked
Chardonnay. These results indicated that quality does not always positively influence consumer
preference or hedonic evaluation of wine.
Gas Chromatography – Mass Spectrometry (GC/MS):
MS library identification of the two oak lactone isomers was not able to distinguish
between the two compounds. Although oak lactone was recognized, the library classified both
peaks as the cis isomer. Thus, in order to identify and quantify both isomers, further
investigation would be necessary. Although definitive ion based identification was not
accomplished, it was assumed that the later eluting oak lactone was the cis isomer. In the
Chardonnay wines analyzed, the later eluting lactone displayed a much larger peak area than the
earlier eluting lactone, translating to a higher overall concentration. This is consistent with a
study by Towey and Waterhouse (1996) which examined variation within lots of barrel aged
Chardonnay and between American and French barrel aged Chardonnay. This study found that
41
the cis isomer at a consistently higher concentration than the trans isomer. Additionally, a study
by Perez-Coello and others (1997) presented chromatographic data that displayed the trans
isomer as having a smaller peak area and eluting earlier than the cis isomer (Perez-Coello and
others 1997).
Unoaked Chardonnay wines did not contain cis or trans oak lactone. This is expected
since the isomers originate from exposure to oak (Brown and others, 2006). High oak
Chardonnay samples had higher concentrations of cis and trans lactone than medium oak
Chardonnay. For cis-oak lactone, high oak Chardonnay samples had a mean concentration of
0.358 ug/L, compared to a mean concentration of 0.289 ug/L in medium oak Chardonnay
samples. For trans-oak lactone, high oak Chardonnay samples had a mean concentration of
0.120 ug/L, compared to a mean concentration of 0.0757 ug/L in medium oak Chardonnay
samples. Ratios of cis/trans oak lactone were 3.82 and 2.98 for medium and high oak
Chardonnay respectively. These values were within the 1.31-7.35 cis/trans oak lactone ratio
range found 2Towey and Waterhouse (1996).
42
Table 1. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for single
compounds (floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 4864.00 540.44 9.27 < 0.0001
Flavor 3 415.75 138.58 2.38 0.085
Replicate 1 5.00 5.00 0.09 0.771
Panelist*Flavor 27 1270.00 47.04 0.81 0.718
43
Table 2. Degrees of freedom and F values from analysis of variance (ANOVA) table of maximum intensity (Imax) for single
compounds (floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 13452.80 1494.76 12.81 < 0.0001
Flavor 3 1935.25 645.08 5.53 0.003
Replicate 1 92.45 92.45 0.79 0.379
Panelist*Flavor 27 6668.50 246.98 2.12 0.016
44
Table 3. Pairwise comparison of maximum intensity (Imax) evaluations for single compounds in model wine samples analyzed by a
trained panel (n=10) using Tukey‘s HSD (p≤0.05).
Contrast Difference Standardized difference Critical value Pr > Diff
Mushroom vs Fruity 13.45 3.94 2.68 0.002
Mushroom vs Coconut 8.60 2.52 2.68 0.073
Mushroom vs Floral 5.05 1.48 2.68 0.460
Floral vs Fruity 8.40 2.46 2.68 0.083
Floral vs Coconut 3.55 1.04 2.68 0.728
Coconut vs Fruity 4.85 1.42 2.68 0.495
Tukey's d critical value:
3.79
45
Table 4. Degrees of freedom and F values from analysis of variance (ANOVA) table of duration (Tend) for single compounds (floral,
fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 59994.30 6666.03 11.88 < 0.0001
Flavor 3 24673.45 8224.48 14.66 < 0.0001
Replicate 1 162.45 162.45 0.29 0.594
Panelist*Flavor 27 28806.80 1066.92 1.90 0.033
46
Table 5. Pairwise comparison of duration (Tend) evaluationsfor single compounds in model wine samples analyzed by a trained panel
(n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Mushroom vs Fruity 47.05 6.28 2.68 < 0.0001
Mushroom vs Floral 16.70 2.23 2.68 0.133
Mushroom vs Coconut 9.95 1.33 2.68 0.551
Coconut vs Fruity 37.10 4.95 2.68 < 0.0001
Coconut vs Floral 6.75 0.90 2.68 0.804
Floral vs Fruity 30.350 4.052 2.68 0.001
Tukey's d critical value:
3.79
47
Table 6. Degrees of freedom and F values from analysis of variance (ANOVA) table of area under the curve (AUC) for single
compounds (floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 66332278.63 7370253.18 6.47 < 0.0001
Flavor 3 27451590.91 9150530.30 8.03 0.000
Replicate 1 31660.90 31660.90 0.03 0.869
Panelist*Flavor 27 34231621.43 1267837.83 1.11 0.374
48
Table 7. Pairwise comparison of area under the curve (AUC) evaluations for single compounds in model wine samples analyzed by a
trained panel (n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Mushroom vs Fruity 1639.33 4.86 2.68 0.000
Mushroom vs Coconut 824.85 2.44 2.68 0.086
Mushroom vs Floral 625.20 1.85 2.68 0.265
Floral vs Fruity 1014.13 3.00 2.68 0.023
Floral vs Coconut 199.65 0.59 2.68 0.934
Coconut vs Fruity 814.48 2.41 2.68 0.091
Tukey's d critical value:
3.79
49
Table 8. Mean Separation of Maximum Intensity (Imax), Duration (Tend), and area under the curve (AUC) for single compounds in
model wine samples analyzed by the trained panel (n=10) using Tukey‘s HSD. Different letters indicate significant differences
between the flavors (p≤0.05).
Flavor Mushroom Floral Coconut Fruity
Imax LS means 67.20a 62.15ab 58.60ab 53.75b
Tend LS means 137.10a 120.40a 127.15a 90.05b
Area LS means 4052.80a 3427.60a 3227.95ab 2413.48b
50
Figure 2. Representative trained panelist time intensity evaluation of single flavors in model wine
0
10
20
30
40
50
60
70
0 50 100 150 200
Inte
nsi
ty
Time (sec)
Floral
Coconut
Mushroom
Fruity
51
Figure 3. Pearson‘s principle component (PC) analysis factor loadings and factor scores from normalized time intensity data of single
compounds in model wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum
intensity).
Coconut
Floral
Fruity
Mushroom
Tmax
Imax
Tend
AUC
-2
0
2
-5 -3 -1 1 3 5
F2 (
26.4
%)
F1 (71.6 %)
52
Figure 4. Comparison of standard data and normalized data from a representative trained panelist‘s time intensity evaluation of the
coconut flavor in single compound model wine
0
10
20
30
40
50
60
70
0 50 100 150 200 250
Inte
nsi
ty
Time (sec)
Standard Data
Normalized Data
53
Figure 5. Comparison of standard data and normalized data from a representative trained panelist‘s time intensity evaluation of the
floral flavor in single compound model wine
0
10
20
30
40
50
60
70
0 50 100 150 200 250
Inte
nsi
ty
Time (sec)
Standard Data
Normalized Data
54
Figure 6. Comparison of standard data and normalized data from a representative trained panelist‘s time intensity evaluation of the
fruity flavor in single compound model wine
0
10
20
30
40
50
60
0 20 40 60 80 100 120 140 160
Inte
nsi
ty
Time (sec)
Standard Data
Normalized Data
55
Figure 7. Comparison of standard data and normalized data from a representative trained panelist‘s time intensity evaluation of the
mushroom flavor in single compound model wine
0
10
20
30
40
50
60
70
80
0 50 100 150 200 250
Inte
nsi
ty
Time (sec)
Standard Data
Normalized Data
56
Table 9. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for two
compounds (all possible combinations of floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel
(n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 22098.87 2455.43 38.10 < 0.0001
Flavor 11 1260.17 114.56 1.78 0.064
Replicate 1 223.99 223.99 3.33 0.070
Panelist*Flavor 99 11722.04 118.40 1.83 0.001
57
Table 10. Degrees of freedom and F values from analysis of variance (ANOVA) of maximum intensity (Imax) for two compounds (all
possible combinations of floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 54284.37 6031.60 32.72 < 0.0001
Flavor 11 8082.51 734.77 3.99 < 0.0001
Replicate 1 324.34 324.34 1.76 0.187
Panelist*Flavor 99 23167.28 234.01 1.27 0.106
58
Table 11. Degrees of freedom and F values from analysis of variance (ANOVA) of duration (Tend) for two compounds (all possible
combinations of floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 284953.52 31661.50 44.95 < 0.0001
Flavor 11 24423.11 2220.28 3.15 0.001
Replicate 1 182.00 182.00 0.26 0.612
Panelist*Flavor 99 117580.43 1187.68 1.69 0.003
59
Table 12. Degrees of freedom and F values from analysis of variance (ANOVA) of area under the curve (AUC) for two compounds
(all possible combinations of floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 354265928.17 39362880.91 32.24 < 0.0001
Flavor 11 56307312.69 5118846.61 4.19 < 0.0001
Replicate 1 27767.26 27767.26 0.02 0.880
Panelist*Flavor 99 116624965.35 1178029.95 0.97 0.571
60
Table 13. Mean separation of contrasts of the four flavors (floral, fruity, mushroom and coconut) analyzed in two compound model
wine solutions evaluated by a trained panel (n=10), p≤0.05.
Flavor Coconut Fruity Mushroom Floral
Imax LS means 47.48b 47.60b 58.05a 57.02a
Tend LS means 106.08a 89.87b 110.45a 108.85a
AUC LS Means 2657.61a 2057.97b 3152.93a 2890.19a
61
Figure 8. Pearson‘s principle component analysis factor loadings and factor scores from normalized time intensity data of two
compounds in model wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum intensity).
The compound listed first/capitalized was the compound panelists were told to evaluate.
COCONUT and floral
COCONUT and fruity
COCONUT and
mushroom
FLORAL and coconut
FLORAL and fruity
FLORAL and mushroom
FRUITY and coconut
FRUITY and floral
FRUITY and mushroom
MUSHROOM and
coconut
MUSHROOM and floral
MUSHROOM and fruity
Tmax
Imax
Tend
AUC
-3
-1
1
3
-5 -3 -1 1 3 5
F2 (
28.1
%)
F1 (64.7 %)
62
Table 14. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for four
compounds (floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10)
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 10152.01 1128.00 8.53 < 0.0001
Flavor 3 633.94 211.31 1.60 0.205
Replicate 1 465.61 465.61 3.52 0.068
Panelist*Flavor 27 3845.44 142.42 1.08 0.409
63
Table 15. Degrees of freedom and F values from analysis of variance (ANOVA) of maximum intensity (Imax) for four compounds
(floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10)
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 20547.11 2283.01 17.30 < 0.0001
Flavor 3 848.84 282.95 2.14 0.110
Replicate 1 409.51 409.51 3.10 0.086
Panelist*Flavor 27 5690.04 210.74 1.60 0.089
64
Table 16. Degrees of freedom and F values from analysis of variance (ANOVA) of duration (Tend) for four compounds (floral, fruity,
mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10)
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 85751.56 9527.95 11.88 < 0.0001
Flavor 3 8659.64 2886.55 3.60 0.022
Replicate 1 667.01 667.01 0.83 0.367
Panelist*Flavor 27 15645.49 579.46 0.72 0.811
65
Table 17. Pairwise comparison of duration (Tend) evaluations for four compounds in model wine samples analyzed by a trained panel
(n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Floral vs Fruity 27.90 3.12 2.68 0.017
Floral vs Mushroom 21.95 2.45 2.68 0.084
Floral vs Coconut 15.40 1.72 2.68 0.328
Coconut vs Fruity 12.50 1.40 2.68 0.510
Coconut vs Mushroom 6.55 0.73 2.68 0.884
Mushroom vs Fruity 5.95 0.66 2.68 0.910
Tukey's d critical value:
3.79
66
Table 18. Degrees of freedom and F values from analysis of variance (ANOVA) of area under the curve (AUC) for four compounds
(floral, fruity, mushroom and coconut) in model wine samples as analyzed by a trained panel (n=10)
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 142705105.90 15856122.88 16.55 < 0.0001
Flavor 3 9369087.93 3123029.31 3.26 0.032
Replicate 1 48782.50 48782.50 0.05 0.823
Panelist*Flavor 27 22241635.66 823764.28 0.86 0.655
67
Table 19. Pairwise comparison of area under the curve (AUC) evaluations for four compounds in model wine samples analyzed by a
trained panel (n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Floral vs Fruity 939.70 3.04 2.68 0.021
Floral vs Mushroom 537.25 1.74 2.68 0.320
Floral vs Coconut 313.78 1.01 2.68 0.742
Coconut vs Fruity 625.93 2.02 2.68 0.197
Coconut vs Mushroom 223.48 0.72 2.68 0.888
Mushroom vs Fruity 402.45 1.30 2.68 0.568
Tukey's d critical value:
3.79
68
Table 20. Mean separation of duration (Tend) and area under the curve (AUC) for four compounds in model wine samples analyzed by
the trained panel (n=10) using Tukey‘s HSD. Different letters indicate significant differences between the flavors (p≤0.05).
Flavor Floral Coconut Mushroom Fruity
Tend LS means 116.50a 101.10ab 94.55ab 88.60b
AUC LS means 3022.00a 2708.23ab 2484.75ab 2082.30b
69
Figure 9. Pearson‘s principle component analysis factor loadings and factor scores from normalized time intensity data of four
compounds in model wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum intensity).
Coconut
Floral
Fruity
Mushroom
Tmax
Imax
Tend
AUC
-1.5
0.1
-5 -3.4 -1.8 -0.2 1.4 3 4.6
F2 (
14.3
0 %
)
F1 (83.93 %)
70
Table 21. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for single
compounds (floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 7464.45 829.38 2.25 0.039
Flavor 3 807.30 269.10 0.73 0.541
Replicate 1 781.25 781.25 2.12 0.154
Panelist*Flavor 27 8262.45 306.02 0.83 0.691
71
Table 22. Degrees of freedom and F values from analysis of variance (ANOVA) of maximum intensity (Imax) for single compounds
(floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 11461.51 1273.50 18.94 < 0.0001
Flavor 3 403.64 134.55 2.00 0.130
Replicate 1 15.31 15.31 0.23 0.636
Panelist*Flavor 27 2591.24 95.97 1.43 0.152
72
Table 23. Degrees of freedom and F values from analysis of variance (ANOVA) of duration (Tend) for single compounds (floral,
fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 60661.95 6740.22 6.39 < 0.0001
Flavor 3 1994.85 664.95 0.63 0.600
Replicate 1 793.80 793.80 0.75 0.391
Panelist*Flavor 27 24088.15 892.15 0.85 0.673
73
Table 24. Degrees of freedom and F values from analysis of variance (ANOVA) of area under the curve (AUC) for single compounds
(floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 42497959.14 4721995.46 7.27 < 0.0001
Flavor 3 2093624.36 697874.79 1.07 0.371
Replicate 1 219137.11 219137.11 0.34 0.565
Panelist*Flavor 27 19027502.89 704722.33 1.09 0.401
74
Figure 10. Pearson‘s principle component analysis factor loadings and factor scores from normalized time intensity data of single
compounds in base wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum intensity).
Coconut
Floral Fruity
Mushroom
Tmax
Imax
Tend
AUC
-2.5
-1.5
-0.5
0.5
1.5
2.5
-4 -3 -2 -1 0 1 2 3 4
F2 (
33.0
4 %
)
F1 (56.11 %)
75
Table 25. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for two
compounds (all possible combinations of floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel
(n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 14297.35 1588.59 33.63 < 0.0001
Flavor 11 860.63 78.24 1.66 0.092
Replicate 1 16.02 16.02 0.34 0.561
Panelist*Flavor 99 5677.95 57.35 1.21 0.155
76
Table 26. Degrees of freedom and F values from analysis of variance (ANOVA) of maximum intensity (Imax) for two compounds (all
possible combinations of floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 76558.17 8506.46 67.14 < 0.0001
Flavor 11 5499.41 499.95 3.95 < 0.0001
Replicate 1 45.94 45.94 0.36 0.548
Panelist*Flavor 99 20363.88 205.70 1.62 0.006
77
Table 27. Degrees of freedom and F values from analysis of variance (ANOVA) of duration (Tend) for two compounds (all possible
combinations of floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 209299.68 23255.52 30.31 < 0.0001
Flavor 11 11041.98 1003.82 1.31 0.228
Replicate 1 117.60 117.60 0.15 0.696
Panelist*Flavor 99 106011.52 1070.82 1.40 0.041
78
Table 28. Degrees of freedom and F values from analysis of variance (ANOVA) of area under the curve (AUC) for two compounds
(all possible combinations of floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 268953467.42 29883718.60 40.62 < 0.0001
Flavor 11 25934898.86 2357718.08 3.21 0.001
Replicate 1 58734.46 58734.46 0.08 0.778
Panelist*Flavor 99 162128675.92 1637663.39 2.23 < 0.0001
79
Table 29. Mean separation of contrasts of the four flavors (floral, fruity, mushroom and coconut) analyzed in two compound base
wine solutions evaluated by a trained panel (n=10), p≤0.05.
Flavor Coconut Fruity Mushroom Floral
Imax LS means 46.10c 48.05bc 54.77a 53.53ab
AUC LS Means 2128.62b 2058.77b 2662.02a 2555.29ab
80
Figure 11. Pearson‘s principle component analysis factor loadings and factor scores from normalized time intensity data of two
compounds in base wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum intensity).
COCONUT and floral COCONUT and fruity
COCONUT and
mushroom
FLORAL and coconut FLORAL and fruity
FLORAL and mushroom
FRUITY and coconut
FRUITY and floral
FRUITY and mushroom
MUSHROOM and
coconut
MUSHROOM and floral
MUSHROOM and fruity Tmax
Imax
Tend AUC
-2
0
2
-6.5 -4.5 -2.5 -0.5 1.5 3.5 5.5
F2 (
18.8
0 %
)
F1 (74.71 %)
81
Table 30. Degrees of freedom and F values from analysis of variance (ANOVA) of time to maximum intensity (Tmax) for four
compounds (floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 8546.70 949.63 16.30 < 0.0001
Flavor 3 622.65 207.55 3.56 0.023
Replicate 1 115.20 115.20 1.98 0.168
Panelist*Flavor 27 2297.60 85.10 1.46 0.137
82
Table 31. Pairwise comparison of time to maximum intensity (Tmax) evaluations for four compounds in base wine samples analyzed
by a trained panel (n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Coconut vs Fruity 6.85 2.84 2.68 0.035
Coconut vs Floral 6.75 2.80 2.68 0.038
Coconut vs Mushroom 3.90 1.62 2.68 0.382
Mushroom vs Fruity 2.95 1.22 2.68 0.617
Mushroom vs Floral 2.85 1.18 2.68 0.642
Floral vs Fruity 0.10 0.04 2.68 1.000
Tukey's d critical value:
3.79
83
Table 32. Degrees of freedom and F values from analysis of variance (ANOVA) of maximum intensity (Imax) for four compounds
(floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 30118.11 3346.46 31.63 < 0.0001
Flavor 3 892.44 297.48 2.81 0.052
Replicate 1 201.61 201.61 1.91 0.175
Panelist*Flavor 27 5432.44 201.20 1.90 0.033
84
Table 33. Pairwise comparison of maximum intensity (Imax) evaluations for four compounds in base wine samples analyzed by a
trained panel (n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Floral vs Coconut 9.30 2.86 2.68 0.033
Floral vs Fruity 5.60 1.72 2.68 0.326
Floral vs Mushroom 3.95 1.21 2.68 0.622
Mushroom vs Coconut 5.35 1.65 2.68 0.366
Mushroom vs Fruity 1.65 0.51 2.68 0.957
Fruity vs Coconut 3.70 1.14 2.68 0.669
Tukey's d critical value:
3.79
85
Table 34. Degrees of freedom and F values from analysis of variance (ANOVA) of duration (Tend) for four compounds (floral, fruity,
mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 107742.76 11971.42 12.77 < 0.0001
Flavor 3 3483.14 1161.05 1.24 0.309
Replicate 1 525.31 525.31 0.56 0.459
Panelist*Flavor 27 18594.49 688.69 0.74 0.798
86
Table 35. Degrees of freedom and F values from analysis of variance (ANOVA) of area under the curve (AUC) for four compounds
(floral, fruity, mushroom and coconut) in base wine samples as analyzed by a trained panel (n=10).
Source DF Sum of squares Mean squares F Pr > F
Panelist 9 102600318.84 11400035.43 18.74 < 0.0001
Flavor 3 8258197.41 2752732.47 4.52 0.008
Replicate 1 23375.70 23375.70 0.04 0.846
Panelist*Flavor 27 26959861.87 998513.40 1.64 0.077
87
Table 36. Pairwise comparison of area under the curve (AUC) evaluations for four compounds in base wine samples analyzed by a
trained panel (n=10) using Tukey‘s HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
Floral vs Coconut 907.25 3.68 2.68 0.004
Floral vs Fruity 471.60 1.91 2.68 0.240
Floral vs Mushroom 420.58 1.71 2.68 0.335
Mushroom vs Coconut 486.68 1.97 2.68 0.216
Mushroom vs Fruity 51.03 0.21 2.68 0.997
Fruity vs Coconut 435.65 1.77 2.68 0.305
Tukey's d critical value:
3.79
88
Table 37. Mean separation of time to maximum intensity (Tmax), maximum intensity (Imax), and area under the curve (AUC) for four
compounds in base wine samples analyzed by the trained panel (n=10) using Tukey‘s HSD. Different letters indicate significant
differences between the flavors (p≤0.05).
Flavor Floral Coconut Mushroom Fruity
Tmax LS means 14.15b 20.90a 17.00ab 14.05b
Imax LS means 51.35a 42.05b 47.40ab 45.75ab
AUC LS means 2587.05a 1679.80b 2166.48ab 2115.45ab
89
Figure 12. Pearson‘s principle component analysis factor loadings and factor scores from normalized time intensity data of four
compounds in base wine evaluation by a trained panel (n=10). The plot illustrates the PC space for each compound and four time-
intensity parameters, Imax (maximum intensity), AUC (area under the curve), Tend (duration) and Tmax (time to maximum intensity).
Coconut Floral
Fruity
Mushroom Tmax
Imax Tend
AUC
-2
0
2
-5 -3 -1 1 3 5
F2 (
18.0
3 %
)
F1 (77.43 %)
90
Table 38. Degrees of freedom and F values from analysis of variance (ANOVA) table of time to maximum intensity (Tmax) for three
commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10), p≤0.05
Source DF Sum of squares Mean squares F Pr > F
Panelist 8 2428.67 303.58 22.21 < 0.0001
Wine 2 46.33 23.17 1.70 0.203
Replicate 1 48.17 48.17 3.52 0.072
Panelist*Wine 16 166.33 10.40 0.76 0.712
91
Table 39. Degrees of freedom and F values from analysis of variance (ANOVA) table of maximum intensity (Imax) for three
commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10), p≤0.05
Source DF Sum of squares Mean squares F Pr > F
Panelist 8 16322.67 2040.33 18.48 < 0.0001
Wine 2 427.00 213.50 1.93 0.165
Replicate 1 60.17 60.17 0.55 0.467
Panelist*Wine 16 1342.67 83.92 0.76 0.712
92
Table 40. Degrees of freedom and F values from analysis of variance (ANOVA) table of duration (Tend) for three commercially
produced Washington State Chardonnay samples analyzed by a trained panel (n=10), p≤0.05
Source DF Sum of squares Mean squares F Pr > F
Panelist 8 75210.48 9401.31 14.36 < 0.0001
Wine 2 3466.82 1733.41 2.65 0.090
Replicate 1 937.50 937.50 1.43 0.242
Panelist*Wine 16 5096.52 318.53 0.49 0.932
93
Table 41. Degrees of freedom and F values from analysis of variance (ANOVA) table of area under the curve (AUC) for three
commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10), p≤0.05
Source DF Sum of squares Mean squares F Pr > F
Panelist 8 110842634.58 13855329.32 15.47 < 0.0001
Wine 2 1317948.11 658974.06 0.74 0.489
Replicate 1 3110400.00 3110400.00 3.47 0.074
Panelist*Wine 16 4114535.89 257158.49 0.29 0.994
94
Table 42. Degrees of freedom and F values from analysis of variance (ANOVA) of consumer panel (n=60) evaluation of Washington
State Chardonnay finish with timed finish perception, p≤0.05.
Source DF Sum of squares Mean squares F Pr > F
Panelist 59 120104.06 2035.66 6.96 < 0.0001
Wine 2 1788.81 894.41 3.06 0.051
95
Table 43. Pairwise comparison of finish time of Washington State Chardonnays analyzed by a consumer panel (n=60) using Tukey‘s
HSD, p≤0.05.
Contrast Difference
Standardized
difference Critical value Pr > Diff
High oak vs unoaked Chardonnay 7.20 2.31 2.37 0.059
High oak vs medium 1.18 0.38 2.37 0.924
Medium oak vs unoaked
Chardonnay 6.02 1.93 2.37 0.136
Tukey's d critical value:
3.36
96
Table 44. Mean separation of finish time (seconds) of Washington State Chardonnays analyzed by a consumer panel (n=60) using
Tukey‘s HSD. Different letters represent a significant difference at *(p<0.1), ** (p<0.05) or *** (p<0.001)
Wine High Oak Chardonnay Medium Oak Unoaked Chardonnay
LS means 53.0a* 51.8ab 45.8b*
97
Table 45. Degrees of freedom and F values from analysis of variance (ANOVA) of consumer panel (n=60) evaluation of Washington
State Chardonnay finish with a 7-point hedonic scale, p≤0.05
Source DF Sum of squares Mean squares F Pr > F
Panelist 59 225.53 3.82 2.31 < 0.0001
Wine 2 9.64 4.82 2.91 0.059
98
Table 46. Mean separation of 7-point hedonic scale rating of Washington State Chardonnays analyzed by a consumer panel (n=60)
using Tukey‘s HSD, *(p<0.1), ** (p<0.05) or *** (p<0.001).
Wine Unoaked Chardonnay Horse Heaven Hill Reserve Chardonnay
LS means 4.8a* 4.483ab 4.2b*
99
Table 47. Coding of branched willingness to purchase responses determined by the consumers (n=60) for the evaluation of the
Washington State Chardonnay.
Code Dollar Range that the consumer would pay/bottled of a
particular wine
1 $0-$5.99
2 $6.00-$7.99
3 $8.00-$9.99
4 $10.00-$11.99
5 $12.00-$13.99
6 $14.00+
100
Table 48. Pairwise comparison of wines for branch willingness to purchase response to Washington State Chardonnays by consumer
panel (n=60). Data were analyzed using an ordered logit series as described in Table 53. (p≤0.05).
Comparison p-value
Unoaked Chardonnay vs medium oak Chardonnay 0.316
Unoaked Chardonnay vs high oak Chardonnay 0.036
Medium Oak Chardonnay vs high oak Chardonnay 0.111
101
Figure 13. Standard curve for cis-oak lactone in model wine (9% EtOH, 0.6% fructose, pH 3.0) with three replicates.
y = 1.5832x - 0.069
R² = 0.8547
0
0.5
1
1.5
2
2.5
3
0 0.2 0.4 0.6 0.8 1 1.2 1.4
lact
on
e a
rea
/in
tern
al
sta
nd
ard
are
a
mg/L cis oak lactone
102
Figure 14. Standard curve for trans-oak lactone in model wine (9% EtOH, 0.6% fructose, pH 3.0) with three replicates.
y = 1.4852x - 0.0381
R² = 0.9174
0
0.5
1
1.5
2
2.5
3
3.5
4
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
lact
on
e a
rea
/in
tern
al
sta
nd
ard
are
a
mg/L trans oak lactone
103
CHAPTER 6
CONCLUSIONS AND FUTURE RESEARCH
Single compound analysis in model wines supported the hypothesis that fruity flavor
finish earlier than coconut and mushroom flavors. Numerically, floral flavor finished earlier
than coconut and mushroom flavors, but this observation was not significant. Therefore, single
compound analysis in model wine did not support the hypothesis that floral flavor would finish
earlier than coconut and mushroom flavors.
Multiple compound analysis in model wines resulted in more variation in results. This
was potentially due to interaction between flavor compounds presented together in solution. For
two compound model wine solutions, mushroom had a suppressive effect on Imax determination.
Analysis of Tend data supported the hypothesis that fruity flavor would finish earlier than coconut
and mushroom flavors. Fruity flavor perception in the presence of floral flavor showed the
significantly earlier finish than other solutions. Contrasting the four flavors overall in two
compound model wine solutions also supported the hypothesized early finish of fruity flavor.
Principle component analysis (PCA) of normalized data further supported differences between
fruity and mushroom flavors. Four compound model wine model wine results showed that fruity
flavor had the shortest perceived finish, supporting hypothesized results for fruity flavor.
Hypothesized early finish of floral flavor was not supported by two compound or four compound
model wine data.
Analysis of base wine showed different trends than those seen in model wine analysis.
Significant results were not observed for finish length in single or multiple compound analysis.
However, base wine analysis of Imax and AUC parameters indicated that certain flavors were
influential as suppressors of intensity and perception. Two compound base wine analysis
showed that mushroom and floral flavors were perceived as significantly more intense than fruity
104
and coconut flavors. Mushroom had significantly greater AUC than other flavors. Four
compound base wine analysis supported this observation, as floral flavor was perceived to be
significantly more intense than coconut flavor and have significantly greater AUC.
Consumer panel analysis of commercially available Washington State Chardonnays
indicated that panelists were able to determine finish differences between unoaked and high oak
Chardonnay finish. High oak Chardonnay was perceived to have significantly longer finish than
unoaked Chardonnay. Additionally, consumers expressed significant differences in acceptance
for finish. Consumers preferred the finish of unoaked wine over high oak wine. This acceptance
was reflected in willingness to purchase analysis of the Chardonnay samples. Consumers were
willing to pay significantly larger dollar values for unoaked Chardonnay than high oak
Chardonnay. This indicated that barrel aging, although associated with quality, does not dictate
consumer preference or willingness to purchase.
Chemical analysis with SPME-GC-MS quantified the differences in cis- and trans- oak
lactone among unoaked, medium, and high oak Chardonnay samples. Quantification of oak
lactone showed that unoaked wine did not contain cis- or trans- oak lactone. Concentrations of
cis- and trans- oak lactone were highest in high oak Chardonnay samples.
Future research on wine finish could include a number of different types of time intensity
studies. Cataloging of additional flavor compound in white model wine could be performed,
creating a profile of flavor finish for white wine. Similar work could be performed in red model
wine systems. This work could then be expanded to catalog flavor finish utilizing different
varieties of white and red base wines.
Evaluation of finish of barrel aged compounds could also be performed. Consumer
panelists would be presented model solutions containing individual compounds associated with
105
barrel aging and rate the flavor compound finish on a hedonic scale. This would indicate which
compounds resulting from barrel aging contribute to a favorable finish. These results could be
paired with analytical analysis of different oak/barrel sources. Winemakers could then determine
what barrels will yield the most favorable compounds and therefore improve the quality of wine
finish.
106
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