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EFFECTS OF ETHANOL, TANNIN AND FRUCTOSE ON THE SENSORY AND
CHEMICAL PROPERTIES OF WASHINGTON STATE MERLOT
By
ANNE CAROLYN SECOR
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE IN FOOD SCIENCE
WASHINGTON STATE UNIVERSITY School of Food Science
MAY 2012
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To the Faculty of Washington State University:
The members of the Committee appointed to examine the thesis of
ANNE CAROLYN SECOR find it satisfactory and recommend that it be accepted.
__________________________________________
Carolyn F. Ross, Ph.D., Chair
__________________________________________
Charles G. Edwards, Ph.D.
__________________________________________
Jeffri C. Bohlscheid, Ph.D.
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ACKNOWLEDGMENTS
I would like to thank Dr. Carolyn Ross for her support throughout this project. Her advice
and encouragement have been invaluable throughout this process. The other members of my
graduate committee, Dr. Charles Edwards and Dr. Jeff Bohlscheid, have also been immensely
helpful.
Thank you to Snoqualmie Winery, for their collaboration in dealcoholizing the wine for
this project.
Thank you to Karen Weller, Scott Mattinson, and Jodi Anderson, for keeping me on track
in all aspects of graduate life.
To Medy Villamor, I am grateful for the support, the collaboration, and the endless
positivity. Also, thank you to the rest of the Ross lab, for your help with sensory panels and
flexibility in the lab, and to the Edwards lab, for the honorary work-space and support.
Finally, this project would not have been possible without the support of my parents,
William and Tammi Secor; my fiancé, Brandon Zwink; and my dearest friends. Thank you.
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EFFECTS OF ETHANOL, TANNIN AND FRUCTOSE ON THE SENSORY AND
CHEMICAL PROPERTIES OF WASHINGTON STATE MERLOT
ABSTRACT
By Anne Carolyn Secor, M.S. Washington State University
May 2012
Chair: Carolyn F. Ross
The relationship between matrix components and sensory properties of red wine was
examined. A Washington State Merlot was dealcoholized to 3.2% and alcohol was added back to
four ethanol levels: 3.2%, 8%, 12% and 16% ethanol (v/v). Within each treatment, wines were
maintained at the original tannin (211 mg/L CE tannin) and fructose (120 mg/L fructose), or
brought to 1500 mg/L CE tannin and/or 2000 mg/L fructose (n=16 solutions). The wines were
spiked with the same concentrations of three aroma compounds: 3-methyl-1-butanol, 2-
phenylethanol, and eugenol. These wines were then evaluated by a trained panel (n=10) for the
intensity of aromas and flavors (‘caramel’, ‘rose’ and ‘clove’), tastes (‘bitterness’ and
‘sourness’), and mouthfeel (‘astringency’ and ‘heat’). Gas chromatography/mass spectrometry
was used to quantify aroma compounds. PCA was used for correlation between sensory and
analytical results. All data were analyzed using analysis of variance (p<0.05) and Fisher’s Least
Significant Difference. Analytical results showed that ethanol significantly reduced the relative
headspace recovery of all three compounds. The interaction effects between ethanol, tannin and
fructose varied based upon the aroma compound and the ethanol content. In standard red wine
ethanol concentrations (12 to 16%), volatile recovery was not influenced by tannin or fructose.
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However, in low ethanol wines, high tannin concentration negatively impacted the relative
recovery of 3-methyl-1-butanol, 2-phenylethanol, and eugenol. An increase in fructose
concentration when ethanol and tannin concentrations were low reduced the recovery of 3-
methyl-1-butanol, but increased the recovery of 2-phenylethanol. The trained panel sensory
evaluation results showed that increasing ethanol concentrations increased ‘clove’ flavor, and
‘heat’, and decreased ‘sourness’ intensity. High fructose concentration increased ‘rose’ aroma
and flavor scores, and decreased ‘clove’ aroma scores. Tannin concentration positively affected
‘clove’ flavor while perceived ‘drying’ and ‘bitterness’ were impacted by ethanol*tannin. PCA
separated treatments based on ethanol, tannin, and fructose concentrations, and chemical
analyses of aroma compounds were not correlated with perceived aromas or flavors. This study
demonstrated the complexity of relationships within the wine matrix, indicating chemical and
sensory effects that winemaking techniques such as saigneé, the addition of water, and
dealcoholization may have on wine quality.
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TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ............................................................................................................. iii
ABSTRACT ................................................................................................................................... iv
LIST OF TABLES ....................................................................................................................... viii
LIST OF FIGURES ........................................................................................................................ x
CHAPTER I: INTRODUCTION .................................................................................................... 1
CHAPTER II: LITERATURE REVIEW ....................................................................................... 4
Importance of Wine to Washington State ................................................................................. 4
Current Trends in Increasing Alcohol Content in Wines .......................................................... 4
Methods of Alcohol Reduction ................................................................................................. 6
Saigneé and water addition ................................................................................................. 6
Dealcoholization by reverse osmosis .................................................................................. 7
Wine Sensory Attributes ........................................................................................................... 8
Alcohol burn ....................................................................................................................... 8
Astringency ......................................................................................................................... 9
Sourness ............................................................................................................................ 10
Bitterness ........................................................................................................................... 11
Aromas .............................................................................................................................. 13
Flavors............................................................................................................................... 16
Physiological factors ......................................................................................................... 17
Wine Matrix: Volatile and Non-Volatile Components ........................................................... 18
Tannin ............................................................................................................................... 19
Fructose ............................................................................................................................. 21
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Ethanol .............................................................................................................................. 21
Aromatic volatile compounds ........................................................................................... 22
Interactions between pairs of components ........................................................................ 25
Interactions among three or more components ................................................................. 27
CHAPTER III: MATERIALS AND METHODS ........................................................................ 31
Materials ................................................................................................................................. 31
Base Wine ............................................................................................................................... 31
Volatile Compound Profiling .................................................................................................. 33
Calibration Curves .................................................................................................................. 34
Wine Treatments ..................................................................................................................... 35
Chemical and Volatile Analysis .............................................................................................. 37
Sensory Analysis ..................................................................................................................... 37
Data Analysis .......................................................................................................................... 41
CHAPTER IV: RESULTS AND DISCUSSION ......................................................................... 42
Chemical Analysis .................................................................................................................. 42
Volatile Compound Analysis .................................................................................................. 45
Sensory Evaluation ................................................................................................................. 57
Principal Component Analysis and Pearson Correlation ........................................................ 63
CHAPTER V: CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH ............. 71
LITERATURE CITED ................................................................................................................. 74
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LIST OF TABLES
Page
Table 1. Treatment number and associated ethanol, tannin, and fructose concentration. A total of 16 treatments were evaluated by both GC/MS and sensory methods. Volatile compound concentrations remained constant for each treatment: 93.8 mg/L 3-methyl-1-butanol, 78.4 mg/L 2-phenylethanol, and 0.5 mg/L eugenol. ...................................................................................... 36
Table 2. Taste and aroma standards used in training session. Base wine was Livingston Red Rosé (Modesto, CA). .................................................................................................................... 39
Table 3. Analytical results of Merlot wine, after dealcoholization and prior to treatment modifications, including pH, titratable acidity (g/100mL), ethanol (%), tannin (mg/L CE), residual sugar (%), fructose (mg/L), free SO2 (mg/L), and total SO2 (mg/L). Results presented are the mean of triplicate measurements, followed by the standard deviation. .................................. 43
Table 4. Analytical results of Merlot wines used for sensory evaluation, including ethanol (%), tannin (mg/L CE), fructose (mg/L), pH, and titratable acidity (g/L). Treatment numbers refer to treatments described in Table 1. Values represent a mean of triplicate measurement, followed by the associated standard deviation. Means with different letters within columns differ at p < 0.05 using Tukey’s HSD. ...................................................................................................................... 44
Table 5. Standard curves created for quantification of 3-methyl-1-butanol, 2-phenylethanol, and eugenol in 3.2% ethanol. Measurements were taken as a mean of three measurements, with six points per standard curve for 3-methyl-1-butanol and 2-phenylethanol, and five points in the eugenol standard curve. ................................................................................................................ 46
Table 6. Calculated F-values and significant interactions of gas-chromatography/mass-spectrometry volatile recovery in Merlot wines varying in concentration of ethanol (3.2%, 8%, 12%, and 16%), tannin (211 and 1500 mg/L CE) and fructose (120 and 2000 mg/L). Significance is denoted as * (p<0.1), ** (p<0.05), *** (p<0.01). ..................................................................... 47
Table 7. Mean concentrations (mg/L) of volatile compounds in Merlot treatments as analyzed by GC-MS. Each treatment refers to treatments listed in Table 1. Means with different letters within columns differ using Fisher’s LSD (p<0.05). ............................................................................... 49
Table 8. Comparison of absolute recovery of 3-methyl-1-butanol, 2-phenylethanol, and eugenol based on peak area from GC-MS HS-SPME in 16 treated wines. All wines were compared to initial, untreated, spiked wine (Treatment 1), which was established as 1.00. Treatment numbers refer to treatments as described in Table 1. .................................................................................. 51
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Table 9. Calculated F-values and significant interactions of the trained panel for Merlot wines. Rep: Replicate; Pan: Panelist; EtOH: Ethanol; Tan: Tannin; Fruc: Fructose. Significance is denoted as * (p<0.1), ** (p<0.05), *** (p<0.01). ......................................................................... 58
Table 10. Mean intensity ratings for Merlot treatments as determined by a trained panel (n=9) using a 15 cm anchored line scale. Replicate evaluations were made over 7 days. Means with different letters within columns are significantly different (p<0.05) using Fisher’s LSD. Treatment numbers refer to treatments described in Table 1. ...................................................... 61
Table 11. Pearson Correlation: correlations between chemical components and sensory attributes of aromas and flavors. Bold text indicates significance (p<0.05). 3-M-1-B: 3-methyl-1-butanol; 2-PE: 2-phenylethanol; EuOL: eugenol. .................................................................................. 66-67
x
LIST OF FIGURES
Page
Figure 1. Chemical structure of a) 3-methyl-1-butanol, b) 2-phenylethanol, and c) eugenol, adapted from www.sigmaaldrich.com. ......................................................................................... 23
Figure 2. Interaction of ethanol and tannin on headspace concentrations of 3-methyl-1-butanol in (a) 211 mg/L CE tannin; (b) 1500 mg/L CE tannin. Letters that are different in both (a) and (b) indicate significantly different means (p<0.05). ........................................................................... 52
Figure 3. Interaction of ethanol and tannin on headspace concentrations of 2-phenylethanol in (a) 120 mg/L fructose; (b) 2000 mg/L fructose. Letters that are different in both (a) and (b) indicate significantly different means (p<0.05). ........................................................................... 55
Figure 4. Interaction of (a) ethanol and tannin and (b) ethanol and fructose on headspace concentrations of eugenol in Merlot wine. Different letters within each figure signify significantly different means (p<0.05). ......................................................................................... 56
Figure 5. Principal Component Analysis of sensory and chemical attributes in Merlot. Blue points indicate treatment and its placement. Red points indicate sensory attributes (UPPERCASE) and chemical attributes (lowercase). .................................................................. 64
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CHAPTER I
INTRODUCTION
The wine industry in Washington State generates over $3 billion of revenue and brings
in over 2 million visitors annually (www.washingtonwine.org). Recently, global warming,
among other causes, has led to an increase in ethanol content of many wines in the state
(Jones 2007). This is undesirable for many winemakers as an extra tax is imposed on wines
containing greater than 14% ethanol. Ethanol can be decreased during winemaking using
various techniques, including water addition prior to fermentation or dealcoholization of the
wine after fermentation. Both techniques can drastically impact the macro-component
concentrations of the wine, including ethanol, polyphenols, proteins, and polysaccharides.
However, concentrations of macro-components also significantly influence the sensory profile
of wines.
The sensory profile of a wine is critical for consumer acceptance. The sensory profile
is impacted by many attributes, including aroma, flavor, taste, and mouthfeel. All of these
attributes are affected not only by the concentration of each volatile and non-volatile
compound in the wine, but also by the chemical interactions among these compounds. While
lower-level interactions have been studied between pairs of components (Conner et al. 1994,
Dufour and Bayonove 1999a, Dufour and Bayonove 1999b, Fischer and Noble 1994, Gawel
et al. 2007, Martin and Pangborn 1970, Nahon et al. 1998, Scinska et al. 2000, Singleton et al.
1975), more complex interactions have not received much focus. For example, it is known
that ethanol reduces the volatility of aroma compounds because it increases their solubility in
the liquid portion of the matrix, which reduces their concentration in the headspace (Hartman
et al. 2002). It is also known that tannins can bind aromatic volatile compounds, reducing
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their concentration in the headspace (Pozo-Bayon and Reineccius 2009), and increases in
monosaccharide concentrations can reduce the solubility of some aromatic volatiles, which
can lead to higher concentrations of the volatile compound in the headspace (Godshall 1997).
However, it is not known how the interactions among different concentrations of ethanol,
monosaccharides, and tannin affect aroma compound volatility.
While some previous studies have examined higher-level interactions between wine
matrix components (Jones et al. 2008, Robinson et al. 2009, Villamor 2012), they used model
wine systems. Although model systems indicate potential interactions between specific
components, a real wine has many other complexities not included in a model system that
may enhance or interfere with the interaction effects of these specific components.
Thus, the present study evaluated these interactions using a dealcoholized wine matrix,
selected so as to better reflect the true nature of the wine. The dealcoholized wine served as a
“base” or “control” wine, to which ethanol, tannin, and fructose concentrations were varied.
Three distinct aroma compounds commonly found in Merlot were kept constant in
concentration. Therefore, this study defined the interactions among ethanol, tannin, and
fructose concentration on sensory and chemical attributes, including aromas, flavors, tastes,
and mouthfeel of Washington State Merlot in a dealcoholized wine system. The main sub-
objectives were as follows:
(1) To investigate the influence of ethanol concentration on perceived astringency,
sourness, bitterness, and intensity of specific aroma compounds. It was hypothesized
that an increase in ethanol would result in an increase in perceived bitterness, and a
decrease in sourness and aroma intensities.
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(2) To investigate the interaction among ethanol, tannin, and fructose on perceived
astringency, sourness, bitterness, alcohol burn, and intensity of specific aroma
compounds. It was hypothesized that perceived intensity of the aromas under study
would decrease with increasing ethanol, but the extent of the decrease at each ethanol
concentration would be dependent upon fructose and tannin concentrations. Although
main effects of ethanol, tannin, and fructose would affect sourness, bitterness, alcohol
burn, and astringency, it was expected that both physiological and cognitive
interactions may affect each taste and mouthfeel.
(3) To investigate the interaction among ethanol, tannin, and fructose on headspace
concentrations of three volatile compounds in wine. It was hypothesized that
headspace recoveries would decrease with increasing ethanol concentration, but the
extent of the decrease would be affected by different concentrations of fructose and
tannin. Specifically, tannin would further reduce recovery, and fructose would
increase the recovery, although neither would dominate over the impact of ethanol
concentration.
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CHAPTER II
LITERATURE REVIEW
Importance of Wine to Washington State
The wine industry in Washington began with the first wine grapes planted in 1825.
The state saw significant growth in the industry until Prohibition in 1920. After the act was
repealed, wineries again became a growing industry, as 42 wineries were in business in the
state by 1938. It wasn’t until the 1960s that commercial-scale production began, with the
advent of predecessors to wineries of today such as Columbia Winery and Chateau Ste.
Michelle. In the 1970’s, the industry was again rapidly expanding, as it still is today.
Currently, a new winery opens in Washington State every 15 days
(www.washingtonwine.org). The state has at least 740 wineries and sells wines of more than
30 varietals. In 2010, 160,000 tons of grapes were harvested, and 12 million cases of wine
were produced (www.washingtonwine.org).
Washington has twelve American Viticultural Areas as defined by the Alcohol and
Tobacco Tax and Trade Bureau, and the number is expected to grow in the near future. With
over 40,000 acres of wine grapes, Washington is the USA’s second largest wine producer.
The most notable varieties include Riesling, Chardonnay, Cabernet Sauvignon, Merlot, and
Syrah. The industry has created $3 billion of revenue, and employs more than 14,000 people
in the state. Wine has become one of the highest tax generators, and tourism has led to an
influx of 2 million visitors annually (www.washingtonwine.org).
Current Trends in Increasing Alcohol Content in Wines
The earth has recently seen climate changes impacting growth of grapevines and their
fruit. For instance, France has experienced a temperature increase ranging from 0.7 to 1.8°C
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between 1950 and 1999. The greatest warming trend (greater than 2.5°C from 1950 to1999),
however, was seen in the Iberian Peninsula, Southern France, and sections of Washington and
California (Jones et al. 2005). Washington’s Columbia Valley observed an increase in 149
growing degree-days between 1948 and 2002, which is similar to the average increase of 171
growing degree-days across the western growing areas of California, Oregon, and Washington
(Jones and Goodrich 2008). The largest impact of this climatic change related to wine quality
is observed as more rapid plant growth and unbalanced ripening (Jones 2007). Both of these
phenomena result in higher concentrations of sugars in the ripe grapes available to be
converted to alcohol by yeast. The problem is not solvable by simply harvesting the grapes
earlier, as the flavor compounds inherently found in grape varieties are still largely
undeveloped until finished ripening (Jones 2007). Thus, viticulturists and winemakers harvest
grapes high in sugar and low in acid in order to harvest flavorful grapes.
The rise in sugar in the grapes increases the final alcohol content in wine, and higher
alcohol content wines have been trending in recent years. For example, Riesling in Alsace has
increased in convertible sugars enough to produce a potential alcohol increase of 2.5% v/v in
the past 30 years (Duchene and Schneider 2005). Godden and Gishen (2005) found an
increase from 12.3% to 13.9% v/v alcohol in Australian red wines and from 12.2% to 13.2%
v/v alcohol in white wines between 1984 and 2004. The increase in alcohol in red wines from
Napa was from 12.5% to 14.8% between 1978 and 2001 (Vierra 2004).
While this increase in alcohol is attributed largely to climatic warming, others have
speculated other causes. For instance, Vierra (2004) cited stylistic changes resulting from
higher consumer demand for bigger, bolder wines. Others cite viticultural practices and
decisions. These include the increased planting of grape varieties that produce more sugar
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(Gambuti et al. 2011), and harvest time (Jones 2007). Some viticulturists intentionally leave
grapes on the vines for an extended “hang-time” to produce higher sugar content and more
intense flavor development. Others do not have this intention, but see the same result, as
grapes harvested earlier in the season, and therefore in the warmer parts of summer, may
experience water loss due to the high temperatures. This desiccation results in higher
concentrations of sugar (Jones 2007). These studies all indicate the challenges associated with
increased sugar content per volume of must, and, therefore, a finished wine higher in alcohol.
Although some consumers enjoy wines with higher ethanol content, the increased
percentage is less cost-effective for the winemaker. The Alcohol and Tobacco Tax and Trade
Bureau (TTB) charges a tax of $1.07 per gallon of wine with less than 14% alcohol. However,
the tax increases to $1.57 per gallon for wines over 14% alcohol. Therefore, winemakers have
an incentive to sell wines with less than 14% alcohol.
Multiple winery processing methods are available to the winemaker to reduce the
alcohol content in a final wine. These include a combination of saigneé and water addition,
and dealcoholization by reverse osmosis, vacuum membrane distillation, pervaporation, and
spinning cone column distillation, among other methods. All result in alterations of macro-
components in the wine matrix.
Methods of Alcohol Reduction
Saigneé and water addition
Saigneé, which means “to bleed” in French, is a term used for the removal of a portion
of unfermented juice out of the must (Wagner 1976). This increases the ratio of grape skins to
juice in the must. Theoretically, the resulting juice may have a higher solids content, which, in
turn, increases tannin and fermentable sugars, among other constituents.
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Saigneé may be accompanied by a second step: the addition of water back into the
must. The volume of juice removed from the must may be replaced with water, thus
decreasing the solids content and the ratio of grape skins to juice. This step reduces ethanol in
the final wine.
The combination of saigneé and water addition has been shown to reduce ethanol
while not significantly changing aromas or flavors. Baiano et al. (2009) found that saigneé
significantly increased total phenolic concentration in musts and fermented wine compared to
traditional, delestage, delayed punching-down, heating of must, cryo-maceration, and
prolonged maceration techniques. Harbertson et al. (2009) observed a decrease in ethanol and
an increase in tannin and perceived sourness as the saigneé run-off and water addition volume
increased, although no other differences in sensory perception were observed. Other studies
involving the effects of saigneé before fermentation have not been found.
Dealcoholization by reverse osmosis
Dealcoholization by reverse osmosis is a method of removing alcohol in a finished
wine. Under pressure, the wine is subjected tangentially via a flow pipe to a semipermeable
filter. The filter removes water, alcohol, and other small molecules in the wine, forming two
partitions: the permeate and the retentate. The permeate (containing water and alcohol) is
distilled to remove alcohol, and the resultant permeate, less the alcohol, is added back to the
retentate. Demineralized water can also be used to replace the permeate.
It is generally assumed that dealcoholization by reverse osmosis does not remove
volatile compounds (Catarino et al. 2006). However, Kavanagh et al. (1991) observed a high
loss of volatile compounds in beers dealcoholized by reverse osmosis from 4.80% v/v alcohol
to 2.04% and 0.96% v/v alcohol. The volatile compounds studied in that particular experiment
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included esters, alcohols, and organic acids. In contrast to compounds contributing to the
aroma, other beverage constituents are much less affected by reverse osmosis. For instance,
Gambuti et al. (2011) observed no differences in total phenolic content of four wines
(including Merlot) subjected to a decrease in 5% ethanol.
Meillon et al. (2010) found a decrease in liking of a wine dealcoholized to 7.9% v/v
alcohol versus a 13.4% v/v alcohol control, and attributed this result to decreases in
complexity and the aromatic profile. In addition to a decrease in ‘heat’, the dealcoholized
wine was perceived as more astringent than the control. When 8.44 g/L sugars from
concentrated grape juice were added to the dealcoholized wine, the ‘berry’ attribute increased
significantly.
Both methods of reducing ethanol in wine, saigneé/water addition and
dealcoholization, influence the concentrations of other macro-components in wine, including
polyphenols and simple sugars. This also influences sensory attributes of the wine, including
alcohol burn, astringency, sourness, bitterness, aroma, and flavor.
Wine Sensory Attributes
Alcohol burn
Alcohol burn may also be described as “heat” or “hotness” in wine. In wine, this is
generally caused by ethyl alcohol. This burning sensation occurs when a chemical compound
stimulates the nerve endings of the trigeminal nerve. The trigeminal nerve is composed of
many fibers that surround fungiform papillae and are distributed randomly throughout the oral
cavity (Whitehead et al. 1985). This is believed to mediate oral burn.
The intensity of the burning sensation is dependent upon the chemical concentration of
the irritant. The threshold concentration for irritation by ethanol was found to be 9% by
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Mitchell and Gregson (1968), but 14% by Diamant et al. (1963). Gawel et al. (2007) found
that the intensity of the burning sensation in wine increased as ethyl alcohol increased from
11.6% to 12.6% to 13.6% v/v. Another study (Yu and Pickering 2008) showed that the
difference threshold in Zinfandel was between 1.08 and 1.14% v/v for orthonasal perception,
or 1.31 and 1.32% v/v for retronasal perception.
Differences in perception of alcohol burn may be explained by taster status. Bartoshuk
et al. (1993) found that super-tasters have more fungiform papillae, and therefore have more
trigeminal fibers, leading to more intense oral irritation by ethanol. Duffy et al. (2004) also
found a correlation between 6-propyl-2-thiouracil (PROP) tasting status and trigeminal
irritation intensity: non-tasters experienced less burn than tasters. Prescott and Swain-
Campbell (2000) also observed a significant difference in intensities of ethanol between
PROP non-tasters and tasters. They also observed desensitization to the irritation as intensity
of alcohol burn decreased for repeated tastings over a ten-minute period. In addition to taster
status, other parameters that affect ethanol threshold include sensory panel experience,
ethnicity, and wine consumption level (Yu and Pickering 2008).
Astringency
Astringency, like alcohol burn, is a sensation caused by stimulation of the trigeminal
nerve ends. Astringency is perceived when a series of complexing reactions between a
compound and the proteins of the mouth and saliva causes the proteins to precipitate out of
solution (Noble 1994, Noble 1998). In wine, this is usually a combination of polyphenols (e.g.
tannins) and proline, an amino acid found in saliva proteins (Haslam and Lilley 1988). The
result is a drying, roughening, or even puckering mouthfeel that can increase in intensity with
higher concentrations of the astringent compound (Kallithraka et al. 1997a).
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Perception of astringency can also be influenced by other macro-components. Scinska
et al. (2000) found that astringency intensity can be masked and decreased by ethanol. The
authors attributed this to the cognitive interactions between the perception of bitter and sweet
tastes, whereby certain compounds, such as ethanol, may be perceived as either bitter or
sweet. Other authors have also found that perceived astringency decreases as ethanol
concentration increases (Fontoin et al. 2008, Gawel 1998). Serafini et al. (1997) claimed the
decreased perception of astringency was due to ethanol’s interference between the binding
reaction between salivary proteins and tannins. Vidal et al. (2004a), who also observed a
decrease in perceived astringency with an increase in ethanol, attributed to the result to the
disaggregation of tannin complexes by ethanol, resulting in smaller, less astringent molecules.
Conversely, Noble (1998) found no effect on perceived astringency by ethanol, and Meillon et
al. (2009) found that astringency decreased in wines that had been dealcoholized by reverse
osmosis. Other macro-components have the ability to affect astringency, as well. For instance,
Vidal et al. (2004a, 2004b) found polysaccharides to interfere with procyanidin aggregations,
potentially decreasing astringency.
Sourness
Sour taste, also termed ‘acidity’, is caused by hydrogen ions in foods. When a food is
ingested, the acid dissociates into a hydrogen ion and an anion. The hydrogen ion binds to the
receptor membrane via ion channels and as concentration increases, intensity increases. When
pH is exclusively considered, sourness increases with a decrease in pH (Fischer and Noble
1994). An increase in titratable acidity (TA), which equates to an increase in hydrogen ions,
also increases sourness intensity (Norris et al. 1984). However, sourness as it relates to
titratable acidity is also dependent on the type of acid, as the anion may also bind, reducing
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the net positive charge on the receptor membrane, and therefore reducing perceived acidity
(Beidler 1978). The difference threshold for titratable acidity is very low, with only 0.02 to
0.05% differences in concentration required to cause a difference in perception (Amerine et
al. 1965).
Various wine constituents affect sourness. In winemaking, the balance between
sourness and sweetness has always been a challenge. First and foremost, it is important to
harvest grapes at the optimal point for balanced sweetness and acidity. However, if a wine is
too sour, winemakers may add a sweetener to reduce the acidity. Scientifically, it has been
reported that sourness can be suppressed by sweeteners (Bonnans and Noble 1993, Zamora et
al. 2006). Ethanol may also affect sourness. Martin and Pangborn (1970) observed a decrease
in the sourness of citric acid when ethanol increased from 4% to 24%. The results of Fischer
and Noble (1994) were consistent with this: tartaric acid sourness decreased slightly with an
increase in ethanol from 8% to 11% to 14% v/v, but the effect was only significant when the
pH was 3.2. They also studied the interactions from different pH levels and found that the
effect by ethanol on sourness was most significant at pH of 3.2, while no difference was
found when the pH was 3.8. They attributed the decrease in sourness to a masking effect, and
stated that ethanol may interact with ion-channel proteins, affecting sourness (Fischer and
Noble 1994). The authors also found that sourness was not affected by catechins (100 to 1500
mg/L), nor did they find an interaction between ethanol and tannin on sourness.
Bitterness
Many compounds have been found to contribute to bitterness. These include amino
acids, peptides, sulfimides, ureas and thioureas (such as PROP and phenylthiocarbamide
[PTC]), esters and lactones, terpenoids, and phenols and polyphenols (Brieskorn 1990). Plant-
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based bitter compounds, including phenols, flavonoids, isoflavones, terpenes, and
glucosinolates, are all known to elicit bitter taste (Bravo 1998). In wine, phenolics are
responsible for bitterness and astringency (Bravo 1998, Delcour et al. 1984), two sensations
that are often confused. The distinction can be defined by the molecular weight of the
phenolics. Plant tannins are generally greater than 500 Da (Bravo 1998), and low molecular
weight compounds produce a bitter taste, while high molecular weight compounds evoke an
astringent mouthfeel (Noble 1994).
Although wine is expected to have some bitterness due to associations with ethanol
(Guinard et al. 1996, Mattes 1994), the perception of bitterness is described as unpleasant, and
can even evoke pain in some individuals. Bitterness ratings have also been correlated to
mouth roughening and drying, especially when a compound is presented at higher
concentrations (Kallithraka et al. 1997a).
Bitterness has a low detection threshold in comparison to other food constituents
(Hladik and Simmen 1996, McBurney 1978). Quinines have a threshold as low as 25 µmol/L,
while sucrose can be as much as 10,000 µmol/L, (Hladik and Simmen 1996). Additionally,
bitter taste has a longer duration than sweet, salty, or sour. For instance, the reaction time for
sucrose is 0.55 sec, 0.37 sec for sodium chloride; 0.48 sec for citric acid, and 0.80 sec for
quinine hydrochloride (McBurney 1978).
The question of bitterness stimulation is widely disputed. McBurney hypothesized that
there are 3 or more bitter receptors that can respond to different compounds: quinine, urea,
and PTC or PROP (1978). Other mechanisms have been studied as well. For instance, the G
protein-coupled receptor signaling pathway (the same mechanism for sweetness perception) is
thought to transduce certain bitter compounds (Bartoshuk et al. 1994, Schiffman et al. 1995).
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Other studies have proposed between 40 and 80 bitter taste receptors, called T2Rs, which can
be expressed in circumvallate, foliate papillae, and fungiform papillae (Adler et al. 2000,
Chandrashekar et al. 2000). In a study by Chandrashekar et al. (2000), fungiform papillae had
the largest number of taste receptors, allowing for the perception of multiple bitter tastants on
the same cell. This would account for why so many compounds can elicit the same bitter
taste.
Previous research in interactions among bitterness and macro-components of wine has
been conducted. Bitterness can be reduced by the addition of sucrose (Noble 1994, Noble
1998, von Sydow et al. 1974), and enhanced by the presence of 4 to 24% ethanol (Martin and
Pangborn 1970). Various studies have studied the interactions between multiple constituents
and the effects they have on bitterness. For instance, Fischer and Noble (1994) found that
bitterness was increased by interactions among ethanol, catechin, and a rise in pH, although
ethanol had the largest influence. They also observed that perceived bitterness was increased
by catechin, but not within the pH range of 3.2 to 3.8. They explained that the isoelectric point
of certain proteins found in saliva might be close to 3.8, creating protein ionization. This
allows the proteins to bind more frequently with catechin, preventing catechin compounds
from binding to bitter taste receptors, and therefore reducing bitterness (Fischer and Noble
1994, Hagerman and Butler 1978). Finally, Singleton et al. (1975) found that high astringency
can mask bitterness.
Aromas
Aroma is perhaps the most complex constituent of wine. It has been proposed that
there are over 800 volatiles in wine (Etievant 1991) but that less than 100 of these are odor-
active (Ferreira et al. 2000, Guth 1997a). Aroma compounds in wine are formed during and
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vary depending on grape fruit development, processing techniques in the winery, and yeast
fermentation of grape juice into wine.
Aromas are detected when a volatile compound is in the vapor phase, mixed into the
air, and passed through the nasal cavity (Clarke and Bakker 2004). Buck (2000) proposed a
new model on aroma discrimination. Approximately 1000 olfactory receptors exist within the
nasal cavity, each expressed by individual olfactory neurons. Neurons of the same receptor
connect to the same set of glomeruli (Bozza and Mombaerts 2001, Mombaerts et al. 1996).
Each receptor can accept multiple odorants, and each odorant can attach to multiple receptors
(Malnic et al. 1999). The combined effect of the activation of different receptors allows for
the brain to experience, remember, and distinguish thousands of patterns, each associated with
a specific aroma (Rubin and Katz 1999).
Theoretically, as the concentration of an odorant increases, the intensity also increases.
However, there are multiple factors that change the perception and intensity of aromas. First,
concentration itself affects perception of aromas. An odorant at low concentration might have
a completely different description from the same odorant at high concentration (Amerine and
Roessler 1975). Second, the presence of two or more odors can also alter how an aroma is
perceived. These include masking, additive effects, synergistic effects, or no effect at all
(Amerine and Roessler 1975). Masking occurs when an aroma is easily perceived when
presented individually, but less intensely when another compound is also present. Additive
effects occur when the intensities of two mixed compounds are the sum of the intensities of
each compound when presented individually. Synergistic effects occur when a compound
presented with another compound appears stronger than the theoretical intensity based on
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concentration alone. These perception effects can occur psychologically, but may also occur
due to chemical interactions in the wine.
One of the most influential ideas in the chemistry of aromatic volatility lies in a
compound’s solubility. The ratio of the concentration of a volatile in the headspace to the
concentration in the liquid is called the partition coefficient. This ratio, (and therefore the
headspace concentration) is affected mainly by solubility, boiling point, and molecular weight
of the aroma compound (Pozo-Bayon and Reineccius 2009). However, the solubility and
volatility can be influenced by many interactions with macro-components in the wine, such as
polysaccharides, proteins, and polyphenols. For instance, hydrophobic aromas interact with
hydrophobic components in the wine, such as ethanol, proteins, and even other aroma
compounds, resulting in higher odorant solubility in the liquid and a lower headspace
concentration. In a study measuring esters, aldehydes, and alcohols, the sensory threshold of
each component was reduced by interactions among the components (Conner et al. 1994).
Various studies have researched the impact of other volatile and non-volatile
constituents individually on aroma perception, and the results are conflicting. Gawel et al.
(2007) found no significant differences in aroma or flavor intensity with increasing ethanol
between 11.6 and 13.6%. Conner et al. (1994) found similar results with ethanol
concentrations up to 17%, but found decreases in activity coefficients of esters at ethanol
concentrations higher than 17%, relating inversely to acid chain length. They discussed how
at concentrations less than 17%, ethanol is mono-dispersed in water, with similar properties as
pure water, but at concentrations higher than 17%, ethanol molecules form hydrophobic
aggregations, in which odorants are more soluble (Conner et al. 1994, Escalona et al. 1999).
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Contrastingly, other studies have found differences in aroma perception with ethanol
changes. For instance, Grosch (2001) found an increase in intensity of fruity and floral aromas
when the ethanol concentration was reduced from 10% to 7%. In addition to differences due
to a reduction in the partial pressure and, therefore, an increase in the partition coefficient,
physiological reasoning may be applied. It is thought that ethanol increases the fluidity of cell
membranes, allowing for easier transport of small and charged molecules: the efficiency of
trans-membrane movement is greatly increased when the lipid bi-layer is disordered (Hunt
1985).
Flavors
Flavors emerge from a range of complex interactions between sample components,
human physiology, and psychological factors. Flavor depends not only on concentration of
volatiles, but also on interactions between volatiles, presence of non-volatile materials, and
ethanol concentration (Goldner et al. 2009). Flavor includes tastes, retronasal olfactory
perception, and trigeminal sensations. Small changes in these variables can change the flavor
of a wine dramatically.
Previous work has shown the cognitive integration that can occur when tastes and
smells are combined. One study demonstrated that when an odor compound and a taste
compound are presented together at subthreshold concentrations, the combination is still
detectable (Dalton et al. 2000). Other studies have shown that when an odor compound is
increased, the associated taste judgment increases, and vice versa (Bonnans and Noble 1993,
Murphy et al. 1977, Murphy and Cain 1980). Also, presenting two compounds in a solution
may not elicit an additive result. Instead, intensity ratings of the mixture are less than the
added intensities of each individual compound (Murphy et al. 1977, Murphy and Cain 1980).
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Another important factor is the mechanism by which odors are perceived as a flavor.
The odors involved in flavor perception are experienced via retronasal olfactory perception.
As the sample enters the oral cavity, the mouth rapidly brings the sample up to body
temperature, and volatile odorants are released from the matrix through the back of the mouth
into the nasal cavity. The environment in which these odors are experienced is unlike the
environment in which aromas are experienced in that perception is internal, not external as
with orthonasal perception. The difference in how these compounds are perceived influences
intensity ratings. Previous research has shown that retronasal odors are less identifiable than
their orthonasal counterpart, because of diffusion and subsequent absorption or adsorption of
the volatile compound into the lungs and naso-oropharyngeal surfaces (Rozin 1982).
Physiological factors
People differ in their sensitivity and therefore in their perception of all attributes
discussed previously. Perception of attributes can be influenced by physiological differences
among individuals, in addition to wine matrix component interactions described previously.
One of the important physiological differences is taster status.
The inability of some humans to taste phenylthiocarbamide (PTC) was discovered in
1931 by Fox (1931). He attributed it to genetic variances between the populations. Since then,
research has shown that differences are not dependent on genetic variation alone, but also
based on gender and race. For instance, Fernberger (1932) found that females are more acute
tasters than males, but Boyd and Boyd (1937) found that the gender effect was large in Wales
but small in Cairo. Much work has been done to describe the difference between those who
can taste PTC (tasters) and those who cannot (non-tasters). Another compound, 6-n-
propylthiouracil (PROP) was developed to replace PTC and its sulfurous odor (Fischer and
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Kaelbling 1966), and even more effects were found, including personality type, food
preferences, smoking habits (Fischer 1971), and even the identification of a subset of tasters:
supertasters (Bartoshuk et al. 1992). Supertasters are able to perceive some bitter compounds
as intensely bitter (Bartoshuk et al. 1992) and ethyl alcohol as more bitter and irritating
(Bartoshuk et al. 1993), among other differences.
The difference between nontasters, tasters, and supertasters lies in anatomically
different taste buds. Supertasters have a significantly larger amount of fungiform papillae than
tasters, who have more fungiform papillae than nontasters. In the same way, supertasters have
a much larger taste pore density than tasters and nontasters (Bartoshuk et al. 1994).
Today, a sample of PROP (0.032 M) and a sample of NaCl (0.1 M) is evaluated by
each panelist. Those who rate NaCl as much higher in intensity than PROP are considered
nontasters, those with similar ratings for both NaCl and PROP are considered tasters, and
those where PROP intensity is rated much higher than NaCl are considered supertasters
(Tepper et al. 2001). Taster status may affect perception and liking of many compounds.
PROP tasters and supertasters tend to perceive caffeine, quinine, and other bitter compounds,
as well as sweet-tasting compounds such as sucrose as more intense (Bartoshuk et al. 1994).
PROP tasters and supertasters also have higher sensitivity to oral irritation from compounds
such as capsaicin (Karrer et al. 1992) and benzyl alcohol (Prescott and Swain-Campbell
2000). All of these differences are challenges to overcome when performing sensory research,
as small differences among panelists can lead to ambiguous results.
Wine Matrix: Volatile and Non-Volatile Components
The sensory attributes previously reviewed have been studied extensively by those
interested in cognitive interactions between matrix components and human perception. An
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additional research topic, lies in the chemistry of the components. Chemical interactions
between matrix components can influence which components are actually available to be
sensed by humans, imipacting the consumer perception of the wine.
Tannin
Tannins refer to a group of compounds that elicit astringency in the mouth by
interacting with salivary proteins. Tannins in wine are made up of catechin and epicatechin as
monomers, dimers, and oligomers, and are also known as flavanols, flavan-3-ols, condensed
tannins, procyanidins, proanthocyanins, or proanthocyanidins (Cheynier et al. 2006). Grape
seed tannins are procyanidins formed of catechin, epicatechin, and epicatechin 3-gallate units.
Tannins from the skin reach approximately 30 mean degrees of polymerization (mDP)
(Souquet 1996), compared with about 10 in the proanthocyanidins from seeds (Prieur et al.
1994) and stems (Souquet et al. 2000).
Tannins are naturally found in grains (sorghum, millet, and barley), peas, carobs, dry
beans and legumes, fruit, tea, and wine (Chung et al. 1998). In wine grapes, phenolic
compounds are found in the solid parts of the grape, including skins, pulp, and seeds, and can
be extracted by maceration during winemaking (Jackson 2000). Tannins in wine are found as
flavans [catechin (Kallithraka et al. 1997a) and epicatechin (Kallithraka et al. 1997b)],
flavonols [quercitin (Trock et al. 1990)], and phenolic flavonoids (catechin mono- and
polymers).
Skins, pulp, and seeds determine the potential concentrations of tannin, but
winemaking techniques can change the final composition in wine (Katalinić 1997, Katalinić
1999). Crushing and pressing alter the phenolic composition in wines (Lamuela-Raventos and
Waterhouse 1994). However, fermentation of juice on the skins influences the phenol levels
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of the must, but it is dependent upon skin contact time. The total phenol concentration in
finished red wines is usually between 1000 and 3500 mg/L (Blanco et al. 1998, Dufour and
Bayonove 1999a, Noble 1998).
After fermentation is complete, tannins are still unstable. With aging, tannins undergo
enzymatic and chemical changes, such as polymerization and precipitation, (Cheynier et al.
2006, Noble 1998). While oxidation and aggregation with anthocyanins can occur, yielding
higher molecular weight molecules, cleavage reactions are also possible (Haslam 1980, Vidal
et al. 2002).
The interaction among tannins and other molecules is dependent upon a number of
factors. These include molecular size, flexibility, solubility of the tannin, pH, and
characteristics of the other molecule (Haslam and Lilley 1988). Solubility is highly influential
in tannin interactions. For instance, the solubility of a tannin changes based on its isoelectric
point and the pH of the wine. Solubilized (ionized) tannins are unable to bind with other
molecules, reducing the interactions between the two compounds. Solubility of tannins is also
affected by ethanol, due to hydrophobic interactions. Tannins, which are large molecules, are
largely non-polar, increasing their affinity to and interactions with other non-polar
compounds, including ethanol. In addition to ethanol, tannins also have the ability to
aggregate with themselves, forming larger complexes (Fulcrand et al. 1996). This aggregation
increases protein precipitation and interaction with other molecules. However,
polysaccharides can interfere with tannin-tannin aggregation (Riou et al. 2002), increasing
solubility of the tannin, and decreasing interaction with other wine constituents.
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Fructose
Fructose is a monosaccharide with 6 carbons. In solution, it reacts reversibly with a
hydroxyl group to form either a chain, furanose (5-carbon ring), or pyranose (6-carbon ring)
(Sanz and Martinez-Castro 2009). It is the most water-soluble of all sugars, and is also soluble
in polar solvents such as alcohol (Sanz and Martinez-Castro 2009). Fructose is the sweetest
naturally-occurring sugar, as it is about 1.65 times as sweet as glucose, and 1.14 times as
sweet as sucrose (Sanz and Martinez-Castro 2009). In red wines, fructose concentrations of
less than 1.5 g/L are considered dry and the sweetness due to these sugars is not detectable on
the palate (Jackson 2000). Sweetness can begin to be detected around 2 g/L, although most
people require 10 g/L to detect distinct sweetness (Jackson 2000). Fructose concentration in
red wines can range from not detectable to 2.5 g/L (Restani 2007).
Fructose is metabolized by yeast during fermentation as an energy source. The
byproducts of this fermentation are ethanol and carbon dioxide (Zamora 2009). After
fermentation by S. cerevisiae is complete, the unfermented glucose and fructose are termed
residual sugar (Constantini et al. 2009). Although both glucose and fructose occur in high
levels in the must and decrease during fermentation, the ratio of fructose to glucose increases
dramatically, because glucose is strongly preferred for fermentation by yeasts over fructose
(Sanz and Martinez-Castro 2009).
Ethanol
Ethanol is the main volatile compound found in alcoholic beverages. It is composed of
a polar hydroxyl group attached to a non-polar combination of a methylene and methyl group,
giving it the ability to become miscible in both water and organic compounds, and to interact
with many types of molecules, including aroma compounds. Factors affecting the volatility of
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ethanol (and other volatile compounds) include temperature, pressure, and non-covalent
bonding interactions with other compounds, volatile or non-volatile. An increase in both
temperature and pressure tends to increase volatility. Non-covalent bonding between volatiles
and non-volatiles decreases the volatility of the volatile component, while non-covalent
bonding between volatiles and other volatiles can either increase or decrease volatility,
depending on solubility of each compound.
Ethanol is produced by the transformation of reducing sugars into ethanol by S.
cerevisiae. Because the initial concentration of sugar varies between wines, the ethanol
concentration in the final wine also varies. Generally, ethanol content ranges from between
10% and 15% (Pozo-Bayon and Reineccius 2009).
Aromatic volatile compounds
According to the literature, 3-methyl-1-butanol, also known as isoamyl alcohol, is a
common aroma component in many foods, including Merlot wines (Figure 1). It has been
described as fusel (Escuadero et al. 2007), malty (Gürbüz et al. 2006), pungent (Abraham and
Berger 1994), and caramel (Villamor 2012). 3-Methyl-1-butanol has a published odor
threshold of 30 mg/L in a 10% w/w ethanol in water solution (Guth 1997b). 3-methyl-1-
butanol has a molecular weight of 88.15 g/mol, and boils at 130°C. It is miscible in ethanol
and soluble in water, up to 54 mg/mL. Buttery et al. (1988) found 3-methyl-1-butanol in
cooked rice, and determined the odor threshold to be 300 µg/L in water.
In wine, several studies have identified 3-methyl-1-butanol. Escuadero et al. (2007)
attempted to determine which odor compounds were present in the most important five wine
varieties from Spain, that is, those that contribute the most to the aromatic profiles. Among
the many compounds detected was 3-methyl-1-butanol. They described the odor as fusel, and
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Figure 1. Chemical structure of a) 3-methyl-1-butanol, b) 2-phenylethanol, and c) eugenol, adapted from www.sigmaaldrich.com.
a) b)
c)
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the range of concentration to be between 112.8 and 277.1 mg/L. Because the odor threshold
was only 30 mg/L (Guth 1997b), this particular compound was found to contribute largely to
the aroma profiles. Other wines in which 3-methyl-1-butanol has been found include wines
from Rioja (Aznar et al. 2001), and Merlot and Cabernet Sauvignon (Gürbüz et al. 2006,
Kotseridis and Baumes 2000).
Like 3-methyl-1-butanol, 2-phenylethanol is also found in wine, among other food
products (Figure 1). It is responsible for the aroma of roses (Guth 1997b). 2-phenylethanol
has a published odor threshold of 10 mg/L in a solution of 10% w/w ethanol in water (Guth
1997b). 2-phenylethanol is larger than 3-methyl-1-butanol, with a molecular weight of 122.16
g/mol. Additionally, due to the benzene ring found in all phenols, 2-phenylethanol has a
higher boiling point than 3-methyl-1-butanol, as it boils between 219 and 221°C. 2-
phenylethanol is miscible in ethanol and soluble in water up to 2 mL/100 mL.
Like 3-methyl-1-butanol, 2-phenylethanol was found in the study on aromas in cooked
rice by Buttery et al. (1988). The researchers found the odor threshold of 2-phenylethanol to
be 1100 µg/L. The researchers also compared the threshold to the actual amount found in
cooked rice (90 µg/L). The odor units, calculated by dividing the concentration present by the
threshold concentration, is only 0.09. Therefore, although 2-phenylethanol was present in
cooked rice, it was not concentrated enough to significantly contribute to the aroma. 2-
phenylethanol was also found in the study of the five Spanish wines by Escuadero et al.
(2007), but the researchers did not determine the odor active values associated with the
compound. Other wines in which 2-phenylethanol was found included wines from Rioja
(Aznar et al. 2001), and Merlot and Cabernet Sauvignon (Gürbüz et al. 2006, Kotseridis and
Baumes 2000).
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The IUPAC name of eugenol is 4-allyl-2-methoyphenol (Figure 1). Eugenol is
characterized as a spicy aroma, and is responsible for the aroma distinctive in cloves (Aznar et
al. 2001). It has also been described as pungent (Abraham and Berger 1994). Eugenol is
commonly extracted into wine during barrel aging, as it is derived from toasted oak (Diaz-
Plaza et al. 2002). Eugenol is the largest of the three compounds studied here, with a
molecular weight of 164.20 g/mol. It also has the highest boiling point at 254°C. Eugenol is
soluble in water up to approximately 1 mg/mL, and is miscible in ethanol. The odor threshold
of eugenol in 10% w/w ethanol in water is very low at 0.005 mg/L (Guth 1997b).
Like both 3-methyl-1-butanol and 2-phenylethanol, eugenol was found in the study of
five Spanish wines by Escuadero et al. (2007). For this compound, the researchers determined
their own odor threshold in a 10% ethanol in water solution containing 5 g/L of tartaric acid
with the pH adjusted to 3.2. The threshold for eugenol was found to be 0.006 mg/L. As the
range of concentration of eugenol found in the five Spanish wines was between 0.017 and
0.060 mg/L, it was determined that eugenol has a significant impact on the aroma of the
wines. Other wines in which eugenol was found include wines from Rioja (Aznar et al. 2001),
and Merlot and Cabernet Sauvignon (Kotseridis and Baumes 2000).
Interactions between pairs of components
Many studies defining the relationships between aromatic volatiles and other wine
matrix components have been completed. Beall (2010) found threshold differences for
eugenol and 1-hexanol between 0% and 8% when GC-olfactometry was employed.
Pfannkoch (2002) reported a reduced recovery (a reduction of 50 to 60%) of C4 to C10
methyl esters in 10% v/v ethanol using solid-phase microextraction (SPME). The decrease
was described as exponential (Hartman et al. 2002). Hartman also suggested a mechanism for
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decreases in volatile recovery: because of ethanol’s and aroma volatiles’ hydrophobicity, an
increase in ethanol increases solubility of aromatics in the aqueous phase. This causes the
equilibrium of the aroma volatile to shift away from the headspace. Additionally, ethanol,
which is also a volatile compound, competes for space for adsorption on the SPME fiber. All
of these mechanisms result in a lower extraction of volatiles onto a SPME fiber, and therefore,
a lower volatile recovery. Other theories have been presented as well. For instance, Godshall
(1997) suggested that mass transport governs flavor release, as opposed to phase partitioning.
It has been stated previously that tannins and polyphenols interact non-covalently with
wine volatile aroma compounds, influencing the partition coefficient (Pozo-Bayon and
Reineccius 2009). Specifically, hydrophobic interactions between aroma and phenolics
increases the solubility of aroma compounds, thereby decreasing activity coefficient of aroma
compound (King and Solms 1982). Dufour and Bayonove (1999a) observed recovery
differences with catechin concentration changes. They studied the effects of isoamyl acetate,
ethyl hexanoate, benzaldehyde, and limonene as related to catechin concentration. They
observed a decrease in isoamyl acetate, ethyl hexanoate, and benzaldehyde recovery with
increasing catechin concentration, and speculated that it may be due to hydrophobic
interactions. They explained an increase in limonene as a salting-out mechanism—as catechin
increased and was solubilized in the solvent, fewer solvent particles were available for
interaction with limonene, resulting in a higher partition coefficient. However, this effect
wasn’t observed until 5 g/L catechin.
Polysaccharides can also affect sensory quality, although the extent of influence is
dependent on the aroma compound and the polysaccharide molecule. For instance, Dufour
and Bayonove (1999b) studied the effects of red wine polysaccharides, including dextrans,
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dextrins, arabinogalactans, rhamnogalacturonans, and mannoproteins, on the volatility of four
aroma compounds. Not only did they find significant differences for each aroma compound
regarding their activity coefficients, but also they found significant differences for each
polysaccharide fraction. The acid-rich polysaccharide fractions tended to salt-out the esters,
while the protein-rich fractions retained the esters. One aroma compound, diacetyl, was
unaffected by all polysaccharide fractions. The differences cited were due to a combination of
mechanisms, including solubility disruption and polysaccharide-aroma hydrogen binding.
Nahon et al. (1998) found that an increase in sucrose increased the release of more volatile
compounds and decreased the release of less volatile compounds. The increase was cited as
reduced solubility of the compound in liquid, while the decrease was attributed to hydrogen
binding. Salting-out, or solubility disruption due to high concentrations of a solute, was found
to be especially common when mono- and disaccharides were studied (Godshall 1997).
Interactions among three or more components
In the sections preceding this one, the effects of each macro-component on each other
and on the aroma compounds has been reviewed. In this section, complex interactions
involving three or more macro-components and aroma compounds will be discussed.
Although the number of studies involving these complex interactions is few, they provide
important insight into the mechanisms involved in the present study.
One of the first studies to determine the interaction effects between proanthocyanidin,
ethanol, and polysaccharides was performed by Vidal et al. (2004a). They used a factorial
experimental design varying ethanol (11%, 13% and 15%), anthocyanins (present or absent),
procyanidins (0.25, 0.5 or 0.75 g/L), and two polysaccharide fractions (present or absent for
both). The study employed a trained panel as they sought to determine the effects of each
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macro-component on mouthfeel of the wine. The researchers found that astringency intensity
was increased due to procyanidin concentration, astringency was decreased but bitterness was
increased due to ethanol concentration, and astringency and bitterness were both reduced due
to the presence of both polysaccharides. They speculated that the polysaccharides interfere
with procyanidin aggregation, which resulted in reduced astringency. The researchers also
observed interaction effects between ethanol and procyanidin: ethanol counteracted the
enhancing effect of higher procyanidin concentration on astringency. No three-way or higher
interactions were studied.
Another major study involving interaction effects was completed by Jones et al.
(2008). The researchers used a factorial design and varied ethanol (11% and 13%), glycerol (0
and 10 g/L), polysaccharides (0 and 170 mg/L), protein (0 and 112 mg/L), and volatiles (70%
and 130% v/v volatile reconstruction mixture) in a model wine solution. Effects on sensory
attributes (six aromas, overall flavor, three tastes, and five attributes relating to mouthfeel)
were measured. The researchers found extensive interaction effects on almost all attributes,
except two aromas, sweetness, acidity, and texture. The key findings were as follows. At low
levels of volatiles, ethanol suppressed the overall aroma when glycerol was not present, but
ethanol enhanced volatile recovery when glycerol was present. Protein increased the aroma
intensity of ‘floral’ when volatiles were low. However, when the volatiles were high, protein
decreased the perceived intensity of ‘floral’. Overall, the researchers found that
polysaccharides had little effect on aroma intensity. However, some aromas were decreased
by polysaccharides when ethanol was at 13%. The researchers found no main effects on
overall aroma intensity, although ethanol was implicated as a significant effect in all
interactions. Ethanol increased hotness, bitterness, and drying. Although proteins are known
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to bind aromatic volatiles, the aroma attributes increased in intensity when protein was
present, albeit only at low levels of volatiles. Although the researchers were perplexed by
various interactions, they paved the path towards understanding of higher interaction.
Robinson et al. (2009) used a model wine solution and gas chromatography coupled
with mass spectrometry (GC-MS) with headspace SPME (HS-SPME) to determine the
effects of ethanol (14% v/v), glucose (240 g/L), glycerol (10 g/L), proline (2 g/L), and
catechin (50 mg/L) on aromatic volatiles and their partitioning in wine. They performed a
five-way interaction ANOVA on all effects. Additionally, they determined the effects of
increasing ethanol (1% increments from 10% to 18% v/v) or glucose (20 g/L increments from
160 mg/L to 320 g/L) on the volatile concentrations. The researchers found all compounds to
be significantly reduced by an increase in ethanol, but the effect of glucose depended on the
volatile compound. The researchers found a significant increase in ethyl 2-metylbutyrate,
ethyl 3-methylbutyrate, isoamyl acetate, 1-hexanol, linalool, and phenylethyl alcohol (2-
phenylethanol) as glucose increased. No other compounds were affected by glucose. As for
the interaction effects, most compounds were influenced by more than one matrix component.
All compounds were affected by an interaction between glucose and ethanol: ethanol reduced
volatile relative peak area, while glucose increased it. Ethanol had a higher-magnitude effect
on higher molecular weight compounds, but glucose’s effect was not dependent on molecular
weight. However, the concentration of glucose used in this study is much higher than what
would typically be found in wines. While this study is important for the basic effects by many
wine macro-components, it lacked interaction effects between different concentrations of
macro-components.
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Villamor’s on-going study (2012) is examining the relationships between ethanol,
tannin, and fructose content on sensory attributes of taste, mouthfeel, and aroma and flavor of
eight odorants in model wine solutions. The researcher is also examining the effects of
interactions between the three macro-components on the volatile recovery of each of the eight
odorants. Other than the study by Villamor and the three studies mentioned above, no studies
on interactions of wine macromolecules and aroma volatiles in model solutions or wine have
been performed, nor have there been any studies dealing with the overall complexity of the
wine itself.
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CHAPTER III
MATERIALS AND METHODS
Materials
1-Pentanol (CAS 71-41-0, Product Code 77597), 3-methyl-1-butanol (≥ 99.8%, CAS
123-51-3), 2-phenylethanol (≥99.0%, CAS 60-12-8), eugenol (4-allyl-2-methoxyphenol)
(CAS 97-53-0), sodium chloride, potassium hydrogentartrate, triethanolamine, sodium
dodecyl sulfate, bovine serum albumin, and (+)-catechin were obtained from Sigma-Aldrich
(St. Louis, MO). Absolute ethanol was obtained from Decon Laboratories (King of Prussia,
PA). Glacial acetic acid and hydrochloric acid (9535-03) were obtained from J.T. Baker
(Phillipsburg, NJ), and sodium hydroxide and ferric chloride were obtained from Spectrum
(Gardena, CA). Fructose was measured by spectroscopy (r-Biopharm, Darmstadt, Germany).
Chemical compounds included 3-methyl-1-butanol (≥98%), 2-phenylethanol (≥99%), eugenol
(≥98%), D-(-)-fructose (CAS 57-48-7), and 6-propyl-2-thiouracil (CAS 51-52-5), all from
Sigma-Aldrich (St. Louis, MO). Biotan was obtained from Laffort (Bordeaux, France).
Base Wine
Merlot wine (15.7% v/v ethanol, 5.7 g/L titratable acidity, pH 3.93, 0.4 g/L volatile
acidity, and 12.5 mg/L total SO2) vinified by Beall (2010) and Villamor (2012) was obtained.
The wine was unfiltered and kept in 750 mL clear glass bottles with screwcaps at 4°C until
use.
The control treatment (n = 34 bottles) from the Beall study (2010) was dealcoholized
by Snoqualmie Winery (Prosser, WA) using an Alcohol Reduction/Sweet Spot Trial Reverse
Osmosis Unit (Mavrik North America, Santa Rosa, CA). All bottles of wine were combined
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during dealcoholization for uniformity. After dealcoholization, 23 bottles were available for
use.
The dealcoholized wines were then evaluated for standard wine parameters. Wines
were analyzed in triplicate for pH using a Fisher Scientific Accumet basic AB15 Plus pH
meter and titratable acidity using a TitroLine Easy Autotitrator (Schott Instruments,
Germany). To measure titratable acidity, 5 mL of wine was placed in approximately 100mL
of MilliQ water (Millipore Corporation, Billerica, MA, USA) and brought to a boil. The
sample was then removed from the heat, covered with a watch glass, and allowed to cool to
room temperature. Subsequently, the sample was titrated by the autotitrator, and the volume
of NaOH required to reach a pH endpoint of 8.2 was recorded for calculations. Ethanol
concentration was measured using an ebulliometer (Presque Isle Wine Cellars, North East,
PA).
Tannin concentration (CE) was measured using the protein precipitation method from
Hagerman and Butler (1978) as modified by Harbertson et al. (2002). Buffer A was prepared
with 200 mM acetic acid and 170 mM NaCl in MilliQ water, and the pH was adjusted to 4.9
using 1 M NaOH. Buffer B was prepared with 5 g/L potassium bitartrate and 12% ethanol in
MilliQ water, with the pH adjusted to 3.3 with HCl. Buffer C consisted of 5% triethanolamine
(v/v) and 5% SDS (w/v) in MilliQ water, with the pH adjusted to 9.4 with HCl. A protein
solution was prepared with 1 mg/mL bovine serum albumin dissolved into Buffer A. Ferric
chloride reagent consisted of 0.01 N HCl and 10 mM FeCl3. The catechin standard was
prepared from a solution of 1 mg/mL (+)-catechin solution dissolved in 10% (v/v) ethanol.
Samples were prepared by first diluting 1:1 in Buffer B, and pipetting 500 mL of the diluted
wine sample into a microfuge tube, together with 1mL of protein solution. The solution was
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incubated for approximately 20 min, and was then centrifuged for 5 min in a microfuge
(14,000 RPM). The supernatant was decanted, and 875 µL of Buffer C were added. This
solution was incubated for 10 min, after which it was vortexed to dissolve the pellet. The
mixed solution stood for 10 min and an initial absorbance was read at 510 nm. Next, 125 µL
of ferric chloride reagent was added, mixed, and the solution was incubated for 10 min. The
absorbance was re-read (510 nm). Tannin concentration (CE) was calculated using the
difference between the two absorbances. A (+)-catechin standard curve was prepared for the
range of 50-300 mg/L for calculations.
Fructose was measured using a UV-based enzyme kit for measurement of D-glucose
and D-fructose (r-Biopharm, Germany), and a Genesys10 UV scanning spectrophotometer
(Thermo Electron Corporation, Waltham, MA).
Volatile Compound Profiling
To determine compounds already present in the wine, headspace solid phase
microextraction (HS-SPME) was coupled to gas chromatography with mass spectrometry
(GC-MS). Dealcoholized wine (n=3 bottles) were sampled using a CTC Pal Autosampler
(LEAP Technologies, Carborro, NC). Prior to analysis, the 65 µm PDMS/DVB fiber
(Supelco, Bellefonte, PA) (Bonino et al. 2003, Miller and Stuart 1999, Sanchez-Palomo et al.
2005) was pre-conditioned at 250°C for 30 min and a column blank and a fiber blank were
both performed. Sample blanks (2 replicates) consisted of 2 mL 13.0% ethanol in milliQ
water and 33% w/v NaCl in amber vials. Wine (2 mL) was placed into an amber vial, in
addition to 10 mg/L 1-pentanol as an internal standard, 33% w/v NaCl, and a magnetic stir
bar, and secured with a Teflon-coated silicon septum lid (Supelco, Bellefonte, PA). Each
sample was equilibrated for 5 min at 30°C with magnetic stirring and mechanical agitation
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(250 rpm). After this equilibration period, the fiber was exposed to the headspace for 30 min
at 30°C (Liu et al. 2005, Villamor 2012). After each sample analysis, the fiber was
conditioned for 30 min at 250°C.
GC/MS was accomplished on an Agilent 6890 gas chromatograph (Avondale, PA)
equipped with a 0.25 mm x 30 m fused silica HP-5MS column (J&W Scientific, Folsom,
CA). The injector temperature was held at 200°C and 12.42 psi, and the fiber was desorbed
using the splitless mode for 5 min. Helium was the carrier gas (flow rate = 1.6 mL/min)
(Villamor 2012). For the temperature program, the GC was held at 35°C for 3 min; increased
0.65°C/min to 42°C; increase 2.5°C/min to 60°C; increased 5°C/min to 110°C; increased
2°C/min to 125°C; increased 20°C/min to 230°C and held for 10 min. Total run time was
53.72 min. The mass spectrometer was used in constant makeup flow mode, with a
temperature of 250°C. Data were collected using the total ion concentration (TIC). The three
compounds of interest were identified using the NIST Mass Spectral Search Program (V. 2.0
d). The resulting TIC scan of the wine screening indicated the presence of 3-methyl-1-butanol
and 2-phenylethenol in the wine.
Volatile compounds selected for analysis were 3-methyl-1-butanol, 2-phenylethanol,
and eugenol. Using the wine screening described below, 3-methyl-1-butanol and 2-
phenylethanol were selected due to their high chromatographic response and their reported
presence in wine (Kotseridis and Baumes 2000). Additionally, these compounds were used in
previous wine matrix experiments conducted at WSU (Villamor 2012).
Calibration Curves
The three volatile compounds were spiked in varying concentrations into an amber
vial containing 2 mL of 3.2% (v/v) ethanol in MilliQ water and 33% (w/v) NaCl, as
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previously described. Concentrations for curve ranges were selected based on previous
research (Villamor 2012), and were targeted to encompass the range of each compound as
found in the wine profiling. The range of 3-methyl-1-butanol extended from 25 to 260 mg/L,
and contained a total of six data points. The 2-phenylethanol curve also had six data points,
ranging from 10 to 160 mg/L. The eugenol curve contained five points, ranging from 0.05 to
1.00 mg/L. Standard curves samples were prepared using the previously described GC-MS
method. The peak areas under each volatile compound were plotted versus concentration for a
standard curve. To evaluate variability among samples, the peak under 1-pentanol was
calculated.
Wine Treatments
The dealcoholized wine was divided into 16 treatments of 330 mL batches contained
in 12 oz amber glass bottles (Brewcraft, Portland, Oregon). According to Table 1, tannin was
added in the form of Biotan (26.8% catechin equivalents, CE), with catechin equivalents
determined using the protein precipitation assay (Harbertson and Adams 2002). The “low”
and “high” concentrations of tannin in the wine treatments were 211 and 1500 mg/L (CE),
respectively. The “low” and “high” concentrations of fructose were 120 and 2000 mg/L
fructose, respectively. The selection and definition of “low” and “high” concentrations of
tannin and fructose were based on previous WSU research (Landon et al. 2009, Villamor
2012). Additions of tannin and fructose were added in concentrations accounting for volume
changes due to ethanol addition for each wine treatment.
Again considering the volume changes of the original wine due to ethanol addition, 2-
phenylethanol and 3-methyl-1-butanol were spiked into each treatment bottle to achieve final
concentrations of 78.4 and 93.8 mg/L, respectively. Eugenol was also spiked into each
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Table 1. Treatment number and associated ethanol, tannin, and fructose concentration. A total of 16 treatments were evaluated by both GC/MS and sensory methods. Volatile compound concentrations remained constant for each treatment: 93.8 mg/L 3-methyl-1-butanol, 78.4 mg/L 2-phenylethanol, and 0.5 mg/L eugenol.
Treatment Number
Ethanol (%) Final [Tannin] (mg/L CE)
Final [Fructose] (mg/L)
1 3.2 211 120 2 3.2 211 2000 3 3.2 1500 120 4 3.2 1500 2000 5 8.0 211 120 6 8.0 211 2000 7 8.0 1500 120 8 8.0 1500 2000 9 12 211 120 10 12 211 2000 11 12 1500 120 12 12 1500 2000 13 16 211 120 14 16 211 2000 15 16 1500 120 16 16 1500 2000
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treatment to achieve 0.5 mg/L. Immediately after volatile compound additions, wines were
flushed with nitrogen and capped using crown caps (Brewcraft USA, Portland, OR).
Chemical and Volatile Analysis
For all chemical analyses, each treatment replicate (n=2 for each treatment) was
measured in duplicate, resulting in four observations for each treatment. Analyses included
pH, titratable acidity, ethanol concentration, tannin concentration (CE), and fructose
concentration. All of these analyses were conducted as described previously.
For volatile analyses, each treatment was measured in replicate using the GC-MS
method described above. Immediately following sensory analysis, 2 mL of wine was placed
into 10 mL amber vials containing 33% NaCl (w/v) and a magnetic stirbar. Each vial was also
spiked with 2 µL 1-pentanol from the 10,000 mg/L stock solution resulting in a final
concentration of 10 mg/L in the sample. 1-Pentanol was used to assess reliability during
analysis. The vials were capped tightly with Teflon-coated silicon septum lids (Supelco,
Bellefonte, PA), and placed randomly into the autosampler for analysis.
Sensory Analysis
Washington State University (WSU) students and staff (N=10, 4 male and 6 female)
were recruited via email, flyers, and online WSU announcements. Eight panelists were
between the age of 21 and 30, while two panelists were 51 years or older. Four panelists
consumed wine one to three times per week, five panelists consumed wine one to three times
per month, and one indicated that they only consumed wine one to three times per year. All
panelists expressed an interest to learn more about wine and become more frequent
consumers. Panelists received coupons at the end of each training session, and a wine glass at
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the end of the panel as incentives. Training and evaluation sessions took place in the Sensory
Facility of the School of Food Science (Pullman, WA).
Panelists met for a total of eight one-hour training sessions. The first session was used
for signing consent forms, collecting demographic information and taster status, and
familiarizing the panelists with tasting procedures. Taster status was determined using the 6-
propyl-2-thiouricil (PROP) test (Tepper et al. 2001) in which panelists blindly tasted a sample
of 0.32 mmol/L PROP and 0.1mol/L NaCl. Intensities of tastes perceived were recorded using
a 15 cm line scale, and compared. Super-tasters, tasters, and non-tasters were determined
according to the procedures described by Tepper et al. (2001). Taster status was used during
interpretation to help identify any outlying data, and to help explain differences in sensitivities
to specific attributes. For sample familiarization, panelists each received 20 mL of untreated,
dealcoholized wine. Panelists were instructed how to sniff and taste wine samples, to increase
reliability among panelists. Panelists individually made notes of aromas, flavors, and tastes
they perceived. These were discussed as a group at the end of the session. Finally, panelists
were introduced to the 15 cm line scale to be used for attribute intensity during the panel. The
scale included “low intensity” and “high intensity” markers at 1.5 cm and 13.5 cm,
respectively.
Throughout the next seven sessions, panelists were trained to recognize and evaluate
the taste, mouthfeel, aroma, and flavor standards. The base wine was Livingston Red Rosé
(Modesto, CA), with the standards described in Table 2. All evaluations measured intensity of
each attribute on the 15 cm line scale. Panelists discussed attributes perceived during each
session, and intensity ratings of all standards were assigned based on group agreement.
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Table 2. Taste and aroma standards used in training session. Base wine was Livingston Red Rosé (Modesto, CA).
Attribute Base Standard Low ethanol 230 mL deionized
(DI) water 20 mL absolute ethanol
High ethanol 210 mL DI water 40 mL absolute ethanol Low sour 150 mL base wine 0.07 g tartaric acid High sour 150 mL base wine 0.75 g tartaric acid Low bitter 150 mL DI water 0.004 g quinine sulfate High bitter 150 mL DI water 0.015 g quinine sulfate Low drying 500 mL base wine 0.38 g Biotan High drying 500 mL base wine 2.28 g Biotan Low caramel base wine 50 mg/L 3-methyl-1-butanol High caramel base wine 350 mg/L 3-methyl-1-butanol Low rose base wine 50 mg/L 2-phenylethanol High rose base wine 350 mg/L 2-phenylethanol Low clove base wine 0.5 mg/L eugenol High clove base wine 3 mg/L eugenol Control base wine none
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Throughout training, standards were revisited to confirm group agreement in intensity
ratings. Panelists practiced blind evaluations by tasting base wine spiked with varying
combinations and concentrations of absolute ethanol, Biotan, 3-methyl-1-butanol, 2-
phenylethanol, and eugenol. Base wines were either Livingston Red Rosé (Modesto, CA) or
Syrah (bulk, Columbia Valley). After each session, all ballots were collected and recorded.
Feedback was given to individual panelists at the beginning of each following session to
encourage agreement among panelists. Average intensity ratings collected during the fifth
session were used as a reference for the remainder of training and sample evaluations. The
final two sessions were used to introduce panelists to the evaluation booths and sensory
evaluation software (Compusense five Release 5.0, Ontario, Canada).
During training, it was decided that the concentrations of 3-methyl-1-butanol and 2-
phenylethanol in the treatment wines were not sufficient for panelists to distinguish
differences. As a result, all wines received an additional spike of both compounds, increasing
the final concentration by 140 mg/L 3-methyl-1-butanol and 96 mg/L 2-phenylethanol.
Bottles were re-flushed with nitrogen and re-capped with new crown caps.
Treated wines were held at 4°C prior to formal evaluations. After training was
complete, panelists participated in six days of evaluations (three days for each replicate) in the
individual testing booths with red lighting. A completely randomized block design was used
and each panelist was presented with each treatment in replicate. Panelists were given the
standard mean intensities reference sheet, a cuspidor, a cup of MilliQ water, and crackers to
rinse their palate between samples. Wine bottles were brought out from cold storage 24 hours
before evaluation and allowed to equilibrate to room temperature (approximately 18°C).
Samples (20 mL) were poured one hour before serving in ISO/INAO clear wine glasses and
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covered with plastic petri dishes. Samples were randomly assigned 3-digit codes and
presented in random order. Panelists evaluated the intensity of each attribute using the 15 cm
line scale presented during training.
Data Analysis
Sensory data were collected using Compusense five software, Release 5.0 (Guelph,
ON, Canada). A three-way analysis of variance(ANOVA) (including replicate, panelist,
ethanol concentration, tannin concentration, and fructose concentrations, ethanol*tannin,
tannin*fructose, fructose*ethanol, and ethanol*tannin*fructose, p<0.1) and Fisher’s Least
Significant Difference (LSD) were performed using XLSTAT (Addinsoft, Paris, France). GC-
MS data were analyzed using three-way ANOVA (including main effects and all interactions,
p<0.1) and Fisher’s LSD (XLSTAT, Addinsoft, Paris, France). For both sensory and GC-MS
data, outliers were determined using Dixon’s Q test (95%) (Dean and Dixon 1951,
Rorabacher 1991), and replaced with the mean. Principal Component Analysis (PCA) and
Pearson’s Correlation were used to correlate sensory analysis to volatile and chemical
analysis.
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CHAPTER IV
RESULTS AND DISCUSSION
Chemical Analysis
The dealcoholized wine contained 3.2% ethanol, 211 mg/L CE tannin, and 120 mg/L
fructose, respectively (Table 3). These baseline concentrations of ethanol, tannin and fructose
served as the “low” standard for each matrix component. Based on previous research
(Villamor 2012), higher concentrations (8%, 12%, and 16% ethanol, 2000 mg/L fructose, and
1500 mg/L CE tannin) were selected for treatment modification. In determining the
concentrations of ethanol, tannin and fructose to add for each modification, calculations were
made considering the baseline concentrations.
Chemical results on the treated wines are shown in Table 4. The ethanol concentration
that was measured was lower than the calculated concentration for treatment modification.
This was attributed to evaporation of ethanol because each treatment was measured after
sensory evaluation, a nitrogen flush, and storage at 4°C. Tannin levels were similar to the
expected value for the low tannin treatments, but the high tannin treatments generally had
higher tannin contents than predicted based on calculations. This was likely due to error
introduced using the protein precipitation method for tannin determination in a low tannin
wine. A study by Jensen et al. (2008) determined that a threshold tannin concentration exists
for precipitation to occur (~140 mg/L CE). If the wine has tannin levels below this threshold,
the predicted concentration may be below the actual concentration. In this study, using a
diluted sample (1:1) reduced the sample to be below threshold, and was subsequently
underestimated for both evaluations before and after addition of Biotan. An addition of 1500
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Table 3. Analytical results of Merlot wine, after dealcoholization and prior to treatment modifications, including pH, titratable acidity (g/100mL), ethanol (%), tannin (mg/L CE), residual sugar (%), fructose (mg/L), free SO2 (mg/L), and total SO2 (mg/L). Results presented are the mean of triplicate measurements, followed by the standard deviation.
Wine Parameter Mean (SD) pH 3.71 (0.01) Titratable Acidity (g/100mL) 0.55 (0.01) Ethanol, % 3.2 (0.05) Tannin (mg/L CE) 211 (22) Fructose, mg/L 120 (3) Free SO2, mg/L 0.2 (0.2) Total SO2, mg/L 0.3 (0.4)
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Table 4. Analytical results of Merlot wines used for sensory evaluation, including ethanol (%), tannin (mg/L CE), fructose (mg/L), pH, and titratable acidity (g/L). Treatment numbers refer to treatments described in Table 1. Values represent a mean of triplicate measurement, followed by the associated standard deviation. Means with different letters within columns differ at p < 0.05 using Tukey’s HSD.
Trt # Ethanol % Tannin, mg/L CE
Fructose, mg/L pH Titratable Acidity g/L
1 3.04 (0.00)a 264.4 (35)b 100.8 (6.5)b 3.65 (0.33) 5.76 (0.06)de
2 3.02 (0.03)a 208.9 (12)b 2273 (92)a 3.50 (0.05) 5.87 (0.03)cd
3 2.98 (0.03)a 1836 (260)a 73.68 (2.8)b 3.63 (0.09) 6.41 (0.13)a
4 3.00 (0.00)a 1726 (6.7)a 2873 (130)a 3.45 (0.19) 6.41 (0.10)a
5 7.52 (0.17)b 232.1 (38)b 114.1 (9.5)b 3.68 (0.01) 5.61 (0.00)def
6 7.52 (0.11)b 212.0 (14)b 2260 (500)a 3.61 (0.12) 5.50 (0.07)efg
7 7.62 (0.03)b 1618 (0.39)a 83.67 (17)b 3.64 (0.06) 6.17 (0.00)abc
8 7.64 (0.00)b 1665 (76)a 2547 (12)a 3.59 (0.03) 6.22 (0.01)ab
9 11.2 (0.23)c 205.5 (8.7)b 116.7 (2.8)b 3.53 (0.15) 5.36 (0.17)fgh
10 11.2 (0.00)c 213.0 (17)b 2443 (62)a 3.54 (0.03) 5.44 (0.01)fgh
11 11.2 (0.06)c 1636 (6.0)a 67.16 (5.2)b 3.68 (0.25) 5.90 (0.07)bcd
12 11.4 (0.17)c 1684 (60)a 2478 (250)a 3.59 (0.08) 5.26 (0.03)ghi
13 15.3 (0.28)d 208.9 (12)b 122.1 (11)b 3.57 (0.09) 4.95 (0.02)i
14 15.2 (0.00)d 217.4 (25)b 2360 (31)a 3.56 (0.08) 5.14 (0.16)hi
15 15.5 (0.03)de 1645 (26)a 104.3 (15)b 3.58 (0.04) 5.78 (0.06)de
16 15.8 (0.00)e 1623 (1.0)a 2560 (43)a 3.52 (0.03) 5.79 (0.04)de
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mg/L CE tannin increased the concentration to above the threshold range, increasing the
accuracy for the high tannin wine samples.
Fructose concentration, titratable acidity, and pH are also reported in Table 4. Fructose
levels were within the expected range. There was a wide range of variability for TA, but the
data followed a general trend of a higher titratable acidity associated with higher tannin
treatments. This was not consistent with previous work (Blanco et al. 1998), where it was
found that titratable acidity decreased with increased phenolic content. Perhaps Biotan
contains a component that contributes to titratable acidity in these treatments, as only 26.8%
of the product was tannin composed of greater than four subunits. The average pH in the
wines was 3.58, and no significant differences were noted.
Volatile Compound Analysis
Standard curve results are shown in Table 5. Utilizing these curves, initial
concentrations of 3-methyl-1-butanol and 2-phenylethanol in the dealcoholized wine were
93.8 ±3.2 mg/L, and 78.4 ±3.0 mg/L, respectively. After the additional spike of 3-methyl-1-
butanol and 2-phenylethanol following the fourth training session, each treatment contained
calculated values of 235.0 mg/L (±1.0) 3-methyl-1-butanol and 172.9 mg/L (±1.2) 2-
phenylethanol.
In Table 6, the F-values for each effect are reported. All three compounds—3-methyl-
1-butanol, 2-phenylethanol, and eugenol—were significantly affected by ethanol and tannin
(p<0.05). Only 3-methyl-1-butanol was significantly affected by fructose as a main effect
(p<0.05). Complex interactions were observed for all three compounds (Table 6). 3-Methyl-1-
butanol was significantly affected by a three-way interaction among ethanol, tannin, and
fructose concentrations (p<0.05), with the
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Table 5. Standard curves created for quantification of 3-methyl-1-butanol, 2-phenylethanol, and eugenol in 3.2% ethanol. Measurements were taken as a mean of three measurements, with six points per standard curve for 3-methyl-1-butanol and 2-phenylethanol, and five points in the eugenol standard curve.
Compound Curve equation Calibration curve range (mg/L)
R2
3-methyl-1-butanol Area = 7.229E6(mg/L) +2.139E8 25-260 0.984 2-phenylethanol Area = 1.692E7(mg/L) +8.236E7 10-160 0.993 eugenol Area = 8.000E7(mg/L) –2.372E6 0.05-1.00 0.990
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Table 6. Calculated F-values and significant interactions of gas-chromatography/mass-spectrometry volatile recovery in Merlot wines varying in concentration of ethanol (3.2%, 8%, 12%, and 16%), tannin (211 and 1500 mg/L CE) and fructose (120 and 2000 mg/L). Significance is denoted as * (p<0.1), ** (p<0.05), *** (p<0.01).
Source of Variation df 3-methyl-1-butanol 2-phenylethanol EugenolReplicate 1 9.08*** 0.013 0.601Ethanol 3 2340*** 246*** 177***Tannin 1 7.41*** 6.33** 8.96***Fructose 1 5.33** 0.000 0.000Ethanol*Tannin 3 4.54*** 2.24* 6.32***Ethanol*Fructose 3 4.64*** 1.610 2.170Tannin*Fructose 1 3.81* 0.009 0.823Ethanol*Tannin*Fructose 3 4.09** 2.49* 1.230
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majority of the variance attributed to ethanol concentration. For 2-phenylethanol, a three-way
interaction effect was also observed for ethanol*tannin*fructose (p<0.1), with the majority of
this variance again due to ethanol. Finally, eugenol was significantly affected by a two-way
interaction of ethanol*tannin (p<0.01) and ethanol*fructose (p<0.1), with much of the
variation due to ethanol. The only compound significantly affected by a replicate effect was 3-
methyl-1-butanol. This error could be because the volatility of 3-methyl-1-butanol compared
to the 2-phenylethanol and eugenol is higher, due to a lower boiling point and molecular
weight, resulting in an increased loss during preparation.
For all three volatile compounds, mean concentrations significantly decreased as
ethanol increased (Table 7). 3-Methyl-1-butanol and 2-phenylethanol were clearly
distinguished by ethanol concentration groups, while eugenol decreased significantly with
ethanol concentrations between 3.2% and 12% only. The recovery of eugenol in 16% ethanol
was not significantly different from the recovery of eugenol in 12% ethanol. Each compound
was also affected by tannin and fructose, although the effects were significant only at lower
ethanol concentrations. 3-methyl-1-butanol was decreased by tannin in 3.2% and 8% ethanol,
and was also decreased by fructose in 3.2% ethanol and low tannin. 2-Phenylethanol was
decreased by tannin in 3.2% ethanol, and was increased by fructose in 3.2% ethanol. Eugenol
was decreased by tannin in 3.2% ethanol and increased by fructose in 3.2% ethanol and low
tannin.
The decrease in aroma volatile concentration in the headspace has been reported in
many previous studies (Conner et al. 1994, Escalona et al. 1999, Goldner et al. 2009, Hartman
et al. 2002, Pfannkoch et al. 2002). The decrease in recovery of 3-methyl-1-butanol, 2-
phenylethanol, and eugenol due to an increase in ethanol can be explained by a combination
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Table 7. Mean concentrations (mg/L) of volatile compounds in Merlot treatments as analyzed by GC-MS. Each treatment refers to treatments listed in Table 1. Means with different letters within columns differ using Fisher’s LSD (p<0.05).
Volatile Compounds Treatment 3-Methyl-1-butanol 2-Phenylethanol Eugenol
1 166a 126ab 0.598b
2 150.c 138a 0.737a
3 157b 116b 0.491c
4 156b 120.b 0.494c
5 110d 78.7c 0.301d
6 111d 81.9c 0.266d
7 101e 83.4c 0.287d
8 103e 79.7c 0.236de
9 76.7f 53.2d 0.135f
10 76.4f 56.4d 0.123f
11 73.7f 56.2d 0.145ef
12 72.7f 51.3d 0.128f
13 47.5g 51.9d 0.105f
14 45.8g 32.8e 0.074f
15 49.2g 28.4e 0.076f
16 47.4g 33.4e 0.081f
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of many mechanisms. First, because these aroma compounds are non-polar, their solubility is
increased by ethanol, which is also non-polar. This decreased the partition coefficient, and
resulted in a lower compound concentration in the headspace. Second, because ethanol is very
volatile itself, it competed with the volatile aroma compounds for adsorption on the SPME
fiber. Consequently, fewer aroma particles were adsorbed onto the fiber, resulting in a lower
recovery (Hartman 2002). In order to overcome the errors associated with SPME use, a
standard curve should be prepared for each possible matrix to account for ethanol volatility.
However, if this is not possible, other methods of extraction are available, such as dynamic
headspace extraction, or stir-bar sorptive extraction.
Relative recoveries of the three aroma compounds in each treatment are found in
Table 8. Treatment 1, which contained 3.2% ethanol, low tannin, and low fructose, was
established as 1.00 and the subsequent treatments were compared to treatment 1, based on
peak areas. A significant decrease with increased ethanol concentration was observed, with
relative recoveries reaching as low as 0.29 for 3-methyl-1-butanol, 0.23 for 2-phenylethanol,
and 0.12 for eugenol. A significant loss in aroma compounds could affect consumer
perception of aroma compounds in a wine with higher ethanol concentrations. However, the
effects of tannin and fructose were dependent upon aroma compound.
For 3-methyl-1-butanol, a significant three-way interaction (p<0.01) occurred between
ethanol, tannin and fructose, and the effects are shown in Figure 2. For ethanol concentrations
between 12% and 16%, the effects of tannin and fructose were minor, as no significant
differences were shown between treatments in 12% or 16% ethanol. However, in 8% ethanol,
significant differences were observed between tannin treatments: an increase in tannin
concentration decreased the recovery of 3-methyl-1-butanol. This was likely due to tannin-
51
51
Table 8. Comparison of absolute recovery of 3-methyl-1-butanol, 2-phenylethanol, and eugenol based on peak area from GC-MS HS-SPME in 16 treated wines. All wines were compared to initial, untreated, spiked wine (Treatment 1), which was established as 1.00. Treatment numbers refer to treatments as described in Table 1.
Treatment 3-Methyl-1-butanol 2-Phenylethanol Eugenol1 1.00 1.00 1.002 0.90 1.09 1.233 0.95 0.92 0.824 0.95 0.96 0.835 0.66 0.63 0.506 0.67 0.65 0.447 0.61 0.66 0.488 0.62 0.63 0.399 0.46 0.42 0.2310 0.46 0.45 0.2111 0.45 0.45 0.2412 0.44 0.41 0.2113 0.29 0.41 0.1814 0.28 0.26 0.1215 0.30 0.23 0.1316 0.29 0.27 0.14
52
52
Figure 2. Interaction of ethanol and tannin on headspace concentrations of 3-methyl-1-butanol in (a) 211 mg/L CE tannin; (b) 1500 mg/L CE tannin. Letters that are different in both (a) and (b) indicate significantly different means (p<0.05).
53
53
volatile binding (King and Solms 1982), the effects of which were suppressed in higher
ethanol concentrations. In higher ethanol, the solubility of tannins increased, which reduced
the interactions between tannin and aromatic volatiles. As a result, tannin did not affect the
volatiles in ethanol concentrations higher than 8%.
At low ethanol concentration (3.2%), and low tannin (211 mg/L CE), an increase in
fructose decreased the recovery of 3-methyl-1-butanol (Figure 2). Such a result may be
caused by polysaccharide-volatile hydrogen binding (Dufour and Bayonove 1999b, Godshall
1997, Nahon et al. 1998). This was possible for 3-methyl-1-butanol in low tannin and ethanol
concentrations, as the effects of high tannin and ethanol dominated the monosaccharide-
volatile aroma mechanism. Alternatively, chemical interactions between fructose and ethanol
were also influential. It has previously been shown that polysaccharides can either disrupt or
increase solubility of volatile compounds. Because ethanol is also a volatile compound, it may
be impacted by polysaccharides. One study (Roberts et al. 1996) found that as sucrose or
glucose concentration increased to 60% w/v, volatility of ethanol, among other compounds,
also increased. Although the concentration of fructose in the study by Roberts et al. was high,
the mechanism they described may explain the presently observed effect: in 3.2% ethanol, as
fructose concentration was increased from 120 mg/L to 2000 mg/L, the volatility of ethanol
increased. An increase in ethanol volatility resulted in ethanol outcompeting 3-methyl-1-
butanol for adsorption onto the SPME fiber and, therefore, recovery of 3-methyl-1-butanol
decreased. However, when a higher concentration of ethanol was present, the volatility of
both ethanol and 3-methyl-1-butanol were not significantly affected by changes in fructose
concentration.
54
54
Like 3-methyl-1-butanol, 2-phenylethanol was affected by a three-way interaction
(p<0.1) between ethanol, fructose, and tannin concentrations (Figure 3). These data indicated
that at low concentrations of ethanol (3.2%) and fructose (120 mg/L), an increase in tannin
concentration decreased the recovery of 2-phenylethanol. This was likely due to tannin-
volatile binding (King and Solms 1982), which was suppressed by higher ethanol
concentrations. 2-phenylethanol was also increased by fructose at low concentrations of
ethanol (3.2%). This was attributed to the solubility disruption effect of fructose (Dufour and
Bayonove 1999b, Godshall 1997, Nahon et al. 1998). As fructose increased in the matrix, it
interacted increasingly with ethanol molecules. As a result, ethanol molecules were less
available for interaction with 2-phenylethanol. Consequently, 2-phenylethanol was less
soluble and a larger concentration of 2-phenylethanol in the headspace was observed. The
effects of both tannin and fructose on 2-phenylethanol were dominated by ethanol as it
increased to 8% and higher.
Finally, for eugenol recovery, significant two-way interactions between ethanol and
tannin (p<0.01) and ethanol and fructose (p<0.1) were observed (Figure 4). For
ethanol*tannin, no differences were observed between tannin treatments when ethanol was in
the expected wine range (between 8% and 16% ethanol). However, at low ethanol
concentration (3.2%), an increase in tannin concentration decreased the recovery of eugenol.
This was most likely due to tannin-volatile binding effects (Dufour and Bayonove 1999a,
King and Solms 1982, Pozo-Bayon and Reineccius 2009), which were eclipsed by the effects
of ethanol concentration when it was between 8% and 16%. Additionally, ethanol and tannin
may have interacted, affecting recovery of eugenol. Higher ethanol content increased the
solubility of tannins in the wine (Haslam and Lilley 1988). Solubilized tannins are less able to
55
55
Figure 3. Interaction of ethanol and tannin on headspace concentrations of 2-phenylethanol in (a) 120 mg/L fructose; (b) 2000 mg/L fructose. Letters that are different in both (a) and (b) indicate significantly different means (p<0.05).
56
56
Figure 4. Interaction of (a) ethanol and tannin and (b) ethanol and fructose on headspace concentrations of eugenol in Merlot wine. Different letters within each figure signify significantly different means (p<0.05).
57
57
bind with other molecules, including volatile aroma compounds. Therefore, an increase in
tannin when the wine has high ethanol did not decrease the volatility of eugenol. For
ethanol*fructose, a similar effect was observed, as fructose did not affect the recovery of
eugenol between 8% and 16% ethanol. However, when ethanol was extremely low (3.2%), an
increase in fructose led to higher recovery of eugenol. Much like 2-phenylethanol, this was
likely due to a salting-out effect imposed by fructose (Dufour and Bayonove 1999b, Godshall
1997, Nahon et al. 1998), where fructose interacted increasingly with ethanol molecules as
fructose increased in concentration. As a result, ethanol molecules were less available for
interaction with eugenol and its volatility and headspace concentration increased. The effect
of ethanol concentration itself appeared to decrease between 12% and 16%. This supports the
theory proposed by Hartman (2002) that the decrease in aroma volatile recoveries was
exponential with an increase in ethanol concentration.
Sensory Evaluation
Analysis of variance results generated by the trained panel are shown in Table 9. Main
effects for sensory attributes were common, while more complex effects as a result of
interaction among components were less common. Ethanol concentration significantly
affected sourness and heat perception (p<0.01), but of the aromas and flavors, only clove
flavor was affected (p<0.01). Fructose concentration significantly affected rose aroma and
flavor (p<0.05), clove aroma (p<0.05) and flavor (p<0.1), and caramel flavor (p<0.1). Tannin
concentration significantly affected clove flavor (p<0.01). For interactions, significant effects
of ethanol*tannin*fructose were observed for rose flavor (p<0.1), but fructose appeared to
have the largest effect. A combination of two-way effects (tannin*fructose and
ethanol*fructose, p<0.1) altered the perception of heat, but ethanol alone contributed the most
58
58
E x
T x
F
3
1.29
0.03
9
1.30
2.11
*
0.27
3
0.18
7
0.88
2
0.58
1
0.31
8
1.71
T x
F 1
0.03
3
0.09
3
0.41
1
0.57
4
0.40
3
0.41
6
0.75
0
0.42
3
0.26
0
3.40
*
E x
F 3
0.47
7
0.16
6
0.61
1
0.48
9
0.10
3
0.88
6
1.06
0.21
5
0.82
0
2.57
*
E x
T 3
0.80
2
0.70
9
1.49
0.53
3
1.23
0.35
8
1.89
3.29
**
7.35
***
0.68
5
Fru
ctos
e (F
) 1
4.28
**
5.73
**
0.75
2
5.19
**
3.08
*
3.12
*
0.42
0
2.17
0.10
7
0.00
0
Tann
in
(T) 1
0.42
1
1.12
0.81
3
0.28
1
7.79
***
0.38
8
0.72
1
83.4
***
598*
**
0.00
5
Eth
anol
(E
) 3
0.20
1
1.72
1.04
0.69
4
8.50
***
1.06
8.42
***
5.05
***
0.72
9
158*
**
Pan
elis
t
9
3.58
***
13.2
***
6.47
***
5.00
***
4.80
***
2.50
***
18.4
***
8.23
***
17.4
***
15.7
***
Rep
licat
e
1
0.39
7
1.68
2.20
0.41
8
0.03
7
0.53
2
0.90
6
0.40
0
3.57
*
2.99
*
Att
ribu
te
df
Aro
ma
R
ose
C
love
C
aram
el
Flav
or
R
ose
C
love
C
aram
el
Tast
e
S
ourn
ess
B
itter
ness
Mou
thfe
el
D
ryin
g
H
eat
Sour
ce o
f Err
or
Tab
le 9
. Cal
cula
ted
F-va
lues
and
sign
ifica
nt in
tera
ctio
ns o
f the
trai
ned
pane
l for
Mer
lot w
ines
. Rep
: R
eplic
ate;
Pan
: Pan
elis
t; Et
OH
: Eth
anol
; Tan
: Tan
nin;
Fru
c: F
ruct
ose.
Sig
nific
ance
is d
enot
ed a
s *
(p<0
.1),
** (p
<0.0
5), *
** (p
<0.0
1).
59
59
variability. Perception of bitterness and drying was significantly affected by a two-way effect
between ethanol*tannin (p<0.05 and p<0.01, respectively).
All results from sensory evaluation should be interpreted carefully as significant
panelist effects were observed for all attributes. Multiple sources may be the cause including
insufficient training, panelist error, or decreased panelist sensitivity. In terms of training,
panelists may have been incompletely trained in the attributes and their standards, which
would lead to inconsistencies among the panelists for treatment evaluations. Amerine (1975)
suggested a minimum of 20 hours of training for descriptive panels. Another study (Chambers
et al. 2004) indicated that more training (120 hours) is required if more attributes are to be
discriminated. The present study consisted of only eight hours of training, but panelists were
given a reference sheet listing the aromas, flavors, tastes, and mouthfeel of the standards
discussed during training. Additionally, the list indicated the panelist mean for the intensity of
each attribute standard which were collected during the fifth training session.
Although insufficient training may be a cause for significant differences among
panelists, it was apparent that heat was significantly different between ethanol concentrations.
Thus, it can be inferred that training may actually have been sufficient, and the high incidence
of panelist error was more likely due to sensitivity differences among panelists. The
sensitivity of panelists was determined using the PROP test (Tepper et al. 2001). Of the ten
panelists, four were non-tasters, five were medium-tasters, and only one was a super-taster.
Although not completely definitive, taster status of the panelists was used to indicate which
panelists might be more or less sensitive to the tastes and flavors under study. While no
statistical outliers were detected, it was apparent that at least one specific panelist had
difficulty distinguishing the attributes. This difference in sensitivity could account for the
60
60
panelist effects found in Table 9. Distinguishing differences for those less sensitive to the
attributes was even more difficult, considering the actual differences between the volatility of
3-methyl-1-butanol, 2-phenylethanol, and eugenol were not large enough for differences in
human perception.
Panelist evaluations resulted in differences for all attributes depending on the
treatments (Table 10). Rose aroma intensity, associated with 2-phenylethanol, significantly
increased with increasing fructose concentration, with the effect most apparent at 8% ethanol.
Rose flavor also increased with increasing fructose concentration, especially at 8% ethanol
and high tannin. The increase in perceived rose flavor can be explained by the effects
previously described involving odor judgments increasing as an associated taste concentration
increases. Murphy and Cain (1980) showed that as sucrose concentration increased and citral
concentration was kept constant, perceived overall aroma increased. This bias, also known as
the dumping effect, is particularly possible because sweetness was not evaluated in this study.
Clove aroma, associated with eugenol, significantly decreased with increasing fructose
(Table 10), consistent with the analytical data for eugenol. For clove flavor, treatments at
3.2% ethanol (treatment 1 to 4) and the treatment at 8% ethanol and low tannin had
significantly lower intensity ratings than the 16% ethanol treatments with high tannin. This
confirmed that clove flavor significantly increased (p<0.01) with increasing ethanol and
tannin. Caramel flavor, which is associated with 3-methyl-1-butanol, was affected by fructose
(p<0.1), with a decrease in intensity with increasing fructose concentrations, consistent with
the analytical data for 3-methyl-1-butanol.
Other studies have found similar results of effects of ethanol on perceived aroma and
flavor. Some research has reported no significant differences in aroma or flavor intensity
61
61
Hea
t
2.2i
2.6hi
3.1fghi
2.8ghi
3.9efgh
4.6de
4.0efg
4.2ef
6.3c
6.7c
5.8cd
6.5c
9.6ab
9.8ab
10.9a
8.4b
Dry
ing
3.3ef
2.3f
9.8abc
9.3abc
3.2ef
3.2ef
10.1a
10.0ab
3.1ef
3.7de
9.3abc
9.3abc
4.4de
4.8d
8.7bc
8.5c
Bit
tern
ess
3.2ef
2.6f
4.6cde
4.7cde
3.5ef
3.3ef
7.7a
6.6ab
2.9f
2.8f
6.5ab
5.3bcd
3.9def
3.7ef
5.6bc
5.3bc
Sour
ness
7.7a
5.9bcd
6.3ab
6.1bcde
5.2bcde
4.9bcde
5.3bcde
5.3bcde
3.7e
4.6cde
5.5bcd
5.5bcd
4.9bcde
4.4de
4.4de
5.0bcde
Car
amel
F
lavo
r
3.4ab
3.6ab
3.0b
3.2ab
3.8ab
3.8ab
3.8ab
2.9b
3.7ab
3.3ab
4.4ab
3.3ab
4.6a
3.5ab
4.4ab
3.4ab
Clo
ve
Fla
vor
2.8def
1.9f
2.6def
2.3ef
2.8def
2.7def
3.8bcde
3.0cdef
3.4bcdef
3.3bcdef
4.6ab
4.1abcd
3.6bcde
3.3bcdef
5.6a
4.5abc
Ros
e F
lavo
r
3.7c
4.9abc
4.2abc
3.3c
4.1abc
4.3abc
3.7c
5.4ab
3.9bc
4.7abc
3.8c
4.9abc
4.2abc
5.5a
4.5abc
4.2abc
Car
amel
A
rom
a
6.2ab
5.4ab
5.0b
5.9ab
4.7b
4.9b
5.5ab
4.5b
4.3b
4.3b
7.0a
4.8b
5.6ab
5.8ab
5.6ab
5.5ab
Clo
ve
Aro
ma
4.1ab
3.5b
3.8b
3.2b
4.1ab
3.2b
4.8ab
3.8b
4.0ab
3.7b
4.3ab
3.8b
4.5ab
3.9b
5.6a
4.4ab
Ros
e A
rom
a
4.1b
5.5ab
5.0ab
4.7ab
4.2b
4.9ab
4.8ab
6.4a
4.7ab
4.9ab
4.0b
5.6ab
4.8ab
5.5ab
5.2ab
4.7ab
Trea
tmen
t
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Att
ribu
te
Tab
le 1
0. M
ean
inte
nsity
ratin
gs fo
r Mer
lot t
reat
men
ts a
s det
erm
ined
by
a tra
ined
pan
el (n
=9) u
sing
a 1
5 cm
anc
hore
d lin
e sc
ale.
Rep
licat
e ev
alua
tions
wer
e m
ade
over
7 d
ays.
Mea
ns w
ith d
iffer
ent l
ette
rs w
ithin
col
umns
are
sign
ifica
ntly
di
ffer
ent (
p<0.
05) u
sing
Fis
her’
s LSD
. Tre
atm
ent n
umbe
rs re
fer t
o tre
atm
ents
des
crib
ed in
Tab
le 1
.
62
62
when ethanol increased from 11.6% to 13.6% v/v (Gawel et al. 2007) or even up to 17%
(Conner et al. 1994). Here, the concentration difference was between 3.2% and 16%, and the
only attribute affected was clove flavor. In the study by Conner et al. (1994), it was described
that ethanol is monodispersed, or in a non-aggregated state, in water up to concentrations of
17%, and has similar properties to water. Therefore, an increase in ethanol likely did not
increase the perception of each aroma and flavor attribute. Likely, clove flavor perception
increased with ethanol due to panelist confusion between the pungency of ethanol and the
pungency of eugenol.
Tastes and mouthfeel were more widely affected by matrix component variations than
aromas and flavors (Table 10). Sourness was significantly higher in 3.2% ethanol treatments.
These findings are consistent with Martin and Pangborn (1970) and Fischer and Noble (1994),
where both studies found a masking effect on sourness by ethanol. In Martin and Pangborn’s
study (1970), citric acid was increased from 0.04 to 0.24%, and ethanol from 4 to 24%, with
results showing that higher ethanol significantly depressed the taste intensity of sourness.
Fischer and Noble (1994) found that an increase in ethanol from 8% to 14% decreased
perceived sourness, most significantly at a pH of 3.2.
Bitterness was affected (p<0.05) by ethanol and tannin concentration, with an
interaction effect between the two components (Table 10). Generally, higher tannin
concentrations resulted in more intense bitterness ratings. This was likely due to the
constituents present in Biotan, the form in which the tannin was added. Biotan was found to
contain 26.8% tannin, which only includes tannins composed of at least four subunits. The
rest of the constituents in Biotan may have been tannins composed of less than four subunits.
It is possible that these smaller tannin molecules imparted a bitter taste, as Noble described
63
63
(1994). At low tannin levels, bitterness was not significantly affected by ethanol. However, at
high tannin levels, the 8% ethanol treatment received the highest bitterness ratings. This is
contradictory to previous studies that indicate ethanol increased perceived bitterness (Fischer
and Noble 1994, Martin and Pangborn 1970). Perhaps ethanol interacted with the taste buds
associated with bitter taste. This has been described previously for other tastes. For instance,
sour compounds and the hydrogen ion channels associated with sour taste are affected by
ethanol (Fischer and Noble 1994). Fructose did not affect bitterness ratings, as suggested by
Lyman and Green (1990), Noble (1994), Noble (1998), and von Sydow et al. (1974).
Drying properties of the wine were significantly (p<0.01) affected by an interaction
between ethanol and tannin (Table 10). An increase in tannin led to an increase in perceived
astringency, but this effect was muted at16% ethanol. This effect is in agreement with
previous research (Fontoin et al. 2008, Scinska et al. 2000) and may be due to the interference
effect of ethanol on the binding reactions between salivary proteins and tannins (Serafini et al.
1997). Although no significant difference was observed for drying with an increase in
fructose concentration, the data indicate that fructose may decrease bitterness, but only when
there is a minimal amount of ethanol (i.e. 3.2%).
Finally, perceived heat increased with increasing ethanol concentration, as previously
found (Jones et al. 2008). Interaction effects (p<0.1) for ethanol*fructose and tannin*fructose
were observed to affect perceived heat, with an increase in fructose when 16% ethanol and
high tannin are present resulting in a decrease in perceived heat.
Principal Component Analysis and Pearson Correlation
The results from the trained sensory evaluation panel can be compared to the
analytical data of the wine using Principal Component Analysis (PCA, Figure 5). PCA
64
64
Figu
re 5
. Prin
cipa
l Com
pone
nt A
naly
sis o
f sen
sory
and
che
mic
al a
ttrib
utes
in M
erlo
t. B
lue
poin
ts in
dica
te tr
eatm
ent a
nd
its p
lace
men
t. R
ed p
oint
s ind
icat
e se
nsor
y at
tribu
tes (
UPP
ERC
ASE
) and
che
mic
al a
ttrib
utes
(low
erca
se).
65
65
showed the relationships between sensorial and chemical attributes, and placed treatments
according to their profile. The PCA graph was explained by two main factor loadings; Factor
1 (F1) explained 39.8% of the variation, while Factor 2 (F2) explained 22.0%. F1 was defined
by the opposing relationship between the analytical values of 3-methyl-1-butanol, 2-
phenylethanol, eugenol, and ethanol, and the sensorial values of sourness and heat. F2 was
defined by the relationships between analytical values of tannin, fructose, and pH, and the
sensorial values of bitterness, drying, caramel aroma, and rose aroma and flavor.
Treatments were separated based on ethanol, tannin, and fructose concentrations.
Treatments were clustered based on their ethanol concentration. Treatments with 3.2%
ethanol were all found to the right of the figure, and, as ethanol concentration increased, the
treatment clusters moved towards the left of the PCA. Within each ethanol concentration
cluster, wines were separated according to their fructose and tannin concentration. For
instance, of treatments 1 to 4, all of which contained 3.2% ethanol, treatment 2 (2000 mg/L
fructose, 211 mg/L CE tannin) was placed in relation to higher measured fructose, while
treatment 3 (120 mg/L fructose, 1500 mg/L CE tannin) was placed in relation to higher
measured tannin. Treatments1 (120 mg/L fructose, 211 mg/L CE tannin) and 4 (2000 mg/L
fructose, 1500 mg/L CE tannin) were placed near to each other. This effect was observed
within each ethanol cluster.
Multiple relationships that can be observed in the PCA were analyzed using Pearson
Correlation (Table 11, Table 12). Measured ethanol and sensory perception of heat were
highly positively correlated (0.960). This validated the sensory data as it showed the trained
panelists were able to detect a difference in heat between treatments of different ethanol
concentrations. A similar relationship was found between measured tannin and drying
66
66
C
aram
el
Flav
or
1
-0.3
68
0.04
4
-0.1
94
0.51
4
0.51
8
-0.5
46
-0.2
19
0.24
0
-0.4
46
Clo
ve
Flav
or
1
0.54
1
-0.4
41
0.50
6
0.41
0
0.74
6
0.78
5
-0.2
68
0.39
5
0.19
9
-0.2
33
Ros
e Fl
avor
1
0.01
4
-0.2
54
-0.3
29
-0.0
86
-0.0
73
0.35
5
0.32
0
0.49
3
-0.1
33
-0.1
17
-0.3
32
Car
amel
A
rom
a
1
-0.3
54
0.23
5
0.39
5
0.40
5
0.29
5
0.23
4
0.05
1
0.03
0
-0.2
07
0.22
2
0.16
2
0.16
8
Clo
ve
Aro
ma
1
0.24
5
-0.1
33
0.80
0
0.60
1
-0.2
40
0.44
2
0.26
4
0.56
1
0.54
8
-0.6
16
0.24
7
0.30
7
-0.0
82
Ros
e A
rom
a
1
-0.1
56
-0.4
23
0.81
9
-0.0
72
-0.4
02
-0.2
41
0.15
9
0.22
2
0.17
3
0.10
5
0.55
4
0.17
6
-0.3
45
-0.0
05
EuO
L
1
-0.1
01
-0.4
32
0.13
3
-0.2
63
-0.7
51
-0.3
77
0.77
5
-0.2
68
-0.1
86
-0.8
34
-0.9
15
0.00
0
-0.0
90
-0.0
65
0.53
9
2-PE 1
0.98
2
-0.0
94
-0.4
65
0.08
9
-0.2
78
-0.7
85
-0.3
97
0.77
5
-0.2
27
-0.1
55
-0.8
85
-0.9
58
-0.0
51
-0.0
81
0.01
7
0.56
8
3-M
-1-B
1
0.96
6
0.93
5
-0.1
66
-0.5
13
0.04
5
-0.3
63
-0.7
63
-0.5
01
0.79
4
-0.1
84
-0.0
65
-0.9
30
-0.9
89
-0.0
23
0.01
9
0.05
2
0.64
1
Vari
able
s
3-M
-1-B
2-PE
EuO
L
Ros
e Aro
ma
Clo
ve A
rom
a
Car
amel
Aro
ma
Ros
e Fl
avor
Clo
ve F
lavo
r
Car
amel
Fla
vor
Sour
ness
Bitt
erne
ss
Dry
ing
Hea
t
Mea
sure
d Et
hano
l
Mea
sure
d Fr
ucto
se
Mea
sure
d Ta
nnin
pH Titra
tabl
e Aci
dity
Tab
le 1
1. P
ears
on C
orre
latio
n: c
orre
latio
ns b
etw
een
chem
ical
com
pone
nts a
nd se
nsor
y at
tribu
tes o
f aro
mas
and
flav
ors.
Bol
d te
xt in
dica
tes s
igni
fican
ce (p
<0.0
5). 3
-M-1
-B: 3
-met
hyl-1
-but
anol
; 2-P
E: 2
-phe
nyle
than
ol; E
uOL:
eug
enol
.
67
67
TA 1
pH 1
0.02
2
Mea
sure
d Ta
nnin
1
0.04
4
0.65
3
Mea
sure
d Fr
ucto
se
1
0.04
5
-0.6
31
-0.0
03
Mea
sure
d E
than
ol
1
-0.0
05
-0.0
24
-0.0
65
-0.6
44
Hea
t
1
0.96
0
-0.0
09
-0.0
27
-0.1
60
-0.6
16
Dry
ing
1
0.04
0
0.05
0
0.03
9
0.97
7
0.08
5
0.58
8
Bitt
erne
ss
1
0.89
1
0.06
5
0.14
3
-0.1
05
0.83
5
0.29
7
0.48
2
Sour
ness
1
0.02
7
0.11
4
-0.7
07
-0.7
39
-0.0
86
0.17
2
0.23
9
0.50
6
Vari
able
s
3-M
-1-B
2-PE
EuO
L
Ros
e Aro
ma
Clo
ve A
rom
a
Car
amel
Aro
ma
Ros
e Fl
avor
Clo
ve F
lavo
r
Car
amel
Fla
vor
Sour
ness
Bitt
erne
ss
Dry
ing
Hea
t
Mea
sure
d Et
hano
l
Mea
sure
d Fr
ucto
se
Mea
sure
d Ta
nnin
pH Titra
tabl
e Aci
dity
Tab
le 1
1. C
ontin
ued.
68
68
perception (0.977). Other notable relationships included the positive correlation between rose
aroma and flavor (0.819), as well as clove aroma and flavor (0.800).
Volatile compounds quantified using GC/MS—3-methyl-1-butanol, 2-phenylethanol,
and eugenol—were negatively correlated with ethanol (≥0.91) as expected from the ANOVA,
but positively correlated with titratable acidity (≥0.53), and sourness (≥0.77). The relationship
between perceived acidity and volatile compound concentration was discussed by Jones et al.
(2008). They observed an increase in acidity with an increase in a reconstituted volatile
mixture containing 14 volatiles, including 2-phenylethanol. The researchers attributed this
increase in acidity to cognitive interactions with taste perception, indicating that the volatiles
added would not significantly contribute perceived acidity to the mixture. Instead, odor-taste
interactions or confusion may account for the correlation between compound headspace
concentration, sourness, and titratable acidity.
Taste and mouthfeel relationships were observed as well. Sourness was negatively
correlated to heat (-0.707), indicating an increase in perceived heat may have a masking effect
on the sourness of wines, as previously reported (Fischer and Noble 1994, Martin and
Pangborn 1970). Bitterness and drying were also significantly correlated (0.891). Because
bitterness was also correlated with measured tannin (0.835), higher bitterness was perceived
in high tannin wines likely because gallic acid and catechin, two components of wine
phenolics and probably in Biotan, naturally imparted a bitter taste (Robichaud and Noble
1990, Thorngate 1995).
Perception of heat and clove aroma, clove flavor, and caramel flavor were correlated
(≥0.51), and clove aroma and flavor were also weakly correlated with caramel flavor (≥0.54).
These relationships could be explained by panelist error in confusing each of these volatile
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69
compounds with ethanol burn. Jones et al. (2008) described a relationship between ‘hotness’
and a reconstituted mixture of 14 volatiles including 2-phenylethanol: an increase in volatiles
increased perceived ‘hotness’. They indicated that perhaps some components have a
‘pungent’ note, which can contribute to hotness or heat. This explanation is likely in the
present study, as well: both 3-methyl-1-butanol and eugenol have been described as pungent
previously (Abraham and Berger 1994, Jordan et al. 2001, Qian and Wang 2005).
Perception of rose aroma and flavor, according to the PCA, were both positively
(0.554, p<0.05, and 0.493, not significant, respectively) related to fructose concentration. This
supports Fisher’s LSD mean separation on the sensory data found in Table 10. Clove aroma
was positively correlated with both heat and measured fructose (0.561 and 0.594,
respectively). Clove flavor was positively correlated to bitterness (0.506) and heat (0.746).
The fact that clove flavor had a higher correlation to heat than clove aroma was representative
of the fact that flavor is affected not only by retronasal odors. While caramel aroma was not
significantly correlated with any attribute, caramel flavor had positive correlations with heat
and measured ethanol (0.514 and 0.518, respectively), and a negative relationship with
measured fructose (-0.546).
Overall, the sensory results did not show a relationship between caramel, rose, and
clove aromas and flavors and their measured headspace concentrations. It was previously
shown that training was likely sufficient, as sensory results had a strong correlation with
analytical values for all taste and mouthfeel attributes. The published thresholds for 3-methyl-
1-butanol, 2-phenylethanol, and eugenol were 30 mg/L, 10 mg/L, and 0.005 mg/L,
respectively, in 10% (v/v) ethanol. The concentrations of each compound in all treatments
studied were higher than the threshold values, yet differences were still not detected.
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70
Differences in aromas associated with these compounds may have been easier detected in a
less-complex matrix solution.
Cognitive interactions may be used to explain the weak relationship between the
sensory results and analytical results observed for the volatile compounds. For 3-methyl-1-
butanol and eugenol, pungent compounds, an increase in ethanol was confused for an increase
in pungency due to 3-methyl-1-butanol and eugenol, resulting in a positive relationship
between perception of caramel, clove and increasing ethanol. For 2-phenylethanol, a dumping
bias for rose flavor and aroma perception was observed when fructose concentration
increased. Therefore, perception of rose aroma and flavor was more closely related to fructose
perception than it is to the actual volatility of 2-phenylethanol.
Although the sensory and chemical data of the aromas and their volatile compounds
were not well correlated, the results are still applicable. While the reduction of ethanol by
saigneé/water addition and dealcoholization may reduce the headspace concentrations of
some volatile aroma compounds, perception of these specific compounds was not
significantly affected. Alteration of macro-molecules may therefore be accomplished without
affecting wine quality.
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71
CHAPTER V
CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
The increase of ethanol in wines due to longer hang-times, a warmer climate, and
improved viticultural and enological practices has resulted in increased research into the
influence of ethanol on wine quality. The present study indicated the effects on wine quality
of increasing ethanol on wine quality. Additionally, the interactions among tannin and
fructose concentrations with ethanol that can result from reducing ethanol by saigneé/water
addition or dealcoholization were determined.
Chemically, ethanol decreased the headspace concentrations of all aroma compounds
studied, but tannin and fructose also influenced volatility to a lesser extent, especially at low
ethanol concentrations. Increasing ethanol concentrations from 8% to 16% decreased
headspace concentrations of aroma volatiles in Merlot. While tannin and fructose also have
the potential to affect headspace concentrations of aroma volatiles, as was observed at 3.2%
ethanol for all three compounds, the effect is less likely in typical Merlots, which range from
10 to 15% ethanol. This is relevant to winemaking because volatile compounds compose one
of the most complex attributes of wine: aroma. Aroma is a major factor in determining wine
quality and its acceptance among consumers.
Based on sensory results, interactions among matrix components influenced the
intensity of various attributes. The interactions between ethanol and tannin affected
astringency and bitterness. Heat perception was affected by two-way interactions involving
ethanol x fructose and tannin x fructose. Ethanol also interacted with fructose or tannin to
affect heat perception, and ethanol reduced sourness. Perception of the three aroma
compounds was not affected by ethanol concentration, contrary to the hypothesis. Ethanol,
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72
along with an increase in tannin, did increase the perception of clove flavor. Fructose affected
the perception of most of the aroma and flavor attributes: an increase in fructose increased the
rose aroma, and decreased clove aroma and flavor, and caramel flavor. The only attribute
affected by a three-way interaction between ethanol, tannin, and fructose was rose flavor,
which was most intense in 16% ethanol, 211 mg/L CE tannin, and 2000 mg/L fructose, and
least intense in 3.2% ethanol, 1500 mg/L CE tannin, and 2000 mg/L fructose.
After analysis of results using Pearson Correlation and PCA, it was apparent that
results involving aroma and flavor attributes did not correlate well with chemical analysis of
the volatile aroma compounds. This was mainly due to psychological interactions between
perception of attributes and panelist error. However, while aroma and flavor differences may
not be apparent to novice wine drinkers, consumers with more experience and a higher
appreciation for wine may be able to detect differences.
This research presents guidelines for producing wines with specific intensities of three
specific aroma compounds. For instance, a winemaker who wishes to minimize sourness and
enhance the aroma and flavor of roses may utilize a combination of saigneé and water
addition to make a wine consisting of approximately 12% v/v ethanol, low tannin, and high
fructose, as these concentrations of macro-components reduced sourness perception and
increased rose aroma and flavor. Perhaps they would like to produce another product that is
considered more astringent, with more intense clove and caramel aromas and flavors. In this
case, the winemaker might choose to use saigneé without water addition to produce a wine
containing 12% to 16% ethanol, higher tannin levels, and lower residual fructose levels.
Finally, winemakers wishing to dealcoholize their wines completely should be aware
of potential changes to the perception of their wine. While the headspace concentrations of
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volatile aroma components may be higher than the original, alcoholic wine, the dealcoholized
wine may be excessively sour, and anatomical and sensory differences among consumers may
actually decrease perception of some aroma and flavors.
While this study was the first to describe three-way interactions of principal macro-
components in an actual wine matrix, the study has limitations. First, only three volatile
aroma compounds were studied. As different volatile aroma compounds interact differently
with ethanol, tannin, and fructose, future experiments should include a variety of aroma
compounds, varying in type of compound (alcohol, aldehyde, ester, etc.), as well as associated
aroma (fruity, earthy, woody, vegetative, etc.) and preference for aroma (e.g. Brettanomyces
spp. metabolites). The effects of different macro-components would also be beneficial. For
instance, glucose and sucrose are commonly found in a finished wine, in addition to fructose.
Also, other polyphenolic compounds, which can be altered due to saigneé/water addition or
dealcoholization, may affect chemical and sensory properties of Merlot. Another set of
components not included in this study was organic acids.
It may also be helpful to test differences between the wines using different types of
panelists (highly inexperienced consumer or highly experienced trainees). This would serve to
show relationships between volatile and non-volatile components, as various types of
consumers would experience them. It would also be interesting to conduct a “likeliness to
buy” study involving the treatments studied in this project and treatments involving other
macro-components of Merlot wine matrices. This would generate an economical drive either
for or against particular enological treatments.
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