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

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.

ii

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.

iii

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.

iv 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.

v 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

vi 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

vii 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

viii 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

ix

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

x 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

1 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.

2

(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.

3

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

4 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

5

(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.

6

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

7 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

8

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).

9

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

10 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-

11 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).

12

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

13 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

14 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).

15

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).

16

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

17

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

18 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

19 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

21 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

22

a) b)

c)

Figure 1. Chemical structure of a) 3-methyl-1-butanol, b) 2-phenylethanol, and c) eugenol, adapted from www.sigmaaldrich.com.

23 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

25 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,

26 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

27 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

28 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

31 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

32 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

33

(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

34 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

35

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 Ethanol (%) Final [Tannin] Final [Fructose] Number (mg/L CE) (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

36 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

37 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.

38

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 20 mL absolute ethanol (DI) water 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

39

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

40 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.

41

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

42

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)

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.

Trt # Ethanol % Tannin, mg/L Fructose, mg/L pH Titratable CE 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

44 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

45

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 R2 curve range (mg/L) 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

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).

Source of Variation df 3-methyl-1-butanol 2-phenylethanol Eugenol Replicate 1 9.08*** 0.013 0.601 Ethanol 3 2340*** 246*** 177*** Tannin 1 7.41*** 6.33** 8.96*** Fructose 1 5.33** 0.000 0.000 Ethanol*Tannin 3 4.54*** 2.24* 6.32*** Ethanol*Fructose 3 4.64*** 1.610 2.170 Tannin*Fructose 1 3.81* 0.009 0.823 Ethanol*Tannin*Fructose 3 4.09** 2.49* 1.230

47

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

48

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

49

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-

50

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 Eugenol 1 1.00 1.00 1.00 2 0.90 1.09 1.23 3 0.95 0.92 0.82 4 0.95 0.96 0.83 5 0.66 0.63 0.50 6 0.67 0.65 0.44 7 0.61 0.66 0.48 8 0.62 0.63 0.39 9 0.46 0.42 0.23 10 0.46 0.45 0.21 11 0.45 0.45 0.24 12 0.44 0.41 0.21 13 0.29 0.41 0.18 14 0.28 0.26 0.12 15 0.30 0.23 0.13 16 0.29 0.27 0.14

51

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 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.

53

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

54

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 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

57

3 1.29 1.30 1.71 0.039 2.11* 0.273 0.187 0.882 0.581 0.318 E x T x F T E x 1 T x F T 0.033 0.093 0.411 0.574 0.403 0.416 0.750 0.423 0.260 3.40* 3 1.06 E x F 0.477 0.166 0.611 0.489 0.103 0.886 0.215 0.820 2.57* 3 1.49 1.23 1.89 nel for Merlot wines. Rep: for wines. Rep: nel Merlot E x T E x 0.802 0.709 0.533 0.358 0.685 3.29** 7.35*** Significance is denoted as * is denoted Significance

1 (F) 2.17 0.752 3.08* 3.12* 0.420 0.107 0.000 4.28** 5.73** 5.19** Fructose Fructose Source of Error Source 1 (T) 1.12 0.421 0.813 0.281 0.388 0.721 0.005 Tannin Tannin 598*** 7.79*** 83.4*** 3 (E) 1.72 1.04 1.06 0.201 0.694 0.729 158*** Ethanol 8.50*** 8.42*** 5.05***

9 3.58*** 13.2*** 6.47*** 5.00*** 4.80*** 2.50*** 18.4*** 8.23*** 17.4*** 15.7*** Panelist 1 1.68 2.20 values and significant interactions of the trained pa of trained the interactions significant and values 0.397 0.418 0.037 0.532 0.906 0.400 3.57* 2.99* - Replicate df Calculated F Calculated . Attribute 9

Aroma Rose Clove Caramel Flavor Rose Clove Caramel Taste Sourness Bitterness Mouthfeel Drying Heat

Table Table Fructose. Fruc: Tannin; Tan: Ethanol; EtOH: Panelist; Pan: Replicate; (p<0.1), ** (p<0.05), *** (p<0.01).

58 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

59 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

60

a i c c c b hi ef de cd ab ab ghi efg fghi efgh 2.2 6.3 6.7 6.5 8.4 2.6 4.2 4.6 5.8 9.6 9.8 Heat 2.8 4.0 10.9 3.1 3.9 a ab f c d ef ef ef ef de de bc abc abc abc abc cm anchored anchored cm

2.3 8.5 4.8 3.3 3.2 3.2 3.1 3.7 4.4 8.7 10.1 9.8 9.3 9.3 9.3 10.0 Drying

. f a f f ef ef ef ab ab ef bc bc def cde cde bcd 2.6 7.7 2.9 2.8 3.2 3.5 3.3 6.6 6.5 3.7 5.6 5.3 Table 1 Table 3.9 4.6 4.7 5.3

in Bitterness a e ab de de bcd cde bcd bcd bcde bcde bcde bcde bcde bcde bcde 7.7 3.7 6.3 4.4 4.4 5.9 4.6 5.5 5.5 6.1 5.2 4.9 5.3 5.3 4.9 5.0 Sourness a b b ab ab ab ab ab ab ab ab ab ab ab ab ab 4.6 3.0 2.9 3.4 3.6 3.2 3.8 3.8 3.8 3.7 3.3 4.4 3.3 3.5 4.4 3.4 Flavor Caramel Caramel Attribute f a ef ab def def def def abc bcde cdef abcd bcde bcdef bcdef bcdef 1.9 5.6 2.3 4.6 2.8 2.6 2.8 2.7 4.5 Clove Clove 3.8 3.0 4.1 3.6 3.4 3.3 3.3 Flavor c c c c a ab bc abc abc abc abc abc abc abc abc abc 3.7 3.3 3.7 3.8 5.5 5.4 3.9 Rose 4.9 4.2 4.1 4.3 4.7 4.9 4.2 4.5 4.2 Flavor b b b b b b a b ab ab ab ab ab ab ab ab . Treatment numbers refer to treatments described described treatments refer to numbers . Treatment 5.0 4.7 4.9 4.5 4.3 4.3 7.0 4.8 6.2 5.4 5.9 5.5 5.6 5.8 5.6 5.5 Aroma Caramel Caramel a b b b b b b b b ab ab ab ab ab ab ab 5.6 3.5 3.8 3.2 3.2 3.8 3.7 3.8 3.9 4.1 4.1 4.8 4.0 4.3 4.5 4.4 Clove Clove Aroma a b b b ab ab ab ab ab ab ab ab ab ab ab ab 6.4 4.1 4.2 4.0 5.5 5.0 4.7 4.9 4.8 4.7 4.9 5.6 4.8 5.5 5.2 4.7 5) using Fisher’s LSD Rose Aroma Mean intensity ratings for Merlot treatments as determined by a trained panel (n=9) using a 15 (n=9) using a panel trained by a as determined for treatments ratings Merlot intensity Mean

. 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ifferent (p<0.0 Treatment Table Table significantly are columns within different 7 days. Means with letters over made were evaluations Replicate scale. line d

61 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

62

(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

63

PERCASE) and chemical attributes (lowercase). attributes PERCASE) chemical and Principal Component Analysis of sensory and chemical attributes in Merlot. Blue points indicate treatment and and treatment indicate points Blue Merlot. in attributes Analysis of sensorychemical Component and Principal . 5 Figure (UP sensory attributes indicate points Red placement. its

64 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

65

1 0.044 0.514 0.518 0.240 -0.368 -0.194 -0.546 -0.219 -0.446 Flavor Caramel Caramel EuOL: eugenol. EuOL: 1 Clove Clove 0.541 0.506 0.410 0.746 0.785 0.395 0.199 -0.441 -0.268 -0.233 Flavor 1 Rose phenylethanol; phenylethanol; 0.014 0.355 0.320 0.493 -0.254 -0.329 -0.086 -0.073 -0.133 -0.117 -0.332 Flavor - PE: 2 PE: - 1 0.235 0.395 0.405 0.295 0.234 0.051 0.030 0.222 0.162 0.168 -0.354 -0.207 Aroma Caramel Caramel butanol; 2 butanol; 1 - 1 Clove Clove 0.245 0.800 0.601 0.442 0.264 0.561 0.548 0.247 0.307 -0.133 -0.240 -0.616 -0.082 - Aroma 1 methyl - Rose 0.819 0.159 0.222 0.173 0.105 0.554 0.176 -0.156 -0.423 -0.072 -0.402 -0.241 -0.345 -0.005 Aroma B: 3 B: - 1 1 - 0.133 0.775 0.000 0.539 M EuOL -0.101 -0.432 -0.263 -0.751 -0.377 -0.268 -0.186 -0.834 -0.915 -0.090 -0.065 - . 3 1 2-PE 0.982 0.089 0.775 0.017 0.568 -0.094 -0.465 -0.278 -0.785 -0.397 -0.227 -0.155 -0.885 -0.958 -0.051 -0.081 : correlations between chemical components and sensory attributes of aromas and flavors. of and aromas sensory and attributes components chemical between correlations : 1 0.966 0.935 0.045 0.794 0.019 0.052 0.641 -0.166 -0.513 -0.363 -0.763 -0.501 -0.184 -0.065 -0.930 -0.989 -0.023 3-M-1-B earson Correlation earson P . 11 text indicates significance (p<0.05) significance indicates text Variables 3-M-1-B 2-PE EuOL Aroma Rose Aroma Clove Aroma Caramel Rose Flavor Flavor Clove Flavor Caramel Sourness Bitterness Drying Heat Ethanol Measured Fructose Measured Tannin Measured pH Acidity Titratable Table Table Bold

66

1 TA 1 pH 0.022 1 0.044 0.653 Tannin Measured Measured 1 0.045 -0.631 -0.003 Fructose Measured Measured 1 -0.005 -0.024 -0.065 -0.644 Ethanol Measured Measured 1 Heat 0.960 -0.009 -0.027 -0.160 -0.616 1 0.040 0.050 0.039 0.977 0.085 0.588 Drying 1 0.891 0.065 0.143 0.835 0.297 0.482 -0.105 Bitterness 1

0.027 0.114 0.172 0.239 0.506 -0.707 -0.739 -0.086 Sourness Continued. Table 11. Table

Variables 3-M-1-B 2-PE EuOL Aroma Rose Aroma Clove Aroma Caramel Rose Flavor Flavor Clove Flavor Caramel Sourness Bitterness Drying Heat Ethanol Measured Fructose Measured Tannin Measured pH Acidity Titratable

67 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

68 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.

69

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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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,

71 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

72 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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LITERATURE CITED

Abraham, B.G. and R.G. Berger. 1994. Higher fungi for generating aroma components through novel biotechnologies. J. Agric. Food Chem. 42:2344-2348.

Adler, E., M.A. Hoon, K.L. Mueller, J. Chandrashekar, N.J.P. Ryba and C.S. Zuker. 2000. A novel family of mammalian taste receptors. Cell. 100:693-702.

Amerine, M.A., E.B. Roessler and C.S. Ough. 1965. Acids and the acid taste. I. The effect of pH and titratable acidity. Am. J. Enol. Vitic. 16:29-37.

Amerine, M.A. and E.B. Roessler. 1975. Wines: Their Sensory Evaluation. W. H. Freeman and Company, San Francisco.

Aznar, M., R. Lopez, J.F. Cacho and V. Ferreira. 2001. Identification and quantification of impact odorants of aged red wines from Rioja. GC-olfactometry, quantitative GC-MS, and odor evaluation of HPLC fractions. J. Agric. Food Chem. 49:2924-2929.

Baiano, A., C. Terracone, G. Gambacorta and E. La Notte. 2009. Phenolic content and antioxidant activity of Primitivo wine: comparison among winemaking technologies. J. Food. Sci. 74:C258-267.

Bartoshuk, L.M., K. Fast, T.A. Karrer, S. Marino, R.A. Price and D.A. Reed. 1992. PROP supertasters and the perception of sweetness and bitterness. Chem. Sens. 17:594.

Bartoshuk, L.M., E. Conner, D. Grubin, T. Karrer, K. Kochenbach, M. Palsco, D. Snow, M. Pelchat and S. Danowski. 1993. PROP supertasters and the perception of ethyl alcohol. Chem. Sens. 18:526-527.

Bartoshuk, L.M., V.B. Duffy and I.J. Miller. 1994. PTC/PROP tasting: anatomy, psychophysics, and sex effects. Physiol. Behav. 56:1165-1171.

Beall, A. 2010. The influence of alcohol content on the consumer acceptance of red wine and threshold values of selected odorants. Thesis, Washington State University, Pullman, WA.

Beidler, L.M. 1978. Biophysics and chemistry of taste. In Handpook of Perception. E.C. Carterette and M.P. Friedman (eds.), pp. 21-49. Academic Press, New York.

Blanco, V.Z., J.M. Auw, C.A. Sims and S.F. O'Keefe. 1998. Effect of processing on phenolics of wines. In Process-Induced Chemical Changes in Food. F. Shahidi et al. (eds.), pp. 327-340. Plenum Press, New York.

74

Bonino, M., R. Schellino, C. Rizzi, R. Aigotti, C. Delfini and C. Baiocchi. 2003. Aroma compounds of an Italian wine (Ruché) by HS-SPME analysis coupled with GC-ITMS. Food Chem. 80:125-133.

Bonnans, S. and A.C. Noble. 1993. Effect of sweetener type and of sweetener and acid levels on temporal perception of sweetness, sourness and fruitiness. Chem. Sens. 18:273-283.

Boyd, W.C. and L.G. Boyd. 1937. Sexual and racial variations in ability to taste phenyl-thio- carbamide, with some data on the inheritance. Ann. Eugen. 8:46-51.

Bozza, T.C. and P. Mombaerts. 2001. Olfactory coding: Revealing intrinsic representations of odors. Curr. Biol. 11:R687-R690.

Bravo, L. 1998. Polyphenols: chemistry, dietary sources, metabolism, and nutritional significance. Nutr. Rev. 56:317-333.

Brieskorn, C.H. 1990. Physiological and therapeutical aspects of bitter compounds. In Bitterness in Foods and Beverages; Developments in Food Science 25. R.L. Rouseff (ed.), pp. 15-34. Elsevier, Amsterdam.

Buck, L.B. 2000. The molecular architecture of odor and pheremone sensing in mammals. Cell. 100:611-618.

Buttery, R.G., J.G. Turnbaugh and L.C. Ling. 1988. Contribution of volatiles to rice aroma. J. Agric. Food Chem. 36:1006-1012.

Catarino, M., A. Mendes, L. Madeira and A. Ferreira. 2006. Beer dealcoholization by reverse osmosis. Desalination. 200:397-399.

Chambers, D.H., A.M.A. Allison and E.I. Chambers. 2004. Training effects on performance of descriptive panelists. J. Sens. Stud. 19:486-499.

Chandrashekar, J., K.L. Mueller, M.A. Hoon, E. Adler, L. Feng, W. Guo, C.S. Zuker and N.J.P. Ryba. 2000. T2Rs function as bitter taste receptors. Cell. 100:703-711.

Cheynier, V., M. Duenas-Paton, E. Salas, C. Maury, J.-M. Souquet, P. Sarni-Manchado and H. Fulcrand. 2006. Structure and properties of wine pigments and tannins. Am. J. Enol. Vitic. 57:298-305.

Chung, K.T., T.Y. Wong, C.I. Wei, Y.W. Huang and Y. Lin. 1998. Tannins and human health: a review. Crit. Rev. Food Sci. Nutr. 38:421-464.

Clarke, R.J. and J. Bakker. 2004. Wine Flavour Chemistry. Blackwell Publishing Ltd, Oxford, UK.

75

Conner, J.M., A. Paterson and J.R. Piggott. 1994. Interactions between ethyl esters and aroma compounds in model spirit solutions. J. Agric. Food Chem. 42:2231-2234.

Constantini, A., E. Garcia-Moruno and M.V. Moreno-Arribas. 2009. Biochemical transformations produced by malolactic fermentation. In Wine Chemistry and Biochemistry. M.V. Moreno-Arribas and M.C. Polo (eds.), pp. 27-57. Springer, New York.

Dalton, P., N. Doolittle, H. Nagata and P.A.S. Breslin. 2000. The merging of the senses: integration of subthreshold taste and smell. Nat. Neurosci. 3:431-432.

Dean, R.B. and W.J. Dixon. 1951. Simplified statistics for small numbers of observations. Anal. Chem. 23:636-638.

Delcour, J.A., M.M. Vandenberghe, P.F. Corten and P. Dondeyne. 1984. Flavor thresholds of polyphenolics in water. Am. J. Enol. Vitic. 35:134-136.

Diamant, H., M. Funakoshi, L. Strom and Y. Zotterman. 1963. Electrophysiological studies on human taste nerves. In Olfaction and Taste. Y. Zotterman (ed.), pp. 193-203. Pergamon Press, Oxford.

Diaz-Plaza, E.M., J.R. Reyero, F. Pardo and M.R. Salinas. 2002. Comparison of wine aromas with different tannic content aged in French oak barrels. Anal. Chim. Acta. 458:139-145.

Duchene, E. and C. Schneider. 2005. Grapevine and climatic changes: a glance at the situation in Alsace. Agron. Sustain. Dev. 25:93-99.

Duffy, V.B., J.M. Peterson and L.M. Bartoshuk. 2004. Associations between taste genetics, oral sensation and alcohol intake. Physiol. Behav. 82:435-445.

Dufour, C. and C.L. Bayonove. 1999a. Interactions between wine polyphenols and aroma substances. An insight at the molecular level. J. Agric. Food Chem. 47:678-684.

Dufour, C. and C.L. Bayonove. 1999b. Influence of wine structurally different polysaccharides on the volatility of aroma substances in a model system. J. Agric. Food Chem. 47:671-677.

Escalona, H., J.R. Piggott, J.M. Conner and A. Paterson. 1999. Effect of ethanol strength on the volatility of higher alcohols and aldehydes. Ital. J. Food Sci. 11:241-248.

Escuadero, A., E. Campo, L. Farina, J. Cacho and V. Ferreira. 2007. Analytical characterization of the aroma of five premium red wines. Insights into the role of odor families and the concept of fruitiness of wines. J. Agric. Food Chem. 55:4501-4510.

Etievant, P. 1991. Wine. In Volatile Compounds in Foods and Beverages. H. Maarse (ed.), pp. 483-546. Marcel Dekker, New York.

76

Fernberger, S.W. 1932. A preliminary study of taste deficiency. Am. J. Psychol. 44:322-326.

Ferreira, V., R. Lopez and J.F. Cacho. 2000. Quantitative determination of the odorants of young red wines from different grape varieties. J. Sci. Food Agric. 80:1659-1667.

Fischer, R. and R. Kaelbling. 1966. Increase in taste acuity with sympathetic stimulation: The relation of a just-noticeable taste difference to a systemic psychotropic drug use. Recent Adv. Biol. Psychiatry. 9:183-195.

Fischer, R. 1971. Gustatory, behavioral and pharmacological manifestations of chemoreception in man. In Gustation and Olfaction. G. Ohloff and A.F. Thomas (eds.), pp. 187-237. Academic Press, New York.

Fischer, U. and A.C. Noble. 1994. The effect of ethanol, catechin concentration, and pH on sourness and bitterness of wine. Am. J. Enol. Vitic. 45:6-10.

Fontoin, H., C. Saucier, P.-L. Teissedre and Y. Glories. 2008. Effect of pH, ethanol and acidity on astringency and bitterness of grape seed tannin oligomers in model wine solution. Food Qual. Pref. 19:286-291.

Fox, A.L. 1931. Six in ten "tasteblind" to bitter chemical. Sci. News Lett. 9:249.

Fulcrand, H., P.-J. Cameira dos Santos, P. Sarni-Manchado, V. Cheynier and J. Favre-Bonvin. 1996. Structure of new anthocyanin-derived wine pigments. J. Chem. Soc. Perkin Trans. 1:735-739.

Gambuti, A., A. Rinaldi, M.T. Lisanti, R. Pessina and L. Moio. 2011. Partial dealcoholisation of red wines by membrane contactor technique: influence on colour, phenolic compounds and saliva precipitation index. Eur. Food Res. Technol. 233:647-655.

Gawel, R. 1998. Red wine astringency: a review. Aust. J. Grape Wine Res. 4:74-95.

Gawel, R., S. van Sluyter and E.J. Waters. 2007. The effects of ethanol and glycerol on the body and other sensory characteristics of Riesling wines. Aust. J. Grape Wine Res. 13:38-45.

Godden, P. and M. Gishen. 2005. Trends in the composition of Australian wine. Aust. N.Z. Wine Ind. J. 20:21-46.

Godshall, M.A. 1997. How carbohydrates influence food flavor. Food Technol. 51:63-67.

Goldner, M.C., M.C. Zamora, P. Di Leo Lira, H. Gianninoto and A. Bandoni. 2009. Effect of ethanol level in the perception of aroma attributes and the detection of volatile compounds in red wine. J. Sens. Stud. 24:243-257.

77

Grosch, W. 2001. Evaluation of the key odorants of foods by dilution experiments, aroma models and omission. Chem. Sens. 26:533-545.

Guinard, J.-X., C. Zoumas-Morse, J. Dietz, S. Goldberg, M. Holz, E. Heck and A. Amoros. 1996. Does consumption of beer, alcohol, and bitter substances affect bitterness perception? Physiol. Behav. 59:625-631.

Gürbüz, O., J.M. Rouseff and R.L. Rouseff. 2006. Comparison of aroma volatiles in commercial Merlot and Cabernet Sauvignon wines using gas chromatography-olfactometry and gas-chromatography-mass spectrometry. J. Agric. Food Chem. 54:3990-3996.

Guth, H. 1997a. Identification of character impact odorants of different white wine varieties. J. Agric. Food Chem. 45:3022-3026.

Guth, H. 1997b. Quantitation and sensory studies of character impact odorants of different white wine varieties. J. Agric. Food Chem. 45:3027-3032.

Hagerman, A.E. and L.G. Butler. 1978. Protein precipitation method for the quantitative determination of tannins. J. Agric. Food Chem. 26:809-812.

Harbertson, J.F., J.A. Kennedy and D.O. Adams. 2002. Tannins in skins and seeds of Cabernet Sauvignon, Syrah, and Pinot noir berries during ripening. Am. J. Enol. Vitic. 53:54- 59.

Harbertson, J.F., M.S. Mireles, E.D. Harwood, K.M. Weller and C.F. Ross. 2009. Chemical and sensory effects of saigneé, water addition, and extended maceration on high brix must. Am. J. Enol. Vitic. 60:450-460.

Hartman, P.J., H.M. McNair and B.W. Zoecklein. 2002. Measurement of 3-alkyl-2- methyoxypyrazine by headspace solid-phase microextraction in spiked model wines. Am. J. Enol. Vitic. 53:285-288.

Haslam, E. 1980. In vino veritas: Oligomeric procyanidins and the ageing of red wines. Phytochemistry. 19:2577-2582.

Haslam, E. and T.H. Lilley. 1988. Natural astringency in foodstuffs - a molecular interpretation. Crit. Rev. Food Sci. 27:1-40.

Hladik, C.M. and B. Simmen. 1996. Taste perception and feeding behavior in nonhuman primates and human populations. Evol. Anthropol. 5:58-71.

Hunt, W.A. 1985. Alcohol and biological membranes. Guilford Press, New York.

Jackson, R.S. 2000. Wine Science. Academic Press, San Diego, CA.

78

Jensen, J.S., H.H.M. Werge, M. Egebo and A.S. Meyer. 2008. Effect of wine dilution on the reliability of tannin analysis by protein precipitation. Am. J. Enol. Vitic. 59:103-105.

Jones, G.V. 2007. Climate change: Observations, projections, and general implications for viticulture and wine production. In Climate and Viticulture Congress. E. Essick et al. (eds.) Zaragoza, Spain.

Jones, G.V., M.A. White, O.R. Cooper and K. Storchmann. 2005. Climate change and global wine quality. Climatic Change. 73:319-343.

Jones, G.V. and G.B. Goodrich. 2008. Influence of climate variability on wine regions in the western USA and on wine quality in the Napa Valley. Clim. Res. 35:241-254.

Jones, P.R., R. Gawel, I.L. Francis and E.J. Waters. 2008. The influence of interactions between major white wine components on the aroma, flavour and texture of model white wine. Food Qual. Pref. 19:596-607.

Jordan, M.J., K. Tandon, P.E. Shaw and K.L. Goodner. 2001. Aromatic profile of aqueous banana essence and banana fruit by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry (GC-O). J. Agric. Food Chem. 49:4813-4817.

Kallithraka, S., J. Bakker and M.N. Clifford. 1997a. Evaluation of bitterness and astringency of (+)-catechin and (-)-epicatechin in red wine and model solution. J. Sens. Stud. 12:25-37.

Kallithraka, S., J. Bakker and M.N. Clifford. 1997b. Red wine and model wine astringency as affected by malic and lactic acid. J. Food. Sci. 62:416-420.

Karrer, T., L.M. Bartoshuk, E. Conner, S. Fehrenbaker, D. Grubin and D. Snow. 1992. PROP status and its relationship to the perceived burn intensity of capsaicin at different tongue loci. Chem. Sens. 17:649.

Katalinić, V. 1997. High-performance liquid chromatographic determination of flavan fingerprint in plant extracts. J. Chromatogr. A. 775:359-367.

Katalinić, V. 1999. Grape catechins—natural antioxidants. J. Wine Res. 10:15-23.

Kavanagh, T.E., B.J. Clarke, P.S. Gee, M. Miles and B.N. Nicholson. 1991. Volatile flavor compounds in low alcohol beers. Tech. Q. Master Brew. Assoc. Am. 28:111-118.

King, B.M. and J. Solms. 1982. Interactions of volatile flavour compounds with propyl gallate and other phenols as compared with caffeine. J. Agric. Food Chem. 30:838-840.

Kotseridis, Y. and R. Baumes. 2000. Identification of impact odorants in Bordeaux red grape juice, in the commercial yeast used for its fermentation, and in the produced wine. J. Agric. Food Chem. 48:400-406.

79

Lamuela-Raventos, R.M. and A.L. Waterhouse. 1994. A direct HPLC separation of wine phenolics. Am. J. Enol. Vitic. 45:1-5.

Landon, J.L., K. Weller, J.F. Harbertson and C.F. Ross. 2009. Chemical and sensory evaluation of astringency in Washington State red wines. Am. J. Enol. Vitic. 60:442-449.

Liu, M., Z. Zeng and Y. Tian. 2005. Elimination of matrix effects for headspace solid-phase microextraction of important volatile compounds in red wine using a novel coating. Anal. Chim. Acta. 540:341-353.

Lyman, B.J. and B.G. Green. 1990. Oral astringency: effects of repeated exposure and interactions with sweeteners. Chem. Sens. 15:151-164.

Malnic, B., J. Hirono, T. Sato and L.B. Buck. 1999. Combinatorial receptor codes for odors. Cell. 96:713-723.

Martin, S. and M. Pangborn. 1970. Taste interaction of ethyl alcohol with sweet, salty, sour and bitter compounds. J. Sci. Food Agric. 21:653-655.

Mattes, R.D. 1994. Influences on acceptance of bitter foods and beverages. Physiol. Behav. 56:1229-1236.

McBurney, D.H. 1978. Psychological dimensions and perceptual analyses of taste. In Handbook of Perception. E.C. Carterette and M.P. Friedman (eds.), pp. 125-155. Academic Press, New York.

Meillon, S., C. Urbano and P. Schlich. 2009. Contribution of the temporal dominance of sensations (TDS) method to the sensory description of subtle differences in partially dealcoholized red wines. Food Qual. Pref. 20:490-499.

Meillon, S., D. Viala, M. Medel, C. Urbano, G. Guillot and P. Schlich. 2010. Impact of partial alcohol reduction in Syrah wine on perceived complexity and temporality of sensations and link with preference. Food Qual. Pref. 21:732-740.

Miller, M.E. and J.D. Stuart. 1999. Comparison of gas-sampled and SPME-sampled static headspace for the determination of volatile flavor components. Anal. Chem. 71:23-27.

Mitchell, M.J. and R.A.M. Gregson. 1968. Interrelations of perceptual reports of smell, taste and irritance over the near-threhold range, for members of the N-aliphatic monohydric alcohol series. Percept. Psychophys. 4:13-18.

Mombaerts, P., F. Wang, C. Dulac, R. Vassar, S. Chao, A. Nemes, M. Mendelsohn, J. Edmondson and R. Aexl. 1996. The molecular biology of olfactory perception. Cold Spring Harb. Symp. Quant. Biol. 61:135-145.

80

Murphy, C., W.S. Cain and L.M. Bartoshuk. 1977. Mutual action of taste and olfaction. Sens. Proc. 1:204-211.

Murphy, C. and W.S. Cain. 1980. Taste and olfaction: independence vs. interaction. Physiol. Behav. 24:601-605.

Nahon, D.F., Y. Navarro, P.A. Koren, J.P. Roozen and A. Posthumus. 1998. Flavor release from mixtures of sodium cyclamate, sucrose, and an orange aroma. J. Agric. Food Chem. 46:4963-4968.

Noble, A.C. 1994. Bitterness in wine. Physiol. Behav. 56:1251-1255.

Noble, A.C. 1998. Why do wines taste bitter and feel astringent? In Chemistry of Wine Flavor. A.L. Waterhouse and S.E. Ebeler (eds.), pp. 156-165. American Chemical Society, Washington, D.C.

Norris, M.B., A.C. Noble and M. Pangborn. 1984. Human saliva and taste responses to acids varying in anions, titratable acidity, and pH. Physiol. Behav. 32:237-244.

Pfannkoch, E., J. Whitecavage and A. Hoffman. 2002. Stir bar sorptive extraction: Capacity and competition effects. GERSTEL Applications Notes 2.

Pozo-Bayon, M.A. and G. Reineccius. 2009. Interactions between wine matrix macro- components and aroma compounds. In Wine Chemistry and Biochemistry. M.V. Moreno- Arribas and M.C. Polo (eds.), pp. 417-435. Springer, New York.

Prescott, J. and N. Swain-Campbell. 2000. Responses to repeated oral irritation by capsaicin, cinnamaldehyde, and ethanol in PROP tasters and non-tasters. Chem. Sens. 25:239-246.

Prieur, C., J. Rigaud, V. Cheynier and M. Moutounet. 1994. Oligomeric and polymeric procyanidins from grape seeds (Vitis vinifera). Phytochemistry. 36:781-784.

Qian, M.C. and Y. Wang. 2005. Seasonal variation of volatile compositioin and odor activity value of 'Marion' (Rubus spp. hyb) and 'Thornless Evergreen' (R. laciniatus L.) blackberries. Food Chem. Toxicol. 70:C13-C20.

Restani, P., A. Persico, C. Ballabio, D. Fuggetta, and E. Moro. 2007. Simultaneous determination of glycerol, fructose and glucose for quality control of wines. In XXXth World Congress of Vine and Wine. Budapest, Hungary.

Riou, V., A. Vernhet, T. Doco and M. Moutounet. 2002. Aggregation of grape seed tannins in model wine: Effect of polysaccharides. Food Hydrocolloid. 16:17-23.

81

Roberts, D.D., J.S. Elmore, K.R. Langley, and J. Bakker. 1996. Effects of sucrose, guar gum, and carboxymethylcellulose on the release of volatile flavor compounds under dynamic conditions. J. Agric. Food Chem. 44:1321-1326.

Robichaud, J.L. and A.C. Noble. 1990. Astringency and bitterness in selected phenolics in wine. J. Sci. Food Agric. 53:343-353.

Robinson, A.L., S.E. Ebeler, H. Heymann, P.K. Boss, P.S. Solomon and R.D. Trengove. 2009. Interactions between wine volatile compounds and grape wine matrix components influence aroma compound headspace partitioning. J. Agric. Food Chem. 57:10313-10322.

Rorabacher, D.B. 1991. Statistical treatment for rejection of deviant values: Critical values of Dixon's "Q" parameter and related subrange ratios at the 95% confidence level. Anal. Chem. 63:139-146.

Rozin, P. 1982. "Taste-smell confusions" and the duality of the olfactory sense. Percept. Psychophys. 31:397-401.

Rubin, B.D. and L.C. Katz. 1999. Optical imaging of odorant representations in the mammalian olfactory bulb. Neuron. 23:499-511.

Sanchez-Palomo, E., M.C. Diaz-Maroto and M.S. Perez-Coello. 2005. Rapid determination of volatile compounds in grapes by HS-SPME coupled with GC-MS. Talanta. 66:1152-1157.

Sanz, M.L. and Martinez-Castro. 2009. Carbohydrates. In Wine Chemistry and Biochemistry. M.V. Moreno-Arribas and M.C. Polo (eds.), pp. 231-248. Springer, New York.

Schiffman, S.S., B.J. Booth, M.L. Losee, S.D. Pecore and Z.S. Warwick. 1995. Bitterness of sweeteners as a function of concentration. Brain Res. Bull. 36:505-513.

Scinska, A., E. Koros, B. Habrat, A. Kukwa, W. Kostowski and P. Bienkowski. 2000. Bitter and sweet components of ethanol taste in humans. Drug Alcohol Depen. 60:199-206.

Serafini, M., G. Maiani and A. Ferro-Luzzi. 1997. Effect of ethanol on red wine tannin- protein (BSA) interactions. J. Agric. Food Chem. 45:3148-3151.

Singleton, V.L., H.A. Sieberhagen, P. de Wet and C.J. van Wyk. 1975. Composition and sensory qualities of wines prepared from white grapes by fermentation with and without grape solids. Am. J. Enol. Vitic. 26:62-69.

Souquet, J.-M., B. Labarbe, C. Le Guerneve, V. Cheynier and M. Moutounet. 2000. Phenolic composition of grape stems. J. Agric. Food Chem. 48:1076-1080.

Tepper, B.J., C.M. Christensen and J. Cao. 2001. Development of brief methods to classify individuals by PROP taster status. Physiol. Behav. 73:571-577.

82

Thorngate, J.H. and A.C. Noble. 1995. Sensory evaluation of bitterness and astringency of 3R(-)-epicatechin and 3S(+)-catechin. J. Sci. Food Agric. 67:531-535.

Trock, B., E. Lanza and P. Greenwald. 1990. Dietary fiber, vegetables, and colon cancer: critical review and meta-analyses of the epidemiological evidence. J. Natl. Cancer Inst. 82:650-651.

Vidal, S., D. Cartalade, J.-M. Souquet, H. Fulcrand and V. Cheynier. 2002. Changes in proanthocyanidin chain-length in wine-like model solutions. J. Agric. Food Chem. 50:2261- 2266.

Vidal, S., P. Courcoux, L. Francis, M. Kwiatkowski, R. Gawel, P. Williams, E. Waters and V. Cheynier. 2004a. Use of an experimental design approach for evaluation of key wine components on mouth-feel perception. Food Qual. Pref. 15:209-217.

Vidal, S., L. Francis, P. Williams, M. Kwiatkowski, R. Gawel, V. Cheynier and E. Waters. 2004b. The mouth-feel properties of polysaccharides and anthocyanins in a wine like medium. Food Chem. 85:519-525.

Vierra, G. 2004. Pretenders at the table: Are table wines no longer food friendly? In Wine Business Monthly. July 2004.

Villamor, R.R. 2012. Impact of wine components on the chemical and sensory properties of wine. Dissertation, Washington State University, Pullman, WA. von Sydow, E., H. Moskowitz, H. Jacobs and H. Meiselman. 1974. Odor-taste interaction in fruit juices. Lebensm.-Wiss. u. Technol. 7:18-24.

Wagner, P.M. 1976. Grapes Into Wine. Alfred A. Knopf, Inc., New York.

Whitehead, M.C., C.S. Beeman and B.A. Kinsella. 1985. Distribution of taste and general sensory nerve endings in fungiform papillae of the hamster. Am. J. Anat. 173:185-201.

Yu, P. and G.J. Pickering. 2008. Ethanol difference thresholds in wine and the influence of mode of evaluation and wine style. Am. J. Enol. Vitic. 59:146-152.

Zamora, F. 2009. Biochemistry of Alcoholic Fermentation. In Wine Chemistry and Biochemistry. M.V. Moreno-Arribas and M.C. Polo (eds.), pp. 3-26. Springer, New York.

Zamora, M.C., M.C. Goldner and M.V. Galmarini. 2006. Sourness-sweetness interactions in different media: white wine, ethanol and water. J. Sens. Stud. 21:601-611.

83