PERCEPTION OF FINISH IN WHITE WINE EMILY GOODSTEIN. A thesis submitted in partial fulfillment of the requirements for the degree of

Transcription

1 PERCEPTION OF FINISH IN WHITE WINE By EMILY GOODSTEIN A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN FOOD SCIENCE WASHINGTON STATE UNIVERSITY Department of Food Science DECEMBER 2011

2 To the Faculty of Washington State University: The members of the Committee appointed to examine the thesis of EMILY SIMPSON GOODSTEIN find it satisfactory and recommend that it be accepted. Carolyn Ross, Ph.D., Chair Jeffri Bohlscheid, Ph.D. Marc Evans, Ph.D. ii

3 ACKNOWLEDGEMENTS I would first and foremost like to acknowledge my father, who passed away on February 7 th, I think his excitement over me going to graduate school would only be surpassed by his excitement at my graduating. I appreciate and am reminded every day of his love, humor, support, and understanding, all of which has led me to this point. His passing during my time at graduate school has made me realize the importance of expressing how much you appreciate the people around you and to never miss the opportunity to thank someone for their contribution to your life. In that spirit, there are so many people at Washington State University that I would like to acknowledge. First, I would like to thank my advisor Carolyn Ross for the opportunity to come to graduate school and pursue a project involving flavor chemistry. Her support throughout this endeavor has made this project possible. I would like to thank my committee members, Jeff Bohlscheid and Marc Evans for their continual assistance which included answering numerous questions and giving excellent advice. I would also like to acknowledge Barbara Rasco, who helped me with editing and was always available for advice and discussion on many different topics. I would like to thank Karen Weller who was extremely helpful throughout my time at WSU and contributed so much to making this project successful. I would also like to thank Jodi Anderson for kindly answering all the questions I had for her both before and after I arrived at WSU. I would also like to acknowledge Scott Mattinson who contributed a great deal of time answering the many questions I had regarding the gas chromatography analysis performed in my study. I would like to thank all the members of the Ross Lab. We iii

4 experienced all the ups and downs of research together, and from panels to in the lab, there was always a question to be answered or a problem to be solved. To my best friends, Sean, Jesse, and Steve I could not have made through this time without them. They have seen me through all the different emotions I have experienced and all the changes in my life over the past two years. Thank you to Sean, who has been patient and helpful beyond what I ever would have expected from anyone. Thank you to Jesse, who has been available to both talk and listen, and always makes me laugh with her right-on remarks. Thank you to Steve, who has always helped me reason out obstacles with practical advice. Finally, I would like to acknowledge my mother, who has always been my number one cheerleader. She has helped me forge ahead and always keeps me looking towards the future. iv

5 PERCEPTION OF FINISH IN WHITE WINE Abstract by Emily Goodstein Washington State University December 2011 Chair: Carolyn F. Ross Wine finish is a sensory attribute associated with wine quality. Wine quality as it relates to finish is currently an understudied area of sensory research. This study aimed to perform both a trained and consumer evaluation of finish in white wines, with additional support provided by the analytical analysis of commercially available unoaked and oaked Chardonnay wines. The hypothesis of this study was that certain flavors such as coconut and mushroom would take longer to finish compared to fruit and floral flavors. Model and base wines were prepared through the addition of four chemical compounds; ethyl hexanoate (fruity), linalool (floral), oak lactone (coconut), and 1-octen-3-ol (mushroom). Trained panelists executed time intensity analysis of these four compounds for the aroma attributes of fruity, floral, coconut, and mushroom. Model wine evaluation of single compounds showed fruity flavor finished significantly earlier than all other flavors. Model wine evaluation of multiple flavors in solution indicated that fruity flavor displayed the shortest finish, with mushroom acting as a suppressor of other flavors. Normalization of model wine time intensity analysis showed that data variation was largely attributed to the contrasting relationship between fruity and mushroom flavors. Base wine analysis did not yield significant differences in flavor finish length. A trend of floral flavor suppression by coconut v

6 flavor emerged when analyzing time intensity parameters for intensity maximum (I max ) and area under the curve (AUC). Consumer panel analysis of finish of commercially available Washington State Chardonnays indicated that consumers were able to discern differences in finish length between unoaked and high oaked wines. Consumers significantly preferred unoaked wines over high oak wines and were willing to pay higher dollar values per bottle for unoaked wines than high oak wines. Solid phase microextraction-gas chromatography-mass spectrometry was utilized to quantify cis- and trans-oak lactone concentration of unoaked, medium, and high oak Chardonnay samples. Increases in oak treatment corresponded to increases in cis- and trans-oak lactone concentrations. This study provides the first rigorous examination of wine finish and its relationship to both the presence of different flavor compounds and consumer acceptance. vi

7 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS... iii ABSTRACT... v LIST OF TABLES... x LIST OF FIGURES... xv CHAPTER 1. INTRODUCTION LITERATURE REVIEW... 3 Wine finish... 3 Economic relevance... 3 Wine processing... 4 Non-volatile components of wine... 5 Volatile components of wine... 6 Volatiles to be analyzed by a trained panel... 7 Floral..8 Fruity 10 Mushroom.10 Barrel aged/coconut..11 Model wine Flavor perception Time intensity Barrel aging of white wine vii

8 Gas chromatography-mass spectrometry analysis of oak lactone MATERIALS AND METHODS Model wine Wines Trained panel Trained panel demographics Training sessions Trained panel formal evaluations Data analysis Consumer panel Consumer evaluation Data analysis Gas chromatography-mass spectrometry (GC-MS) RESULTS AND DISCUSSION Trained panel Single flavor compound analysis in model wine Multiple flavor compound analysis in model wine Single flavor compound analysis in base wine Multiple flavor compound analysis in base wine Consumer panel evaluation of commercial Chardonnay Gas chromatography-mass spectrometry FIGURES AND TABLES CONCLUSIONS AND FUTURE RESEARCH viii

9 7. REFERENCES ix

10 LIST OF TABLES Page 1. Analysis of variance (ANOVA) of time to maximum intensity (T max ) for single compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for single compounds in model wine samples analyzed by a trained panel (n=10) Pairwise comparison of maximum intensity (I max ) evaluations for single compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) duration (T end ) for single compounds in model wine samples analyzed by a trained panel (n=10) Pairwise comparison of duration (T end ) evaluations for single compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of area under the curve (AUC) for single compounds in model wine samples analyzed by a trained panel (n=10) Pairwise comparison of area under the curve (AUC) evaluations for single compounds in model wine samples analyzed by a trained panel (n=10) Mean Separation of Maximum Intensity (I max ), Duration (T end ), and area under the curve (AUC) for single compounds in model wine samples analyzed by the trained panel (n=10) Analysis of variance (ANOVA) of time to maximum intensity (T max ) for two compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for two compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of duration (T end ) for two compounds in model wine samples analyzed by a trained panel (n=10) x

11 12. Analysis of variance (ANOVA) of area under the curve (AUC) for two compounds in model wine samples analyzed by a trained panel (n=10) Mean separation of contrasts of the four flavors analyzed in two compound model wine solutions evaluated by a trained panel (n=10) Analysis of variance (ANOVA) of time to maximum intensity (T max ) for four compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for four compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) duration (T end ) for four compounds in model wine samples analyzed by a trained panel (n=10) Pairwise comparison of duration (T end ) evaluations for four compounds in model wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of area under the curve (AUC) for four compounds in model wine samples analyzed by a trained panel (n=10) Pairwise comparison of area under the curve (AUC) evaluations for four compounds in model wine samples analyzed by a trained panel (n=10) Mean separation of duration (T end ) and area under the curve (AUC) for four compounds in model wine samples analyzed by the trained panel (n=10) Analysis of variance (ANOVA) of time to maximum intensity (T max ) for single compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for single compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) duration (T end ) for single compounds in base wine samples analyzed by a trained panel (n=10) xi

12 24. Analysis of variance (ANOVA) of area under the curve (AUC) for single compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of time to maximum intensity (T max ) for two compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for two compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) duration (T end ) for two compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of area under the curve (AUC) for two compounds in base wine samples analyzed by a trained panel (n=10) Mean separation of contrasts of the four flavors analyzed in two compound base wine solutions evaluated by a trained panel (n=10) Analysis of variance (ANOVA) of time to maximum intensity (T max ) for four compounds in base wine samples analyzed by a trained panel (n=10) Pairwise comparison of time to maximum intensity (T max ) evaluations for four compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of maximum intensity (I max ) for four compounds in base wine samples analyzed by a trained panel (n=10) Pairwise comparison of maximum intensity (I max ) evaluations for four compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) duration (T end ) for four compounds in base wine samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of area under the curve (AUC) for four compounds in base wine samples analyzed by a trained panel (n=10) xii

13 36. Pairwise comparison of area under the curve (AUC) evaluations for four compounds in base wine samples analyzed by a trained panel (n=10) Mean separation of time to maximum intensity (T max ), maximum intensity (I max ), and area under the curve (AUC) for four compounds in base wine samples analyzed by the trained panel (n=10) Analysis of variance (ANOVA) table of time to maximum intensity (T max ) for three commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) table of maximum intensity (I max ) for three commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) table of duration (T end ) for three commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) table of area under the curve (AUC) for three commercially produced Washington State Chardonnay samples analyzed by a trained panel (n=10) Analysis of variance (ANOVA) of consumer panel (n=60) evaluation of Washington State Chardonnay finish with timed finish perception Pairwise comparison of finish time of Washington State Chardonnays analyzed by a consumer panel (n=60) Mean separation of finish time (seconds) of Washington State Chardonnays analyzed by a consumer panel (n=60) Analysis of variance (ANOVA) of consumer panel (n=60) evaluation of Washington State Chardonnay finish with a 7-point hedonic scale Mean separation of 7-point hedonic scale rating of Washington State Chardonnays analyzed by a consumer panel (n=60) Coding of branched willingness to purchase responses determined by the consumers (n=60) for the evaluation of the Washington State Chardonnay xiii

14 48. Pairwise comparison of wines for branch willingness to purchase response to Washington State Chardonnays by consumer panel (n=60) xiv

15 LIST OF FIGURES Page 1. Structure of coconut, floral, fruity, and mushroom compounds utilized in the trained panel Representative trained panelist time intensity evaluation of single flavors in model wine Pearson s principle component (PC) analysis factor loadings and factor scores from normalized time intensity data of single compounds in model wine evaluation by a trained panel (n=10) Comparison of standard data and normalized data from a representative trained panelist s time intensity evaluation of the coconut flavor in single compound model wine Comparison of standard data and normalized data from a representative trained panelist s time intensity evaluation of the floral flavor in single compound model wine Comparison of standard data and normalized data from a representative trained panelist s time intensity evaluation of the fruity flavor in single compound model wine Comparison of standard data and normalized data from a representative trained panelist s time intensity evaluation of the mushroom flavor in single compound model wine Pearson s principle component analysis factor loadings and factor scores from normalized time intensity data of two compounds in model wine evaluation by a trained panel (n=10) Pearson s principle component analysis factor loadings and factor scores from normalized time intensity data of four compounds in model wine evaluation by a trained panel (n=10) Pearson s principle component analysis factor loadings and factor scores from normalized time intensity data of single compounds in base wine evaluation by a trained panel (n=10) xv

16 11. Pearson s principle component analysis factor loadings and factor scores from normalized time intensity data of two compounds in base wine evaluation by a trained panel (n=10) Pearson s principle component analysis factor loadings and factor scores from normalized time intensity data of four compounds in base wine evaluation by a trained panel (n=10) Standard curve for cis-oak lactone in model wine (9% EtOH, 0.6% fructose, ph 3.0) with three replicates Standard curve for trans-oak lactone in model wine (9% EtOH, 0.6% fructose, ph 3.0) with three replicates xvi

17 CHAPTER 1 INTRODUCTION Wine production represents a burgeoning industry in Washington State. Quality can be influential in determining the economic success of a wine and the winery, many of which are small businesses. Characteristic of high quality wine, wine finish refers to the lingering afterflavor that occurs once wine has been swallowed or expectorated (Jackson 2000). Wine finish is influenced by flavor, which is an important parameter of a wine sensory profile that can dictate wine quality and/or consumer acceptance, thereby making wine flavor a topic of great interest to researchers. Despite interest in wine flavor, wine finish has remained a largely unstudied topic. The objectives of this study were to determine the impact of flavor compounds on white wine finish. Evaluation of white wine was chosen over red wine. Although red wines typically are associated with long finish, they are also associated with astringency. Because astringency was not a sensory property of interest in the present study, the presence of this attribute could be distracting to the panelist. This distraction could result in what is known as attribute dumping (Lawless and Heymann 1998). Dumping occurs when an obvious trait is not evaluated panelists attempt to evaluate the conspicuous trait by dumping their thoughts and perceptions onto another trait (Lawless and Heymann 1998). As this can cause misleading results, it was preferable to utilize white wine for evaluation as it lacks the astringency of red wine that could cause the dumping phenomenon. In addition, astringency is known to be a challenging and fatiguing sensory attribute to evaluate thus by excluding this from the study, the panelists were able to focus on wine flavor. In this study, a trained and consumer panel evaluated white wine finish. The objective of the trained sensory panel was to perform a time intensity analysis of the finish of selected white wine flavors in model wine and base wine to determine how different flavor compounds affected 1

18 and contributed to finish perception. These trained panelists evaluated ethyl hexanoate (fruity flavor), linalool (floral flavor), oak lactone (coconut), and 1-octen-3-ol (mushroom). A secondary objective of this panel was to determine the impact of presenting multiple flavor compounds to panelists. It was hypothesized that compounds associated with fruit and floral notes would finish earlier than compounds associated with coconut and mushroom notes. In addition to a trained panel where panelists performed a time intensity analysis of four distinct flavors, a consumer panel for the evaluation of overall finish was performed. The consumer sensory panel evaluated commercial Chardonnay wines that were subjected to different oaking regimes, corresponding to no oak, medium and high oak wines. Consumers evaluated the length of finish, liking of finish, and their willingness to purchase the wine. It was hypothesized that consumers would be willing to pay higher dollar amounts for wines with a longer finish. Concurrent with the consumer panel, gas chromatography-mass spectrometry (GC-MS) analysis of the wines was performed to quantify the concentration of oak lactone in the Chardonnay wine. It was hypothesized that no oak, medium and high oak Chardonnay wines would contain increasing concentrations of oak lactone. This study of white wine finish yielded an idea of what flavors contribute to a lengthening of wine finish perception. Since extended finish is an attribute that has been associated with high quality wines, this study will allow wine producers to evaluate the presence and methods to generate these flavors in their wines. By altering wine quality perception by lengthening finish, economic success may be increased 2

19 CHAPTER 2 LITERATURE REVIEW Wine Finish Wine finish is defined by Jackson as the lingering taste following the swallowing of wine (Jackson 2000). Lengthy finish is most notable in fine red wines but is also observed in Sauvignon Blanc and Chardonnay white wine varieties (Amerine and Rossler 1983). Typically, it is believed that certain flavors are associated with different lengths of finish. Fruity and floral flavors are thought to have a shorter finish while oak, spice, and earthy flavors are thought to have a longer finish. However, this is a relatively unexplored area and commonly accepted theory is based largely upon conventional wisdom rather than scientific data. Finish is an attribute closely related to quality. Longer finish is associated with higher quality wines (Jackson 2002). High quality dictates the selling price and cache associated with a particular wine. Studying the finish of different white wine flavors could indicate what flavors are important determinants of perceived wine quality, and can therefore be related to economic relevance. Economic Relevance The wine industry represents an important economic entity to many countries around the world (Washington Wine Commission 2007). Historically, European countries such as France and Germany have been major producers of white wine (Jackson 2000). But, in recent years, there has been an increase in the number of countries attempting to create successful wine industries. Wine production in Washington State has grown into an important economic enterprise. The Washington Wine Commission reports that 12 million cases of wine are produced annually in Washington State. This contributes approximately three billion dollars to 3

20 the Washington State economy and approximately 4.7 billion dollars to the national economy (Washington Wine Commission 2007). A significant portion of grapes grown in Washington State are white varietals, with Riesling, Chardonnay, and Pinot Gris grapes ranking as the top three (Washington Wine Commission 2007). Due to the economic importance of white wine production within Washington State, it has become important to study factors that alter white wine quality.. However, determining what factors affect white wine quality can be a difficult matter as wine is a complex solution made up of non-volatile and volatile components (Jackson 2000). In order to understand how these elements of physical composition arise, wine processing procedures must be observed. Wine Processing The processing of grapes into a finished wine can have a distinct effect on white wine flavor. The process begins by harvesting the grapes, either by hand or machine. Grapes are prepared for crushing by removing the stems, which are a source of unwanted phenols. Grapes are then crushed to form a must, a mixture of grape juice, skins, and seeds. Crushed grapes inadvertently go through maceration. This is not always considered a desirable process for white grapes, as it can lead to the collection of phenolic compounds in certain varietals (Jackson 2000). In the context of grape must, phenols can lead to oxidative browning (Servili and others 2000). However, maceration is thought to increase the amount of flavor compounds (Ramey and others 1986). The must is then pressed and clarified, after which producers will make adjustments to the juice composition to ensure optimal fermentation (Jackson 2000). Juice is either allowed to ferment utilizing its naturally present microbial flora, or it is inoculated with a pure and/or 4

21 known strain of yeast to facilitate more successful fermentation results. At this point, certain wine varieties may be stored in oak barrels. Barrel aging of wines is usually reserved for fine red wines and certain white wine varieties such as Chardonnay and some Sauvignon Blanc. After fermentation, modifications are made to the wines including sugar and acidity adjustment. Wine is then stored in either stainless steel or oak barrels for aging before bottling (Jackson 2000). Storage in oak barrels provides the wine exposure to flavor compounds that originate in the wood and oxygen, both of which are thought to positively affect the wine (Wilker and Gallander 1988). Wine processing measures lead to the creation of the wine matrix which consists of a combination of non-volatile and volatile components. Non-volatile Components of Wine Non-volatile wine components include sugars and acids which contribute to sweet and sour tastes, respectively. The tastes created by the presence and proportion of these sugars and acids have been described as the dominant contributors to white wine flavor and also a major influence on the perception of wine quality (Zamora and others 2006). A study by Zamora and others in 2006 discussed the importance of sweet and sour taste perception in white wine. In this study, taste perception in solutions with tartaric acid to a ph of 3.0 and 3.8 and fructose at 11.1 mm and 38.9 mm were evaluated by panelists. The study found that tartaric acid acted as a suppressor of fructose. Fructose also suppressed tartaric acid, but not as effectively. So, fructose to tartaric acid ratio effects the perception of sweet and sour tastes (Zamora and others 2006). Grape harvest is often timed in order to pick grapes when they have reached a desirable balance between acid and sugar concentration (Lund and Bohlmann 2006). Acidity levels are higher before grape maturation due to the development and storage of acids in storage vacuoles within the grape mesocarp. As the grape matures, sugars such as glucose and fructose become more 5

22 abundant, while malic acids are utilized in metabolic processes (Combe 1992). Therefore, appropriate timing of harvest can be highly influential in wine flavor and quality perception. Polyphenolic compounds are also constituents of the non-volatile fraction of wine and include compounds such as tannins and catechins, which contribute to astringent and bitter sensations, respectively (Arnold and others 1980, Robichaud and Noble 1990). Research has shown that perception of white wine quality is negatively influenced by increasing concentrations of phenolic compounds due to significant increases in perceived astringency (Arnold and Noble 1978). A 1978 study by Arnold and Noble showed that panelists found significant differences when evaluating 25, 80 and 130 mg/l phenolic extract in model wine. Perception of astringency increased significantly as phenolic concentration increased (Arnold and Noble 1978). Although tannins and catechins are more typically characteristic of red wines, flavonoid tannins have been noted to contribute to bitterness in Riesling wine varieties. A 2008 study by Etaio and others demonstrated that wine produced by cofermentation of Viura grapes (a white variety) with Tempranillo grapes (a red variety) displayed decreased overall polyphenol content of 6 abs 280 and tannin concentration of 0.23 g/l when compared to Tempranillo wine alone (Etaio and others 2008). Water, glycerol, and nonflavonoid phenols are also considered non-volatile constituents of wine (Jackson 2000). Volatile Components in Wine Volatile compounds are numerous in white wine and other alcoholic beverages. According to a 2001 review by Ebeler, over1300 compounds have been successfully dectected in wine, beer, malt beverages, brandy, and distilled spirits. Major categories of flavor compounds include terpenes, phenols, ethyl esters, acetate esters and lactones (Ebeler 2001). Different compounds may be responsible for different perceived flavors. For example, monoterpenes are 6

23 associated with floral notes (Marais 1983) whereas oak lactones are associated with coconut notes (Waterhouse and Towey 1994). In a 1997 study by Guth, 44 odor active compounds were quantified in Scheurebe and Gewürztraminer white wine varieties (Guth 1997). A 2003 study of Chardonnay wines utilizing gas chromatography-olfactometry found 81 odor active volatile compounds and identified 61 of these compounds (Lee and Noble 2003). Both studies examined odor active compounds, meaning compounds that contribute to aroma. A compound that is not odor active can be present but may not create noticeable impact on wine aroma. Alcohols also represent a major volatile element present in wines. Ethanol is the main alcohol present in wines, and is the product of yeast fermentation (Ebeler 2001). Ethanol plays a number of important sensory roles in wine. It is responsible for burning sensations and also at appropriate concentrations, sensations of sweetness. A 2006 study by Zamora and others showed that increasing ethanol percentage from 4% to 12% led to larger sweetness intensity ratings when panelists were evaluating model wine solutions (Zamora and others 2006). Other alcohols are also present in wines including fusel alcohols (i.e. hexanol and 1-octen-3-ol) and methanol (Jackson 2000). Although there are numerous volatile compound found within the wine matrix, four compounds were chosen for analysis by trained panelists in the present study. Volatiles to be Analyzed by a Trained Panel Compounds were chosen for this study based on a variety of factors. Choices were based on the Wine Aroma Wheel published by Noble and others in The Wine Aroma Wheel categorizes aroma notes from general to specific terms, in order to provide a lexicon of terminology with which wine aroma can be described (Noble and others 1987). The objective was to utilize four compounds that represented four distinct areas of the Aroma Wheel. With 7

24 this in mind, a representative compound was found for floral, fruity, mushroom, and oak. Compounds were also determined based on cited odor activity or presence in at least one white wine variety. Molecular weight was taken into account to avoid the usage of extremely volatile compounds that would be inappropriate for sensory evaluation. Floral Linalool (Figure 1a) is a volatile terpene (Clarke and Bakker 2004) associated with green, floral and citrus notes (Burdock 20009). With a reported taste threshold of 5 ppm (Burdock 2010), linalool was chosen due to notable floral qualities. The compound is found in numerous white grape varieties, but present at highest quantities in Gewürztraminer ( ppm), Sheurebe ( ppm), and Morio-Muscat ( ppm) cultivars (Schreier and others 1977). Linalool has also been reported in Chardonnay, but at a lower concentration (0.1 ppm respectively) (Aldave and other 1993). Linalool can be available in different forms within a grape, some of which are non volatile (Ebeler 2001). These forms include the free, or volatile, linalool, oxidized linalool, and glycosidically bound linalool (Wilson and others 1986). A 1986 study by Wilson and others found that free linalool was present in equivalent amounts in the skin and juice of Muscat grape varieties, while a lower concentration of free linalool was found in the grape pulp. These findings were contrary to the popular theory that terpenes were generally a constituent of the grape skin (Wilson and others 1986). Research has also been performed to determine the impact of processing conditions and different levels of grape maturation on linalool concentration. Marais and van Wyk demonstrated that maturation caused significant increases in linalool concentration in Riesling juice with concentrations increasing from 0.49 to 5.35 µg/l. While studying four maturation 8

25 Figure 1. Compound structures for a) linalool, b) ethyl hexanoate c) 1-octen-3-ol and d) oak lactone a) b) c) d) 9

26 levels, which corresponded to 16.1, 18.8, 18.7, and 19.7 Brix, continuous increases in linalool were seen in grapes in the first three levels ( Brix), after which decline to 0.35 µg/l was noted. Since continuous increases in linalool concentration were observed during grape maturation, the researchers postulated that results were caused by the occurrence of hydrolytic or enzymatic reactions. These reactions may have either released bound linalool or converted precursor compounds into linalool during processing (Marais and van Wyk 1986). Fruity Ethyl hexanoate (Figure 1b) is an ethyl ester that is associated with fruity (Burdock 2009) and apple (Genovese and others 2007) notes. With a taste threshold of 10 ppm, ethyl hexanoate was chosen due to its high odor activity in numerous white wine varieties. Odor activity has been documented in Gewürztraminer and Scheurebe (Guth 1997), Fiano (Genovese and other 2007), and Chardonnay wines (Lee and Noble 2003). Ethyl esters are formed as a result of fermentation (Bardi and others 1998). During fermentation, fatty acids are released and are esterified with alcohols (Nordstom 1964). Synthesis of ethyl hexanoate is influenced by fermentation conditions (Nordstom 1964). Anaerobic conditions resulted in the formation of 5 fold higher concentrations of ethyl hexanoate than semi-anaerobic conditions (Bertrand 1968). These results were strain dependent to S. oviformis and S. ellipsoideus. Also, utilizing strains of S. cerevisiae resulted in higher levels of ethyl hexanoate than in fermentations utilizing S.uvarum yeast strains (Bertrand 1968). Mushroom 1-octen-3-ol (Figure 1c) is an alcohol associated with mushroom notes with a recognition threshold of 10 ppm (Burdock 2009). Linked with fungal growth on grapes, this compound was chosen due to its distinct mushroom aroma and flavor (La Guerche and others 2006). A

27 study by La Guerche and others examined the mushroom and earthy compounds found in grapes with fungal growth. Although typically associated with Botrytis cinerea growth, 1-octen-3-ol is also produced by four other types of fungi. Quantification by gas chromatography-mass spectrometry (GC-MS) revealed the presence of 1-octen-3-ol in Gamay and Semillion grape varieties, while gas chromatography-olfactometry (GC-O) analysis indicated that 1-octen-3-ol was an odor active compound with mushroom aroma. Sensory testing determined that 1-octen- 3-ol had a low sensory odor perception threshold in three different media (water: 2 µg/l, model wine 20 µg/l, red wine 40 µg/l). Researchers noted this as an important factor, as low concentrations could lead to noticeable impact on wine. The study found that 1-octen-3-ol is also able to withstand fermentation with minimal degradation and/or decreases in concentration (La Guerche and other 2006). In additional to being a result of fungal growth on grapes, presence of 1-octen-3-ol can also be attributed to cork taint (Ezquerro and Tena 2005). Cork taint is a wine fault that negatively impacts wine odor (Jackson 2000). While cork taint is typically associated with 2,4,6 trichloroanisole, a 2005 study by Ezquerro and Tena determined that 1-octen-3-ol is also a contributing compound to this wine fault (Ezquerro and Tena 2005). Barrel aged/coconut Oak lactone is an important volatile compound present in wines aged in oak barrels, with characteristics of vanilla, wood, and coconut (Burdock 2009). Lactones are classified as cyclized esters, and have different flavor qualities based on isomerization and carbon chain length (de Mann 1999). Also known by the names whiskey lactone, 4-hydroxy-3-methyloctanoic acid lactone, and 2(3H)-furanone, oak lactone has a reported taste threshold of 0.5 ppm (Burdock 2009). 11

28 There are four isomers of oak lactone (Figure 1d). These forms include naturally occurring (4S, 5S) cis-oak lactone and (4S, 5R) trans-oak lactone, and non-naturally occurring (4R, 5R) cis-oak lactone and (4R, 5S) trans-oak lactone (Brown and others 2006). A sensory analysis of all four oak lactone isomers was performed by Brown and others in 2006 to determine if sensory differences in aroma detection exist between the four isomers of oaklactone. Aroma detection threshold tests were performed by untrained panelists (n=33 to 43) using red and white wine as the base for presenting the four isomers of oak lactone. In both red and white wine, aroma detection thresholds were lower in naturally occurring cis-oak lactone than in naturally occurring trans-oak lactone. Duo-trio tests were performed by untrained panelists (n=36) to evaluate if there was a perceivable difference in aroma between oak lactone enantiomers in wine. There was not a significant difference between naturally occurring and non-naturally occurring trans-oak lactone enantiomers, but significant differences were observed between naturally occurring and non-naturally occurring cis-oak lactone enantiomers. Duo-trio testing comparing wines with naturally occurring cis-oak lactone to wines with naturally occurring cis and trans- oak lactone (150 µg/l in white wine, 300 µg/l in red wine for all isomers) did not yield significant results. The study concluded that cis-oak lactone has a greater impact on wine aroma than the trans-oak lactone in red and white wine (Brown and other 2006). Model Wine Model wines act as a simplified wine matrix, containing only a limited number of components. In the present study, trained panelists evaluated a model wine consisting of water, ethanol, tartaric acid, and fructose. Tartaric acid and fructose were chosen based on their natural presence in wine (Jackson 2000). Examples of model wine usage include the previously cited study by Zamora and others in 2006, where sweet and sour perception was evaluated in the 12

29 presence of differing levels of ethanol. Model solutions contain 2, 4, or 12% ethanol combined with 11.1 mm or 38.9 mm fructose, 3.0 or 3.8 ph (modified by tartaric acid) (Zamora and others 2006). A 1999 study by Kadim and Mannheim utilized model wine with 12% ethanol, potassium hydrogen tartrate, and tartaric acid to 3.2 ph to observe the kinetics of phenolic extraction from oak during the aging process. For the purpose of the current study, model wine enabled panelists to isolate specific volatile compounds and determine their sensory effects. Flavor Perception Perception of flavor (i.e. strawberry, peach, mushroom) is often confused with taste. Taste includes perception of bitter, salty, sweet, sour, and umami sensations. These perceptions occur due to the presence of buds which line the tongue, epiglottis, and soft palate (Jackson 2002). Taste buds are receptors and their stimulation leads to signaling which travels to the thalamus and cortex resulting in the sensation of taste (Jackson 2002). Flavor is considered the result of retronasal stimulation (Meilgaard and others 2007). This occurs when volatile compounds in the mouth and/or upper digestive tract are able to stimulate receptors in the nasal passage. The stimulation of nasal receptors elicits the transmission of a signal to the brain, specifically thought to be the orbitofrontal cortex, resulting in the perception of flavor (Jackson 2002). Some researchers feel that flavor should be defined as any perception originating in the mouth (Meilgaard and others 2007). Meilgaard and others state that this would mean that taste and flavor are combined under the same descriptor. Still, it is important to recognize that different pathways are involved in eliciting these sensations. The focus of the current study will be on flavor finish and not taste or mouthfeel finish. Panelists will be required to separate flavor from taste and mouthfeel sensation when performing time intensity analysis. 13

30 Time Intensity Time intensity is a method of sensory evaluation that allows the panelist to evaluate the intensity of an attribute over a period of time. This method has been used to measure flavor changes in chewing gum (Ovejero-Lopez and others 2005), evaluate astringency of alcoholic beverages (Valentova and others 2002), and analyze differences in sweeteners (Bonnans and Noble 1993). In wine, studies have been performed to analyze bitter and astringent sensations caused by phenols (Robichaud and Noble 1990), astringency and sweetness perception/interaction (Ishikawa and Noble 1995) and effectiveness of different palate cleansers (Ross and others 2007). The time period can be either fixed or unlimited based on the purpose of the study and/or the discretion of the researcher. The data collected over a time period can then be evaluated based on summary statistics which include T max, I max, duration (T end ), and area under the curve (AUC). T max indicates the time it takes to reach maximum intensity, whereas I max indicates the maximum intensity based on a scale of 0-100% low to high intensity. Duration indicates the length of time a panelists takes to return to 0% intensity. Area under the curve (AUC) is an integration which has a value that s interpretation can differ depending upon study objectives (Meilgaard and others 2007). Values for increasing and decreasing inclines and their corresponding areas are also generated. This is typically used as a computer based method and the test requires that panelists be focused, understand the attribute in question, and understand how to utilize the computer program. As a result of these requirements and the relative complexity of time intensity tests, it is utilized in trained panels. Due to the multitude of data points generated by the panelist, there is variation even among trained panelists. Therefore, much discussion and research has been devoted to 14

31 determining the best method of analysis. The most basic analysis requires that the summary statistics listed above be compared using analysis of variance (ANOVA) to determine significant differences (MacFie and Liu 1992). However, in a 1992 paper, researchers MacFie and Liu asserted that this method is insufficient as it does not take information from the entire curve. MacFie and Liu proposed a normalization method that averages curves generated by all panelists for a particular sample. Normalization occurs for both intensity and time as follows: Intensity normalization: Time normalization: (MacFie and Liu 1992) The intensity normalization equation compares the mean I max value (I max ) to the individual I max value (I max i ). The time normalization also compares mean time values (t max, t end, t dec ) to individual time values (t max i, t end i, t dec i ), but is more complicated as there are separate equations to analyze the ascending (to T max ) and descending (from T max ) portions of the curve. MacFie and Liu state that this method is most appropriate because it normalizes both variables and preserves qualities of the curve created by the original data (MacFie and Liu 1992). Principle Component Analysis (PCA) is also suggested by MacFie and Liu as a potential method for analyzing data, particularly if great differences are seen in the data generated by the panelists. PCA focuses on grouping of similar attributes and thus may aid in identifying similarities when results seem quite dissimilar (MacFie and Liu 1992). 15

32 Time intensity studies have previously been performed with different types of alcoholic beverages. A 2000 study by Piggott and others demonstrated the utilization of time intensity for Scotch malt whisky. The objective of this study was to determine if time intensity could be used as a viable testing method and determine the effect of barrel aging on a Scotch malt whisky attribute. Five different barrel types were utilized and whisky was aged for 24, 30, 42, and 60 months. Panelists (n=13) evaluated the time intensity of sweetness over a 60 second time period that consisted of a ten second in mouth holding time followed by swallowing and aftertaste perception. Based on the data collected in this experiment, the researchers performed a comparison of different data analysis methods. The researchers determined that no method of analysis produced statistically significant results, which they felt was due to the extreme variation observed between panelists. Utilization of the 1992 MacFie and Liu method previously described provided what Piggott and others considered the most reliable results (Piggott and others 2000). Several studies have evaluated flavor perception over time with time intensity testing. A 1997 study by Mialon and Ebeler examined perception of vanillin and limonene compounds in emulsions of varied oil to water ratios. Panelists evaluated flavor intensity for both in mouth intensity and post expectoration intensity. Salivary flow was measured for each panelist, as it is thought that factors that alter environment of the mouth may influence retronasal perception (Mialon and Ebeler 1997). The two compounds did not display similar patterns of change as the oil to water ratio in which the compounds were presented in was changed. Most notable, duration of vanillin perception was heightened as oil content was raised. Significant differences were observed when comparing duration of vanillin perception in the presence of 0% oil and 50% oil. Researchers felt this change may have been influenced by the salivary flow of 16

33 individual panelists. Limonene was perceived as less intense by panelists when oil content was increased in the emulsion. The researchers attributed this to the nonpolar nature of the compound (Mialon and Ebeler 1997). Time intensity studies provide unique methodology to evaluate flavor perception. In the current study, flavor perception based on effects of barrel aging procedures was be analyzed. Barrel Aging of White Wines Numerous studies have been completed regarding barrel aging of both red and white wine. This is a topic of interest due to the unique flavor and aroma qualities oak barrels can impart to finished wines. Barrel aging is related to the introduction/production of at least ten volatile compounds. These compounds include furfural, eugenol, guaiacol, and oak lactone (Towey and Waterhouse ). Additionally, profiling of flavor compounds derived from oak barrels can give some indication of the origin and seasoning/treatment of the wood utilized during aging. Since oak lactone has been determined to have sensory impact on white wine, it is important to determine factors that can lead to the increase or decrease of oak lactone throughout the barrel aging process. A 1996 Towey and Waterhouse study examined the effect of the age of the barrel on the volatile extraction and subsequent volatile composition of Chardonnay wines aged in American, French, and Hungarian oak barrels. The researchers examined numerous volatile compounds, including oak lactone. Oak lactone levels were highest in wines aged in one year old barrels. Also, oak lactone were found at higher concentration in wines aged in new barrels than in wines aged in barrels that were two years of age. Origin of oak also affected the levels of oak lactone. New American and Hungarian oak barrels were found to lead to the 17

34 extraction of a lower concentration of volatiles than new French oak barrels (Towey and Waterhouse 1996). In an earlier Waterhouse and Towey study performed in 1994, GC was utilized to analyze Chardonnay aged in American oak barrels and Chardonnay aged in French oak barrels. The objective of this study was to determine whether or not ratios of cis and trans- oak lactone could be utilized as markers of wood origin. The researchers stated that the ability to distinguish between American and French oak had economic significance, as French oak was a more expensive product. Measurements of alcohol content, ph, and titratable acidity did not differ significantly between Chardonnays aged in American oak versus Chardonnays aged in French oak. However, differences were observed when comparing levels of oak lactone found in American and French oak aged wines. American oak aged Chardonnay typically had greater levels of oak lactone, but researchers saw discrepancies among the data, with oak originating in Oregon producing the lowest oak lactone levels of all oaks. Discrepancies in oak lactone levels observed among wines exposed to American and French oak were not consistently observed. Therefore, researchers deemed that this was not an appropriate parameter by which to distinguish American and French oaks. Significant and consistent discrepancies were noted between American and French ratios of cis/trans oak lactone. American oak had significantly higher cis/trans oak lactone ratios (mean=6) than French oaks (mean=1.3). It was concluded that analysis of cis/trans oak lactone ratios via GC was a viable method for determining wood origin of barrels (Waterhouse and Towey 1994). The oak barrel aging of wines impacts sensory characteristics by intensifying aroma notes such as vanilla and oak. Oak extracts are thought to play a role in the intensification of wine aromas. The objective of a 1992 study by Francis and others was to determine if wood origin 18

35 and processing contributed to the sensory qualities of oak wood extracts presented in a model wine. Oak wood was collected from three French forests and one American forest. Three seasoning treatments were evaluated: green, seasoned in the country of origin, and seasoned in Australia. The oak was then either heat treated or not heat treated. Quantitative difference testing was performed to analyze the aroma of the oak extracts. Country of origin was a significant factor, with oak aromas in French oak being comparable or significantly more intense than oak aromas in American oak. Green versus seasoned treatments were significantly different for the aroma intensity of cedar, nutty, and raisin notes. Significant differences were also found when comparing country of seasoning. There was a significant difference between the unheated and heated treatments for the intensity of caramel, cedar, nutty, and raisin aromas. Oak origin, seasoning, and heat treating created significant differences in aroma perception of oak extracts. These differences can be observed by utilizing GC-MS. Gas Chromatography-Mass Spectrometry Analysis of Oak Lactone A method optimization for quantification of barrel aging compounds in wine using solidphase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS) was performed in a 2006 study by Carrillo and others. In this optimization, researchers altered numerous factors and utilized model systems and red wine. The researchers explored the use of four different types of SPME fibers, with divinylbenzene carboxen polydimethylsiloxane (DVB CAR PDMS) determined to be of greatest use in the procedure. Optimal sodium chloride content in headspace SPME vials was determine to be 30% (w/v) of the solution. Finally extraction temperature and time were found to be significant factors when optimizing the quantification of barrel aging compounds. Long time and high temperature were necessary to elicit well-resolved peaks. The researchers recommended a 60 minute extraction carried out at 19