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AWRI: Identification Of The Major Drivers Of ‘Phenolic’ Taste In White Wines After tasting and discussing the wines on two separate occasions, the panelists arrived at a set of descriptors that best differentiated the wines on the basis of ‘Grigio’ style as compared with the ‘Gris’ style. The wines were rated in duplicate on the intensity of the selected attributes using a 15 cm partially structured line scale with “low” and “high” word anchors at the 10% and 90% marks. Conformance to the ‘Grigio’ vs. ‘Gris’ style continuum was assessed using a 15 cm unstructured line scale with “Grigio like” and “Gris like” on the left and right extremes respectively. All wines were tasted in a random order at room temperature. Tastings were conducted in individual booths under amber lighting. Statistical Analysis The conformance to Pinot Grigio vs. Pinot Gris style and the intensities of the individual taste and textural characters normally associated with phenolic character in white wine (astringency, oiliness, hotness, bitterness and viscosity) were modelled using Partial Least Squares (PLS) Regression analysis using ethanol concentration, pH, titratable acidity, residual sugar, and A280 and A320 as explanatory variables (the absorbances are proxies for total phenolic content, and total hydroxycinnamic acids respectively). To avoid over-fitting (i.e. including explanatory variables that do not contribute significantly to the explanatory power of the model), best subset regression was also used to model wine style on composition. Specifically, over-fitting was avoided by selecting models that were both closely fit (with a high adjusted R 2 ) and had a Mallows CP coefficient (Mallows, 1973) that was lower than the number of explanatory variables. 4.3.2 Study 2: Effect on Riesling Style and Perceived Quality Wine Samples 24 of the 100 best selling dry white wines in Australia in 2009 were selected by the AWRI’s technical tasting panel on the basis of perceived variation in phenolic taste. Of these, the nine dry Rieslings were used in this study (details are given in Appendix B, Table B-2). The tasters and sensory methods The selected Riesling wines were independently tasted blind in the same order by 20 practicing Australian white winemakers and two researchers employed by Australian wine companies. The tasters scored the wines for quality using the Australian 20 point system. In addition, the winemakers were asked to indicate if the wine displayed a phenolic taste which included bitterness, astringency, viscosity, oiliness, hardness, or an overall ‘phenolic character’. Approximately 30 mL of each wine were tasted under natural light and at room temperature. Statistical Analysis PLS regression was used to model winemaker quality scores of the Riesling wines on the same explanatory variables. Wine score was also correlated with taste attributes. Page | 32
AWRI: Identification Of The Major Drivers Of ‘Phenolic’ Taste In White Wines 4.3.3 Study 3: Effect on Perceived Quality and Consumer Acceptance of Commercial Dry White Wines Wine Samples The 100 best selling dry white wines in Australia in 2009 were screened for phenolic character by the AWRI’s technical tasting panel. Twenty four wines were selected to represent a range of ‘phenolic character’. Seven were unoaked Chardonnay wines, five oaked Chardonnay, ten Rieslings, and two were Pinot Gris wines. Their analyses are given in Appendix B, Table B-2. A further subset of 14 of these wines was selected for a consumer acceptance study. The tasters and sensory methods The consumer panel comprised 203 Sydney consumers who drank white wine at least once per week, and who occasionally purchased $10-20 bottled white wine. Demographically, around half the tasters were aged over 40, half were female, around half were single, and over three quarters had undertaken some form of tertiary study. All preferred consuming white wines to sparkling, rose or red wines. The consumers were asked to taste the 14 wines and rate how much they liked them on a nine point Likert scale (Like extremely, very much, moderately and slightly – neither like nor dislike – dislike slightly, moderately, very much and extremely). 30 mL of the 14 wines were presented in a randomised order under natural lighting and at room temperature. The winemaker panel and the tasting conditions used are the same as those used in Study 2. Statistical Methods Both consumer liking ratings and winemaker scores for all wines were correlated against alcohol concentration, pH, titratable acidity, residual sugar, A280 and A320. Partial Least Squares (PLS) Regression was also used to relate these compositional variables to consumer liking and winemaker quality score. As it was assumed that winemakers applied style definitions when assigning quality scores, both winemaker quality score and liking for wines for a single recognised style (dry Riesling) were also regressed against composition. The proportion of winemakers that indicated that the wine had a phenolic taste was correlated with alcohol, pH, titratable acidity, residual sugar, A280, A320 and average score. Binary logistic regression was applied to model the individual yes/no responses of winemakers to phenolic character with wine composition. Page | 33
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