Marjorie Dever

Sensory and compositional profiles of British Columbia Chardonnay and Pinot noir wines

Abstract Sensory and compositional analyses were used to profile (i) 1993 Chardonnay wines ( n = 16), (ii) 1988–1993 Chardonnay wines ( n = 25) and (iii) 1992–1993 Pinot noir wines ( n = 14) from British Columbia (B.C.). All wines were analyzed for titratable acidity, pH, absorbance, phenol and alcohol content. In a series of three experiments, 10 judges evaluated colour, aroma and flavour attributes of all wines in duplicate. Principal component analysis of the sensory data for 1993 Chardonnay wines indicated a diversity of styles including wines which were both oaky and buttery to those which were intensely fruity. Wines with oaky character had greater complexity and deeper colour, while those with fruity character were sweeter and more perfumed. Discriminant analysis indicated that, in general, the 1992–1993 vintages were more fruity than the 1988–1991 vintages which were more yellow and had higher oak and diacetyl characters. Discriminant analysis also showed specific winery “styles”. While some wineries produced fruity, floral wines and others produced wines which were more yellow, oaky, buttery and astringent. Principal component analysis of the sensory data for the Pinot noir wines indicated four major styles of B.C. wines: those which were complex with high fruit and berry flavours, those which were intensely red, oaky, astringent and spicy, those which were herbaceous, and those which lacked fruit/berry character, astringency and body.

Development and evaluation of multiple regression models for prediction of sweet cherry liking

Abstract In two experiments, multiple regression models were developed and evaluated to identify the relevant sensory attributes for cherry liking. In Experiment 1, 16 judges evaluated 18 cherry varieties for seven visual characteristics (colour intensity, uniformity-of-colour, speckles, size, stem length, external firmness and ‘visual’ liking) and seven flavour/texture characteristics (flesh firmness, flesh colour intensity, juiciness, sweetness, sourness, flavour intensity and ‘flavour/ texture’ liking). Stepwise multiple regression was used to develop the most appropriate statistical models for prediction of visual and flavour/texture liking based on visual and flavour/texture characteristics, respectively. Both models were simple and easily understandable with two sensory variables. The best model for visual liking required only size and uniformity-of-colour variables; whereas, the best model for flavour/texture liking required sweetness and flavour intensity variables. In Experiment 2, 18 judges evaluated 30 sweet cherry cultivars, using the same methodology, to create a validation data set. Correlation coefficients ( R ) and prediction standard errors (PSEs) between the observed (Experiment 2) and predicted (Experiment 1) liking scores were used to evaluate the prediction equations. The prediction equation for flavour/texture liking was most satisfactory ( R = 0.85, PSE = 0.61). A new equation developed from the validation data confirmed the importance of sweetness and flavour intensity. In contrast, the prediction equation for visual liking was less satisfactory ( R = 0.56) and a new equation developed from the validation data set confirmed only size as an important variable.