Victoria Lizama

Application of Multivariate Regression Methods to Predict Sensory Quality of Red Wines

Several multivariate methods including partial least squares (PLS) regression, principal component regression (PCR) or multiple linear regression (MLR) have been applied to predict wine quality, based on the definition of chemical and phenolic parameters of grapes and wines harvested at different ripening levels. Three different models including grape phenolic maturity parameters (grape), wine phenolic parameters (wine) and a combination of grape and wine phenolic parameters (grape + wine) were analysed for each of the wine sensory attributes. The grape parameter model has been presented as the best test to predict wine quality based on sensory scores. On the other hand, wine models showed lower accuracy. The combination of grape and wine parameters presented intermediate results showing sometimes good predictability. Moreover, PLS and PCR appeared as more accurate multivariate methods compared to MLR. Although MLR showed higher correlation coefficients, lower RPD values were observed, displaying thus its lower prediction accuracy. Multivariate calibration statistics appeared as a promising tool to predict wine sensory quality in an easy and inexpensive way.

Nota. Diferenciación de vinos tintos varietales de la Comunidad Valenciana a partir del contenido de alcoholes y polioles / Note. Differentiation of varietal red wines from Communidad Valenciana (Spain) based on their composition in terms of alcohols and polyols:

The composition in terms of alcohols and polyols of 44 and 49 red wines (harvested in 1994 and 1995, respectively) produced in Comunidad Valenciana (Spain) from the grape varieties Cabernet Sauvignon, Tempranillo, Monastrell and Bobal was,analyzed. Discriminant analysis revealed differences among wines, and a clear separation of the samples (90 and 98%) was obtained. The most important compo nents in the differentation of the varieties studied were isoamyl alcohol for Cabernet Sauvignon, cis- 3-hexenol and isobutyl alcohol for Tempranillo, methanol and cis-3-hexenol for Monastrell, and 2,3- butanediol for Bobal. The classification obtained by elimination of the five less significant variables was similar to the one obtained using statistical treatment of all variables (with 84% and 90% in their respective harvests).