Multivariate data analysis as a discriminating method of the origin of wines

A data set of 178 wines from Piedmont (Barbera, Grignolino, Barolo) was evaluated by multivariate data analysis in order to both build the category models and single out anomalous samples. By feature selection (Fisher weights) only 8 variables, out of the 28 chemical and physico-chemical original variables of the data set, were selected on account of their high univariate discriminant ability. Classification methods (KNN, LDA, PCA) and modelling techniques (Bayesian analysis, SIMCA) were applied to the 8-dimension data set; classification ability was about 98 %

Characterization of cider by its hydrophobicproteinprofile and foamparameters

This paper describes the characterization of ciders (both “natural” and sparkling cider) from the Principality of Asturias (northwest region of Spain) through the analysis of their protein content, based on their hydrophobic properties, and their foam characteristics. A reversed-phase high performance liquid chromatography (RP-HPLC) was applied to the protein analysis, and the foamparameters were measured with Bikerman’s method. Multivariate techniques allowed the authors to differentiate ciders on the basis of the press and foam taking technologies, and foam sensory quality. Feasible and robust models were constructed for classifying purposes. Higher than 95% correct classifications were obtained for differentiating ciders on the basis of the factors studied (cider making technology and foam sensory quality). The multivariate regression model computed allowed the authors to predict (correlation coefficients higher than 0.8) the foamparameters related to foam stability and bubble average lifetime in “natural” cider