Abstract

This study applies multivariate statistical approaches to wine quality evaluation. With 27 red wine samples, four factors were identified out of 12 parameters by principal component analysis, explaining 89.06% of the total variance of data. As iterative weights calculated by the BP neural network revealed little difference from weights determined by information entropy method, the latter was chosen to measure the importance of indicators. Weighted cluster analysis performs well in classifying the sample group further into two sub-clusters. The second cluster of red wine samples, compared with its first, was lighter in color, tasted thinner and had fainter bouquet. Weighted TOPSIS method was used to evaluate the quality of wine in each sub-cluster. With scores obtained, each sub-cluster was divided into three grades. On the whole, the quality of lighter red wine was slightly better than the darker category. This study shows the necessity and usefulness of multivariate statistical techniques in both wine quality evaluation and parameter selection.

Keywords:

BP neural network, information entropy, principal component analysis, weighted cluster analysis, weighted TOPSIS method, wine quality evaluation,

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