Chemometric Classification of Basque and French Ciders based on their Total Polyphenolic Content and CIELab parameters

ALONSO-SALCES, R. M.; GUYOT, S.; HERRERO, C.; DRILLEAU, J. F.; BERRUETA, L. A.; GALLO, B.; VICENTE, F.

Food Chemistry

ELSEVIER

Año: 2005 vol. 91 p. 91 - 98

0308-8146

Total polyphenol contents, estimated by Folin–Ciocalteu method, and CIELab chromatic parameters were determined in Basque and French ciders with the aim of developing a classification system to confirm the authenticity of ciders. A preliminary study of data structure was performed by a multivariate data analysis using chemometric techniques such as cluster analysis and principal component analysis. Supervised pattern recognition methods, such as linear discriminant analysis, K-nearest neighbours (KNN), soft independent modelling of class analogy and multilayer feed-forward artificial neural networks (MLF-ANN), provided classification rules for the two categories based on the experimental data. KNN results for Basque ciders afforded an excellent performance in terms of recognition and prediction abilities (99%), providing a useful tool to detect genuine Basque ciders. Despite KNN and MLF-ANN giving the best results for French ciders, with a success rate of prediction ability around 91%, this would not be acceptable for authentication purposes.