Polyphenolic compositions of Basque natural ciders: A chemometric study

• Published in: Food chemistry 2006-08, Vol.97 (3), p.438-446
• Main Authors: Alonso-Salces, Rosa M., Herrero, Carlos, Barranco, Alejandro, López-Márquez, Diana M., Berrueta, Luis A., Gallo, Blanca, Vicente, Francisca
• Format: Article
• Language: English
• Subjects: apple cider; apples; Biological and medical sciences; Chemometrics; Cider; classification; expert systems; food analysis; food composition; Food industries; Fundamental and applied biological sciences. Psychology; HPLC; Pattern recognition; Polyphenol; polyphenols; principal component analysis; product authenticity; Thiolysis
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2005.05.022

Polyphenolic compositions of Basque natural ciders were determined by high-performance liquid chromatography, with diode array detection following thiolysis, in order to differentiate ciders according to the geographical origin of the main raw material used for their elaboration. Fifty percent of the apples used for cidermaking in the Basque Country are imported from France or Galicia (N.W. Spain); this gives beverages of different chemical compositions and sensory qualities. A data set, consisting of 64 cider samples and 33 measured variables, was evaluated using multivariate chemometric techniques. A preliminary study of data structure was performed by cluster analysis and principal component analysis. Different classification systems for the two categories were obtained on the basis of the chemical data by applying several supervised pattern recognition procedures, such as linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA), and multilayer feed-forward artificial neural networks (MLF-ANN). KNN, SIMCA and the MLF neural network provided complementary results: KNN allowed the correct classification of almost all the ciders of the Galician category, SIMCA provided a model for the ciders of the French category that excluded all ciders made with Galician apples (50% of raw material), and the neural network achieved a level of hits for the classification of the ciders obtained from French apples (50% of raw material) above 95%. Polyphenolic profiles of the ciders provide enough information to develop classification rules for identifying ciders according to the geographical origin of the raw material used for cidermaking.