Application of Fuzzy algorithms in conjunction with 1 H-NMR spectroscopy for alcoholic beverages differentiation.

Abstract

According to recent statistics elaborated at the European Union level, wine represents one of the most falsified commodities. In this context, the development of reliable classification models for alcoholic beverages differentiation requires, besides sensitive analytical tools, the use of the most suitable data processing methods like those based on advanced statistical tools or artificial intelligence. The present study aims to establish a new innovative approach for alcoholic beverages differentiation (wines and fruit distillates) able to increase the discrimination rate of the developed models. Thus, a data dimensionality reduction step, performed on the obtained 1 H-NMR profiles, was applied. This stage consisted in the application of Fuzzy Principal Component Analysis (FPCA) before themodel development through discriminant analysis. The enhancement of the classification potential given by the application of FPCA as compared to Principal Component Analysis (PCA) was discussed. The association between 1 H-NMR spectroscopy and an appropriate statistical approach proved to be a very effective tool for alcoholic beverages differentiation. In order to develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in corroboration with fuzzy algorithms for data dimensionality reduction. The study proved the efficiency of using FPCA scores over those obtained through the widely applied PCA. Based on the proposed approach, it was proved that for wine classification a perfect separation was obtained according to the geographical origin, cultivar and vintage. A percentage of 100% correctly classified samples was also achieved for botanical and geographical differentiation of fruit distillates. This article is protected by copyright. All rights reserved.