Assuring the authenticity of northwest Spain white wine varieties using machine learning techniques

•Application of soft computing techniques for classifying different grape varieties.•Evaluation of discriminatory power for different volatile compounds using classifiers.•Perfect classification accuracy using real data and Random Forest algorithm.•Complementary analysis of model behaviour against loss of information.

Classification of wine represents a multi-criteria decision-making problem characterized by great complexity, non-linearity and lack of objective information regarding the quality of the desired final product. Volatile compounds of wines elaborated from four Galician (NW Spain) autochthonous white Vitis vinifera from four consecutive vintages were analysed by gas chromatography (FID, FPD and MS detectors), and several aroma compounds were used for correctly classifying autochthonous white grape varieties (Albariño, Treixadura, Loureira and Dona Branca). The objective of the work is twofold: to find a classification model able to precisely differentiate between existing grape varieties (i.e. assuring the authenticity), and to assess the discriminatory power of different family compounds over well-known classifiers (i.e. guaranteeing the typicality). From the experiments carried out, and given the fact that Principal Component Analysis (PCA) was not able to accurately separate all the wine varieties, this work investigates the suitability of applying different machine learning (ML) techniques (i.e.: Support Vector Machines, Random Forests, MultiLayer Perceptron, k-Nearest Neighbour and Naïve Bayes) for classification purposes. Perfect classification accuracy is obtained by the Random Forest algorithm, whilst the other alternatives achieved promising results using only part of the available information.

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