Abstract
A study in order to differentiate Tempranillo and Tempranillo blanco grapes and wines from A.O.C. Rioja (Spain) has been carried out. The three most important groups of chemical compounds in grapes and wines were determined: nitrogen and phenolic compounds by HPLC–DAD, and volatile compounds by GC–MS. A machine learning approach was carried out to achieve the most important chemical parameters to differentiate Tempranillo and Tempranillo blanco grapes and wines based on the Kruskal–Wallis test, F-score feature selector, and support vector machine classification. Four importance levels were established for both grape and wine discrimination. The first level is composed of variables that can differentiate Tempranillo and Tempranillo blanco grapes and wines by analyzing the chemical concentrations. The second, third, and fourth level of importance is composed of a variable set that can correctly classify the samples, each one determined according to the F-score importance ranking and the SVM discriminations. Some chemical compounds demonstrated to be good predictors to wine and grape discriminations, whereas some chemicals are only a good predictor to one discrimination approach.
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T. Garde-Cerdán declares that she has no conflict of interest. N. L. da Costa declares that she has no conflict of interest. P. Rubio-Bretón declares that he has no conflict of interest. R. Barbosa declares that he has no conflict of interest. E. Baroja declares that he has no conflict of interest. J. M. Martínez-Vidaurre declares that he has no conflict of interest. S. Marín-San Román declares that he has no conflict of interest. I. Sáenz de Urturi declares that he has no conflict of interest. E. P. Pérez-Álvarez declares that he has no conflict of interest.
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Garde-Cerdán, T., da Costa, N.L., Rubio-Bretón, P. et al. The Most Important Parameters to Differentiate Tempranillo and Tempranillo Blanco Grapes and Wines through Machine Learning. Food Anal. Methods 14, 2221–2236 (2021). https://doi.org/10.1007/s12161-021-02049-6
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DOI: https://doi.org/10.1007/s12161-021-02049-6