foods Article 13C NMR-Based Chemical Fingerprint for the Varietal and Geographical Discrimination of Wines Alberto Mannu 1,* , Ioannis K. Karabagias 2,* , Maria Enrica Di Pietro 3 , Salvatore Baldino 1 , Vassilios K. Karabagias 2 and Anastasia V. Badeka 2 1 Department of Chemistry, University of Turin, Via Pietro Giuria, 7, I-10125 Turin, Italy;
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[email protected] (V.K.K.);
[email protected] (A.V.B.) 3 Department of Chemistry, Materials and Chemical Engineering “G. Natta”, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy;
[email protected] * Correspondence:
[email protected] (A.M.);
[email protected] (I.K.K.) Received: 6 July 2020; Accepted: 20 July 2020; Published: 2 August 2020 Abstract: A fast, economic, and eco-friendly methodology for the wine variety and geographical origin differentiation using 13C nuclear magnetic resonance (NMR) data in combination with machine learning was developed. Wine samples of different grape varieties cultivated in different regions in Greece were subjected to 13C NMR analysis. The relative integrals of the 13C spectral window were processed and extracted to build a chemical fingerprint for the characterization of each specific wine variety, and then subjected to factor analysis, multivariate analysis of variance, and k-nearest neighbors analysis. The statistical analysis results showed that the 13C NMR fingerprint could be used as a rapid and accurate indicator of the wine variety differentiation. An almost perfect classification rate based on training (99.8%) and holdout methods (99.9%) was obtained.