Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an innovative method for wine analysis. Every grapevine variety has a unique structural formula, which can be considered as the genetic fingerprint of the plant. This specificity appears in the composition of the final product (wine). In the present study, the originality of Hungarian wines was investigated with 1H NMR-spectroscopy considering 861 wine samples of four varieties (Cabernet Sauvignon, Blaufränkisch, Merlot, and Pinot Noir) that were collected from two wine regions (Villány, Eger) in 2015 and 2016. The aim of our analysis was to classify these varieties and region and to select the most important traits from the observed 22 ones (alcohols, sugars, acids, decomposition products, biogene amines, polyphenols, fermentation compounds, etc.) in order to detect their effect in the identification. From the tested four classification methods—linear discriminant analysis (LDA), neural networks (NN), support vector machines (SVM), and random forest (RF)—the last two were the most successful according to their accuracy. Based on 1000 runs for each, we report the classification results and show that NMR analysis completed with machine learning methods such as SVM or RF might be a successfully applicable approach for wine identification.
Highlights
Publisher’s Note: MDPI stays neutralNuclear magnetic resonance (NMR) spectroscopy is a structure analysis method applied since the 1940s
Based on a thorough study of literature, Amargianitaki et al [22], Masetti et al [23], and recently Kalogiouri and Samanidou [24] introduced and compared several kinds of multivariate analysis methods (PCA, PLS-DA, kNN, neural networks (NN), SIMCA, and support vector machines (SVM)) to investigate their classification success depending on cultivar, vintage, geographical origin, and even seasonality, while considering NMR data
We calculated the variable importance based on the SVM and random forest (RF) model outputs, whereas the varieties Cabernet Sauvignon, Blaufränkisch, Merlot, and Pinot Noir and wine regions Eger and Villány were classified considering their 22 traits of alcohols, sugars, acids, decomposition products, biogene amines, polyphenols, and fermentation compounds based on 861 samples from 2015 and 2016
Summary
Publisher’s Note: MDPI stays neutralNuclear magnetic resonance (NMR) spectroscopy is a structure analysis method applied since the 1940s. The NMR technique provides several advantages, including easy sample preparation and short running time [1]. This is an easy and rapid way to obtain the chemical composition of grapes, grape juice, must, and wine; it is suitable for the identification of small organic compounds (metabolite profiling), such as amino acids [2], organic acids, alcohols [3,4], sugars, and phenolic compounds [5]. The principle behind NMR spectroscopy is that certain nuclei have magnetic moments The practice of this technique applies electromagnetic radiation for the selective excitation of these nuclei with magnetic moments.
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