1 H NMR chemometric models for classification of Czech wine type and variety

• Published in: Food chemistry Vol. 339; p. 127852
• Main Authors: Mascellani, Anna, Hoca, Gokce, Babisz, Marek, Krska, Pavel, Kloucek, Pavel, Havlik, Jaroslav
• Format: Journal Article
• Language: English
• Published: England 01.03.2021
• Subjects: Cluster Analysis; Color; Czech Republic; Discriminant Analysis; Principal Component Analysis; Proton Magnetic Resonance Spectroscopy - methods; Vitis - chemistry; Vitis - metabolism; Wine - analysis; Czech Republic; Wine classification; Fructose (Compound CID: 2723872); 2,3-Butanediol (Compound CID: 262); Trigonellin (Compound CID: 5570); p-Hydroxyphenylacetic acid (Compound CID: 127); Turanose (Compound CID: 5460935); Gallate (Compound CID: 370); Glycerol (Compound CID: 753); Alanine (Compound CID: 5950); Wine analysis; Methanol (Compound CID: 887); Formate (Compound CID: 283); Proline (Compound CID: 145742); Acetate (Compound CID: 175); Isobutanol (PubChem CID: 6560); H NMR; Methionine (Compound CID: 876); Acetoacetate (Compound CID: 6971017); Succinate (Compound CID: 160419); Ethanol (Compound CID: 702); Chlorogenate (Compound CID: 1794427); Lactate (Compound CID: 612); Pyruvate (Compound CID: 107735); Tartrate (Compound CID: 3806114); Phenethyl alcohol (Compound CID: 57361413); Tyrosine (Compound CID: 6057); Uridine (Compound CID: 6029); Choline (Compound CID: 305); Catechin (Compound CID: 9064); Epicatechin (Compound CID: 72276); Glucose (Compound CID: 5793); Acetoin (Compound CID: 179); Phenylalanine (Compound CID: 6140); Glutamate (Compound CID: 33032); Leucine (Compound CID: 6106); Ethyl Acetate (Compound CID: 8857); Histidin (Compound CID: 6274); Myo-Inositol (Compound CID: 892); Isopentanol (Compound CID: 31260); randomForest; Isoleucine (Compound CID: 6306); Chemometrics
• ISSN: 1873-7072

A set of 917 wines of Czech origin were analysed using nuclear magnetic resonance spectroscopy (NMR) with the aim of building and evaluating multivariate statistical models and machine learning methods for the classification of 6 types based on colour and residual sugar content, 13 wine grape varieties and 4 locations based on H NMR spectra. The predictive models afforded greater than 93% correctness for classifying dry and medium dry, medium, and sweet white wines and dry red wines. The trained Random Forest (RF) model classified Pinot noir with 96% correctness, Blaufränkisch 96%, Riesling 92%, Cabernet Sauvignon 77%, Chardonnay 76%, Gewürtztraminer 60%, Hibernal 60%, Grüner Veltliner 52%, Pinot gris 48%, Sauvignon Blanc 45%, and Pálava 40%. Pinot blanc and Chardonnay, varieties that are often mistakenly interchanged, were discriminated with 71% correctness. The findings support chemometrics as a tool for predicting important features in wine, particularly for quality assessment and fraud detection.