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.

The aim of this study was to find out what kind of “Bianca” wine could be produced when using organic yeast, what are the dynamics of the resulting alcoholic fermentation, and whether this method is suitable for industrial production as well. Due to the stricter rules and regulations, as well as the limited amount and selection of the permitted chemicals, resistant, also known as interspecific or innovative grape varieties, can be the ideal basic materials of alternative cultivation technologies. Well-designed analytical and organoleptic results have to provide the scientific background of resistant varieties, as these cultivars and their environmentally friendly cultivation techniques could be the raw materials of the future. The role of the yeast in wine production is crucial. We fermented wines from the “Bianca” juice samples three times where model chemical solutions were applied. In our research, we aimed to find out how organic yeast influenced the biogenic amine formation of three important compounds: histamine, tyramine, and serotonin. The main results of this study showed that all the problematic values (e.g., histamine) were under the critical limit (1 g/L), although the organic samples resulted in a significantly higher level than the control wines. The glycerin content correlated with the literature values, since it is well known that the glycerin-pyruvic acid transformation results in a 6–10 g/L concentration.