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.

Grape (Vitis spp.) is one of the most important horticultural crops, cultivated worldwide on more than 7.3 million hectares for various purposes such as winemaking, fresh fruit consumption, rootstock, and ornamental plants. Based on the inter- and intraspecific morphological variability, several descriptor lists, manuals and ampelographic studies are available for identification. Among the organs, leaves have the most traits, while the young shoot, bunch and berry are also important in the characterization of the genotypes. Vitis species and cultivars are described by leaf morphological characterization developed in many ways for the identification of genotypes, to clarify synonymies and distinct clones or evaluate the diversity of wild Vitis taxa. Morphometric—also known as ampelometric—evaluation has an extensive background in the literature. However, for some reasons, only a part of the literature is cited, despite its significant scientific value. In this paper, we summarize the efforts of metric characterization of the grapevine leaf with the introduction of the scientific objectives and reviewing the studies showing the innovations in phenotyping during the past 120 years.

There are hundreds of morphologic and morphometric traits available to classify and identify grapevine (Vitis vinifera L.) genotypes, while statistical evaluation of those has certain limitations, especially when we have no information about the traits that are discriminative to a certain sample set. High numbers of investigated characters could cause redundancy, while reducing those numbers may result in data loss. Grapevine is one of the most important horticultural crops, with many cultivars in production. The characterization of the genotypes is of undeniably high importance. In this study, we analyzed a dataset of scientific and historical importance with 125 morphological traits of 97 grapevine cultivars described by Németh in 1966. However, the traits are not independent in a set of a large number of categorical traits with too few cultivars. Therefore, the number of traits was first reduced using a simple and effective algorithm to eliminate traits with redundant information content using the asymmetric measure of association Goodman and Kruskal’s λ. We reduced the number of traits from 125 to 59 without any information loss. For the classification, we applied a random forest (RF) method. In this way, 93% of the cultivars were correctly classified using only four traits of the data set. To our knowledge, only a few studies applied a trait elimination algorithm similar to ours in ampelography that can be used for other biological data sets of similar structure. The classification results give a morphological explanation to several cultivars from the Carpathian Basin, a territory where all three Vitis vinifera L. geographical groups, occidentalis, orientalis and pontica, are represented. We found that the information-loss-avoiding data reduction method we applied in our study solved the redundancy-caused interdependencies and provided a suitable dataset for classifying grapevine genotypes. For example, this method may successfully be applied in digital image analysis-based traditional morphometric investigations in ampelography.

Leaf chlorophyll content is a key indicator of plant physiological status in viticulture; therefore, regular evaluation to obtain data for nutrient supply and canopy management is of vital importance. The measurement of pigmentation is most frequently carried out with hand-held instruments, destructive off-site spectrophotometry, or remote sensing. Moreover, smartphone-based applications also ensure a promising way to collect colorimetric information that could correlate with pigmentation. In this study, four grapevine genotypes were investigated using smartphone-based RGB (Red, Green, Blue) and CIE-L*a*b* colorimetry and a portable chlorophyll meter. The objective of this study was to evaluate the correlation between leaf chlorophyll concentration and RGB- or CIE-L*a*b*-based color indices. A further aim was to find an appropriate model for discriminating between the genotypes by leaf coloration. For these purposes, fully developed leaves of ‘Chardonnay’, ‘Sauvignon blanc’, and ‘Pinot noir’ clones 666 and 777 were investigated with the Color Grab smartphone application to obtain RGB and CIE-L*a*b* values. Using these color values, chroma, hue, and a further 31 color indices were calculated. Chlorophyll concentrations were determined using an Apogee MC100 device, and the values were correlated with color values and color indices. The results showed that the chlorophyll concentration and color indices significantly differed between the genotypes. According to the results, certain color indices show a different direction in their relationship with leaf pigmentation for different grapevine genotypes. The same index showed a positive correlation for the leaf chlorophyll concentration for one variety and a negative correlation for another, which raises the possibility that the relationship is genotype-specific and not uniform within species. In light of this result, further study of the species specificity of the commonly used vegetation indices is warranted. Support Vector Machine (SVM) analysis of the samples based on color properties showed a 71.63% classification accuracy, proving that coloration is an important ampelographic feature for the identification and assessment of true-to-typeness.

Protein-based clarification agents were tested to clarify Chardonnay grape musts during sedimentation. The experiments were conducted in the Etyek–Buda Wine Region in Northwest Hungary over four vintages between 2020 and 2023. The performance of the treatment agents was influenced by several factors, such as the composition of the grape must (the absolute concentration and the relative ratio of phenolic compounds) which varied with the vintage characteristics, the physiological and phenolic ripeness of the grapes, and the composition of the clarifying agents itself. Recent investigations show that fully ripe fruit juices can be clarified more effectively, and the effectiveness increases when different types of clarification agents are combined with the plant proteins, e.g., PVPP greatly facilitates the removal of phenolic compounds. The tested plant protein-based clarification agents did not influence the YAN source of the grape musts before fermentation. Our investigations proved an effective impact of these preparations even during the first steps of wine technology. Sensory properties and chemical stability are improved by decreasing the polyphenol content before fermentation, and, besides the good technological effects, wines treated with plant protein agents can be included in the vegan diet.

The content of 2,3-butanediol ((R,R) and meso isomers) and 1,2-propanediol in grape berries and “liquid samples” (all non-berry extracts) from the Tokaj wine region of Hungary was investigated. Our aim was to find out how the activity of Botrytis cinerea influences the concentrations of these compounds compared with healthy grapes. Based on the measured concentrations, we can make a distinction between healthy berries and noble, rotted, so-called aszú berries. We also investigated if there is a difference between finished aszú wines and liquids intended for aszú production. We wanted to investigate the amount and distribution of the stereoisomers of 2,3-butanediol and their proportions. The results of the HS-SPME-GC-FID analysis of the samples showed significant differences in the 2,3-butanediol content between healthy and botrytised, aszú berries and between liquid samples for aszú production and aszú wines. In the berry samples, meso-2,3-butanediol could not be detected, whereas in the liquid samples, we found good amounts of this isomer. This may be due to the fact that the appearance of the meso form of 2,3-butanediol is a consequence of alcoholic fermentation. Significant differences were found between wines from healthy grapes and wines from botrytised grapes in terms of the levo-2,3-butanediol content, so that from an analytical point of view, a difference can be made between wines from healthy and botrytised grapes. No significant differences were found between berry and liquid samples in terms of 1,2-propanediol concentrations during our tests.