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.
Grapevine (Vitis vinifera L.) leaves show high morphological diversity alongside the shoot. This variability has been investigated in this study to explore the change in leaf size, leaf thickness, stomata density and stomata size among the 1 st , 5 th and 10 th leaves on the main shoots and leaves on the laterals. Results showed that leaf size altered from the basal abaxial leaves to the middle of the shoot, while the laterals had the smallest leaves. Number of stomata also varied significantly regarding the different levels of the canopy. First leaves on the shoots had the least stomata per unit leaf area while this number increased above. In contrast with this the size, i.e. length and width of the stomata did not differ. Leaf thickness was the lowest on the leaves of the lateral shoots, while the values decreased from the 1 st to the 10 th nodes. These results raised the question about the ontogeny and heteroblasty of the grapevine foliage.
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.
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.
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