1,1,6-Trimethyl-1,2-dihydronaphthalene Content of Riesling Wines in Hungary.

1,1,6-Trimethyl-1,2-dihydronaphthalene (TDN) is known to give a petrol note when smelling and tasting wine, which is most pronounced in Riesling varieties. Its increasing concentration has been linked to climate change. In the present work, a gas chromatographic method was used to quantify free TDN in "Italian Riesling", "Rhine Riesling", and "Kéknyelu" wines from Hungary. From the vintages 2010 to 2020, 39 bottles of wine from different wine regions were evaluated by instrumental analysis and sensory evaluation. Our aim is to determine the extent to which the Riesling wines in Hungary show a petrol character. The other objective was a comparison of sorts to see if there is a difference in TDN production potential between Italian Riesling and Rhine Riesling. We also aimed to clarify the question whether the Hungarian variety Kéknyelu is also capable of developing a petrol character. The wines we tested were corked and screw-locked. This allowed us to compare the difference in TDN concentration variation over time between the two closure methods.

A Review of Ampelometry: Morphometric Characterization of the Grape (Vitis spp.) Leaf.

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.

Morphological Traits Evaluated with Random Forest Method Explains Natural Classification of Grapevine (Vitis vinifera L.) Cultivars.

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.