Roots and rhizosphere microbiota diversity is influenced by rootstock and scion genotypes: can this be linked to the development of the grafted plant?  

An undesirable note called “floral taint” has been observed in red wines by winemakers in the Niagara region caused by large volumes of frozen leaves and petioles [materials-other-than-grapes (MOG)] introduced during mechanical harvest and subsequent winemaking late in the season. The volatiles, which we hypothesized are responsible, are primarily terpenes, norisoprenoids, and specific esters in frozen leaves and petioles. The purpose of this study was to investigate the volatile compounds which may cause the floral taint problem and explore how much of them (thresholds) may lead to the problem. Also, the glycosidic precursors of some of these compounds were analyzed to see the changes happening during frost events.

We depict the main features of five viticulture agroclimatic indices for 626 wine growing regions within 41 countries.

European regulations describe what elements must be given in the specifications of DPO determination ; mainly production conditions, links between quality and products characteristics and the physical traits of the production area. These elements are given in the “link to terroir” paragraph relating natural and human factors, detailed product characteristics linked to the geographical area and at last interactions between product originality and the geographical area.

Preliminary experiment of harvest timing was carried out in Eger wine district, Hungary in 2009. In situ physiological responses, berry quality parameters and wine quality of the Kékfrankos grapevine were studied at two growing sites (Eger-K6lyuktet6 – non-stressed, flat vineyard, and Eger-Nagyeged hill – water stressed, steep slope vineyard).

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.