Advancing grapevine science through genomic research

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.

Mild to moderate and timely water deficit is desirable in grape production to optimize fruit quality for winemaking. It is crucial to develop robust and rapid approaches to assess grapevine water stress for scheduling deficit irrigation. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are restricted in field applications.

Dans une région comme la Toscane, zone dans laquelle sont produits certains des meilleurs vins italiens et du monde, la province d’Arezzo a actuellement une importance relativement marginale

Following the 2008-2009 reform of the European Union’s common market organisation in wine all protected designations of origin and geographical indications were imposed to prepare a product specification that described the conditions of their use. In this paper, we describe this process and the Hungarian system of geographical indications.

Studying wine metabolome through multiple targeted methods is complicated and limitative; since grapes, yeasts, bacteria, oxygen, enological techniques and wine aging collaborate to deliver one of the richest metabolomic fingerprint.