Characterization of phenolics and VOCs in wines obtained from Malbec vineyards of the Uco Valley submitted to high-altitude solar UV-B and water restriction

This experiment tries to characterize the role of soil, climate and harvest date on the composition of grape-derivated thiols, 3-mercapto-hexanol (3MH) and 3-mercapto-hexile acetate (A3MH), in the white wines from Colombard varieties in Gascony (South-West of France). A network of 6 plots has been observed since 1999 on different pedologic units. The plots have common agronomical characteristics, plantation spacing (2,900 to 3,500 vines per ha), plantation aging (1985-1990), strength conferred by rootstock (SO4, RSB), soil management (grass covered 1 by 2) and training system (vertical shoot positionning pruned in single Guyot). Meteorological stations are located near the plots.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions. 

It is well known that free varietal thiols, in particular 3-mercaptohexanol (3MH) and 3-mercaptohexyl ace-tate (3MHA), are important constituents to the aroma of New Zealand Sauvignon blanc wines.

Understanding how different pedoclimatic conditions interact with vine and berry physiology, and subsequently impact wine quality, is paramount for an good valorization of viticultural terroirs and can help to optimize mitigation strategies in the face of global warming

Each grape cultivar is composed of a population of individuals that are genetically different. Clonal selection has allowed the purification and improvement of the global quality