Apoplastic pH influences Vitis vinifera Barbera recovery responses to short and prolonged drought 

The importance of the non-Saccharomyces yeasts (NSY) in winemaking has been extensively reviewed in the past for their aromatic or bioprotective capacity while, recently their antioxidant/antiradical potential has emerged under winemaking conditions. In the literature the antioxidant potential of NSY was solely explored through their capacity to improve glutathione (GSH) content during alcoholic fermen- tation [1], while more and more studies pointed out the activity of the non-glutathione soluble fraction released by yeasts [2].

Les aires d’appellations de la Côte d’Or résultent d’une sélection humaine empirique, historique et évolutive en adéquation avec les facteurs naturels. Afin de comprendre quels facteurs naturels et humains agissent sur le caractère et l’évolution des terroirs des Côtes de Nuits et de Beaune, une méthodologie de recherche a été développée. Elle s’articule autour de deux axes, la caractérisation physique des lieux-dits viticoles et l’historicité de la qualité de ces lieux-dits. Le travail avec un S.I.G permet d’étudier l’évolution spatiale et temporelle de la qualité.

Red wines constitute the majority of the wines produced in Bordeaux. All along the winemaking process, many microorganisms may develop in wine. A lot of them are useful but a common defect found in wine is linked to the development of Brettanomyces bruxellensis, a yeast that produces volatile phenols. These molecules are responsible for an unwanted sensorial defect described as similar to “horse sweat”, “burnt plastic” or “leather”. It has been shown that while some wines are very permissive and easily contaminated, others are pretty resistant to Brettanomyces development. However, common parameters such as pH, alcohol or sugars composition cannot fully explain the differences observed in wine permissiveness.

The selection of ancient grapevine varieties aims to achieve genetic gains in several important traits that can make the variety more interesting for the objectives of the producers. Traditionally, yield and quality traits of the must have been considered for selection, but many others can be taken into account. Linear mixed models are fitted to the data to predict the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each evaluated trait, which will be the basis for selection.

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.