Reduced bunch compactness in a clone of Tempranillo associates with a complex reciprocal translocation detected by long-read sequencing genomics

The C-glycosidic ellagitannins constitute a subclass of hydrolyzable tannins of remarkable structural diversity. In this work we first achieved the hemisynthesis of flavano-ellagitannins, then we used them to develop a new efficient detection and quantification procedure for the C-glycosidic ellagitannins as well as flavano-ellagitannins.

Climatic changes cause significant variations in the composition of grapes. for red grapes, a mismatch between phenolic and technological ripening is often observed. There is also often a marked increase in pH and a reduction in fixed acids, which affect the stability and evolution of the wine during ageing. These experiments will provide more information on the role of pH during the winemaking of red wines on the extraction and evolution of phenolic compounds.

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.

es indices agroclimatiques concernant le bilan hydrique et la température moyenne de l’air, ont été utilisés pour la caractérisation des zones avec différentes aptitudes pour la viticulture de vin (Vitis vinifera L.) dans l’état du Maranhão, Brésil.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).