LC-MS based metabolomics discriminates premium from varietal chilean Cabernet Sauvignon cv. Wines

Field-grown crops such as grapevines, rely on ambient environmental conditions for growth and development.

This study investigated the impact of the gsk/gse ratio on the evolution of acetaldehyde and of major phenolic compounds of aglianico wine in wine like solution and real wine. Four model solutions and the correspondant control wines were prepared. The natural weight ratio between grape skins and seeds was determined on the real grapes, and a control wine was obtained from those.

The wine industry is currently on the search for new aromas and less browning in their products. In the improvement process of wine, lower fermentation temperatures have been considered, however, the yeasts in the market cannot tolerate such temperatures

The aim of this communication is the study of the Ronda and Arriate municipal territories environment in order to define and to establish the main physical factors in relation to vine-growing land use. The vine-growing zoning proposed is based on geopedological and climatic features.

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).