Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

The study of the capacity to consume oxygen of the wines is an aspect of great interest since it allows to analyse their useful life.

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).

Aims: This study aimed to (1) characterise colour and phenolic profiles of commercial Australian Pinot noir wines, (2) understand regional drivers of sensory and volatile profiles of commercial Australian Pinot noir wines, and (3) generate a deeper understanding of where Australian Pinot noir wines profiles sit in an international context.

Differences in wine flavour proceed primarily from grape quality. Environmental factors determined by the climate, soil and training systems modify many grape and wine quality traits. Metabolic profiling based on proton nuclear magnetic resonance (1H-NMR) spectra has been proved to be useful to study multifactorial effects of the vine environment on intricate grape quality traits. The capacity of this method to discriminate the environmental effects on wine has to be demonstrated.

This work began as an intellectual game, in order to discuss the notion of wine quality in terms of terroir and territory spatial structure. Vine and wine quality has long been questioned by scientists. Each discipline approaching it with his own tools.