The role of œnology in the enhancement of terroir expression

Acacia senegal gum (Asen) is an edible dried gummy exudate [1] added in young red wines to ensure their colloidal stability, precluding the precipitation of the coloring matter. Asen macromolecules, belonging to the arabinogalactan-protein (AGP) family [2], are hyperbranched, charged and amphiphilic heteropolysaccharides composed especially of sugars (92-96 %) and a small fraction of proteins (1-3 %). Asen is defined as a continuum of macromolecules that could be separated into three fractions by hydrophobic interaction chromatography (HIC) [3-4]. HIC-F1 (85-94 % of Asen), HIC-F2 (6-18 % of Asen) and HIC-F3 (1-3 % of Asen) are named and classified in that order according to their protein content, and then a growing hydrophobicity. The efficiency of Asen towards the coloring matter instability is evaluated according to an “efficacy test” that consists to determine the Asen quantity required to prevent the flocculation by calcium of a colloidal iron hexacyanoferrate solution (International Oenological Codex).

The wine industry is currently shifting toward more sustainable production systems. There are many reasons for this as the interest of people over climate change and, consequently the wine consumer’s choice toward organic and biodynamic, reduced carbon-footprint, vegan and other environmentally friendly wines. While the viticultural effects of biodynamic and organic practices on wine grapes have been investigated, there is a lack in literature on the general effect on the final quality of wine

Esca, a severe trunk disease affecting vineyards, is caused by fungal pathogens that induce wood necrosis and decay, leaf symptoms, yield losses, and potentially a rapid death of the vine. The prevalence of this disease varies across years, regions, cultivars, and plot ages. Despite its significance in understanding and predicting dieback risk in different vineyards, the role of climate in trunk diseases remains a relatively unexplored research area. While some studies have demonstrated the impact of certain climatic conditions on the prevalence of the disease, they often focus on a limited number of plots and yield conflicting results.We conducted a statistical analysis, using a Bayesian approach on a national database comprising prevalence data of esca from over 500 different plots in France, spanning the years 2003 to 2022 and encompassing various cultivars.

The main objective of this work is to study and model the impact of the polyphenolic profile on the stability and quality of wine aroma during oxidative aging.

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).