Application of an in vitro digestion model to study the bioaccessibility and the effect of the intestinal microbiota on the red wine proanthocyanidins 

The sustainability of viticulture in response to climate change has been addressed mainly considering agronomic impacts, such as water management and diseases, either separately or together.
In grapevines, there is strong evidence that different genotypes respond differently to biotic and abiotic stresses. A screening was conducted on various local cultivars in response to drought and Neofusicoum parvum infection aiming to evaluate their susceptibility to abiotic stress and resistance to fungal diseases.

Grapevine (Vitis vinifera L.) exhibits a high level of genetic and phenotypic diversity among the approximately 6000 cultivars recorded. This perennial crop is highly vulnerable to numerous fungal diseases, including esca, which is a complex vascular pathology that poses a significant threat to the wine sector, as there is currently no cost-efficient curative method[1]. In this context, an effective approach to mitigate the impact of such diseases is by leveraging the crop’s genetic diversity. Indeed, susceptibility to esca disease appears to vary between cultivars, under artificial or natural infection. However, the mechanisms and varietal characteristics underlying cultivar susceptibility to esca are still unknown.

Several studies have tried to develop different methods to study the photodegradation of wine in an accelerated way, trying to elucidate the effect of light on the wine compounds[1]. In a previous study, our team developed a chamber that speeds up the photodegradation of rosé wine[2]. In the present work we have tried to establish a correlation between irradiation times in accelerated conditions and the natural exposure to the cycles of light that usually exist in markets or at home.

Global changes and sustainability challenge researchers in saving water and nutrients. The response of woody crops, which can be forced at facing more drought events during their life, is particularly important. Vitis vinifera can be an important model for its relevance in countries subjected to climate changes and its breeding, requiring cuttings plantation and strong pruning.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).