Identification of the agronomical and landscape potentialities in Côtes du Rhône area (France)

In this study we developed a methodology to prepare aromatic reconstitutions from fractions of a wine organic extract and we assessed these reconstitutions both in wine model solution and in de- aromatized wine.

Fermenting grape juice represents one of the oldest continuously maintained anthropogenic microbial environments and supports a well-mapped microbial ecosystem. Several yeast and bacterial species dominate this ecosystem, and some of these species are part of the globally most studied and best understood individual organisms. Detailed physiological, cellular and molecular data have been generated on these individual species and have helped elucidate complex evolutionary processes such as the domestication of wine yeast strains of the species Saccharomyces cerevisiae. These data support the notion that the wine making environment represents an ecological niche of significant evolutionary relevance. Taken together, the data suggest that the wine fermentation ecosystem is an excellent model to study fundamental questions about the working of microbial ecosystems and on the impact of biotic selection pressures on microbial ecosystem functioning. Indeed, and although well mapped, the rules and molecular mechanisms that govern the interactions between microbial species within this, and other, ecosystems remain underexplored. Here we present data derived from several converging approaches, including microbiome data of spontaneous fermentations, the population dynamics of constructed consortia, the application of biotic selection pressures in directed laboratory evolution, and the physiological and molecular analysis of pairwise and higher order interactions between yeast species. The data reveal the importance of cell wall-related elements in interspecies interactions and in evolutionary adaptation and suggest that predictive modelling and biotechnological control of the wine ecosystem during fermentation are promising strategies for wine making in future.

Yeast species S. cerevisiae, S. uvarum, S. kudriavzevii and their hybrids present clear metabolic differences, even when we compared S. cerevisiae wine versus wild strain. These species and hybrids produced significantly higher amounts of glycerol, organic acids, 2,3-butanediol, and 2-phenyl ethanol and a reduction of the ethanol yield, properties very interesting in the sector to deal with climate change effects. To understand the existing differences, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum, S. kudriavzevii, and hybrids.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.

The H2020 RUSTICA project aims to propose, demonstrate, and implement technical solutions to convert organic residues from fruit and vegetables into high-quality novel bio-based fertilisers (BBF).