Yeast co-inoculations in winemaking have been investigated in various applications, but most often in the context of modulating the aromatic profiles of wines. Our study aimed to characterize S. cerevisiae interactions and their impact on wine by taking an integrative approach. Three cocultures and corresponding pure cultures of S. cerevisiae were characterized according to their fermentative capacities, the chemical composition and aromatic profile of the associated Chardonnay wines. The various strains studied within the cocultures showed different behaviors regarding their development.

Wine contains secondary metabolites derived from aromatic amino acids (AADC), which can determine quality, stability and bioactivity. Several yeast species, as well as some lactic acid bacteria (LAB), can contribute in the production of these aromatic compounds. Winemaking should be studied as a series of microbial interactions, that work as an interconnected network, and can determine the metabolic and analytical profiles of wine. The aim of this work was to select microorganisms (yeast and LAB) based on their potential to produce AADC compounds, such as tyrosol and hydroxytyrosol, and design a microbial consortium that could increase the production of these AADC compounds in wines.

Phenolic compounds are responsible for the most important red wine quality attributes. Anthocyanins and tannins play crucial roles in color and mouthfeel properties of red wines. Phenolic analysis in the winery is hindered by analytical constrains.

Heat and drought episodes during the growing season are becoming more frequent and more severe in many of the world’s grape‐growing regions

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.