Understanding the impact of rising temperatures due to climate change on aromatic compositions in Malbec wines from Mendoza, Argentina

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.

Many works have been carried out to identify the key aroma volatile compounds of oak wood (e.g., whisky-lactone, furfural, maltol, eugenol, guaiacol, vanillin) using conventional gas chromatography coupled with olfactometry and mass spectrometry (GC-O-MS). Inspired by recent untargeted approaches in the field of food “omics”, this work aims to extend our knowledge on the impact of cooperage process on the volatile composition of oak wood using two-dimensional comprehensive gas chromatography coupled with time of flight mass spectrometry (GCxGC-ToFMS).

Grapevines are grown mainly as grafts worldwide, but the rootstocks most commonly used were selected between the late 19th and early 20th centuries and are based on reduced genetic diversity[1]. In the context of climate change, it is indeed urgent to diversify the range of rootstocks with genotypes much more adapted to drier environments, than the existing ones[2]. The aim of this study was to evaluate the potential of new genetic resources for grapevine rootstock breeding programs. For this purpose, 12 American and Asian wild Vitis species (3 to 5 accessions per species = 50 accessions) were evaluated for their rooting ability and drought response.

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.

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).