Evaluation of terroir suitability for vine cultivation in new areas using geographic multi-criteria decision support

It is common to find tanks in the winery with wine below their capacity due to wine transfers between tanks of different capacities or the interruption of operations for periods of a few days. This situation implies the existence of an ullage space in the tank with prolonged contact with the wine causing its absorption/oxidation. Oxygen uptake from the air headspace over the wine due to differences in the partial pressure of O2 can be rapid, up to 1.5 mL of O2 per liter of wine in one hour and 100 cm2 of surface area1 and up to saturation after 4 hours.

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

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).

Mannoproteins are released at the end of alcoholic fermentation due to yeast autolysis [1]. It has been described a positive effect of these molecules on lactic acid bacteria growth [2]. The main objective of this work was the characterization of different mannoproteins extracted from active dry yeast (ADY) and the assessment of their effect on Oenococcus oeni and malolactic fermentation (MLF).

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.