Preliminary results of water status and metabolite content of three new crossbreed winegrape genotypes

In the Bordeaux vineyards, press wine represents approximately 15% of the total volume of wine produced[1]. Valuing this large volume of wine is necessary from an economic point of view, but also because of their organoleptic contribution to the blend, and their contribution to the construction of wines for laying down. Therefore, this study was developed considering the lack of recent scientific knowledge on the composition of red press wines. The aim of this study is to establish an initial assessment of their chemical composition including aromatic compounds and a phenolic part.

In grapevine, phenology from bud break to berry maturation, depends on temperature and water availability. Increases in average temperatures accelerates initiation of bud break, exposing newly formed shoots to detrimental environmental stresses. It is therefore essential to identify genotypes that could delay phenology in order to adapt to the environment. The use of different rootstocks has been applied to change scion’s characteristics, to adapt and resist to abiotic and biotic stresses[1].

The odors of wines are diverse, complex and dynamic and much research has been devoted to the understanding of their chemical bases. However, while the “basic” chemical part of the problem, namely the identity of the chemicals responsible for the different odor nuances, was satisfactorily solved years ago, there are some relevant questions precluding a clear understanding. These questions are related to the physicochemical interactions determining the effective volatilities of the odorants and, particularly, to the perceptual interactions between different odor molecules affecting in different ways to the final sensory outputs.

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).

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.