Identification of a stable epi-allele associated with flower development and low bunch compactness in a somatic variant of Tempranillo Tinto

The wine closure industry is mainly divided into three categories: screw caps, synthetic closures, and cork-based closures. Among this latter, microagglomerated cork stoppers treated with supercritical CO2 are now widely used, especially to avoid cork taint contaminations[1]. They are designed with cork granules obtained from cork offcuts of the punching process during the natural cork stoppers production. A previous study[2] showed that these stoppers released fewer polyphenols in 12 % (v/v) hydroalcoholic solution than natural cork stoppers.

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.

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.

The quality and color stability of red wines are directly related to content and distribution of phenolic compounds. However, the climate change produces the asynchrony between the dates of technological and maturity of grapes. The crop-forcing technique (CF) restores the coupling between phenolic and technological ripeness while limits vineyard yields. Blending of wines is frequently used to equilibriate composition of wines and to increase their stability, color and quality. The aim of the present work is to study the phenolic composition and color of wine blends made with FW (wines from vines subjected to CF) and CW (wines for vines under the usual cultivation practices).

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).