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

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.

Global warming and increased frequency and/or severity of drought events are among the most threatening consequences of climate change for agricultural crops. In response to drought, grapevine (as many other plants) exhibits osmotic adjustment through active accumulation of osmolytes which in turn shift the leaf turgor loss point (TLP) to more negative values, allowing to maintain stomata opened at lower water potentials1. We investigated the capacity of Pinot noir leaves to modulate their osmotic potential as a function of: (i) time (seasonal osmoregulation), (ii) growing temperatures, and (iii) drought events, to enhance comprehension of the resilience of grapevines in drought conditions. We performed trails under semi-controlled field conditions, and in two different greenhouse chambers (20/15 °C vs 25/20 °C day/night). For two consecutive vegetative seasons, grafted potted grapevines (Pinot noir/SO4) were subjected to two different water regimes for at least 30 days: well-watered (WW) and water deficit (WD).

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).

By dissecting the root system architecture (RSA) into its underpinning components (e.g. root emission, axial growth, radial growth, branching, root direction or tropism) and identifying the relationships between them, functional-structural 3D root models are promising tools for analyzing the diversity and complexity of root system phenotypes with Genotype × Environment interactions. The model parameters are assumed to be synthetic traits, less influenced by the environment, and consequently with less polygenic architectures than the integrative RSA traits they drive. Root models can serve as a basis for in silico development of root system ideotypes by highlighting the developmental processes and parameters that most likely influence RSA fitness.

Wine perception is a multisensory experience that makes use of the sight, smell, and taste senses. When wine is sensorially assessed, the stimulus received generates multiple signals that tasters convert into organoleptic descriptors. Colour is commonly the first attribute evaluated during wine tasting. Moreover, the colour properties provide the taster with a priori information of the wine’s aroma. This preconceived perception is later confirmed or denied during the aroma evaluation.