Grapevine adaptation to drought and resistance to Neofusicoccum parvum, causal agent of Botryosphaeria dieback

The wooden barrel is a key factor in enology, since wine chemical composition and sensory properties changes significantly in contact with the barrel[1]. Today’s highly competitive market constantly demands new differentiated products and wineries search innovations continuously. Wood selection is crucial: barrels stability to keep constant their contribution and the result on products, and additional and differentiated wood contributions to impact their new products. Oak wood selection has traditionally been carried out using parameters such as specie, location and grain, however, it goes one step further nowadays. Large cooperage work with non-destructive techniques that allow classifying oak wood quickly and easily according to their organoleptic contribution[2].

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.

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 is the result of the alcoholic fermentation (AF) of grape must. Besides AF, wine can also undergo the malolactic fermentation (MLF) driven out by lactic acid bacteria (LAB). Among LAB, Oenococcus oeni and Lactiplantibacillus plantarum are the dominant species in wine. Even if O. oeni is the most common LAB undergoing MLF in wine, due to its high tolerance to wine conditions, L. plantarum can be used to undergo MLF in must. The moderate tolerance of L. plantarum to low pH and ethanol, may compromise the fermentative process in harsh 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).