Assessing the Effectiveness of Electrodialysis in Controlling Brettanomyces Growth in Wine

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).

Drought is one of the main challenges for viticulture in the context of global change. The choice of rootstock could be leveraged for vineyard adaptation to drought as we can improve plant performance without modifying the scion variety. However, most of the existing rootstocks, selected over a century ago, have a narrow genetic background which could compromise their adaptive potential.

Grapevine (Vitis spp.) is greatly influenced by climatic conditions and its economic value is therefore directly linked to environmental factors. Among these factors, temperature plays a critical role in vine phenology and fruit composition. In such conditions, elucidating the mechanisms employed by the vine to cope with heat waves becomes urgent. For the past few years, our research team has been producing molecular and metabolic data to highlight the molecular players involved in the response of the vine and the fruit to high temperatures [1]. Some of these temperature-sensitive genes are currently undergoing characterization using transgenesis approaches coupled or not with genome editing, taking advantage of the Microvine genotype [2].

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.

Grapevines are grown mainly as grafts worldwide, but the rootstocks most commonly used were selected between the late 19th and early 20th centuries and are based on reduced genetic diversity[1]. In the context of climate change, it is indeed urgent to diversify the range of rootstocks with genotypes much more adapted to drier environments, than the existing ones[2]. The aim of this study was to evaluate the potential of new genetic resources for grapevine rootstock breeding programs. For this purpose, 12 American and Asian wild Vitis species (3 to 5 accessions per species = 50 accessions) were evaluated for their rooting ability and drought response.