Data deluge: Opportunities, challenges, and lessons of big data in a multidisciplinary project

The Institut Français De La Vigne Et Du Vin (IFV) is conducting many experiments on innovative winegrowing practices, which are emerging in companies in the sector, or which are still at the R&D stage for agricultural suppliers. The purpose of these practices may be to reduce environmental impact, to adapt vineyards to climate change, or to achieve other technical, economic or social aims. Whatever the objective, it is necessary to verify the relevance of these new practices, and in particular their environmental relevance, i.e. That at the very least, the changes in practices do not increase the environmental impact of the technical itineraries.

Aim of the study was to investigate the metabolomic differences between Chilean Cabernet Sauvignon wines, divided according to their quality in two main groups: “Varietal” and “Premium”, and to point out metabolites tentative markers of their chemical signature and sensorial quality. Initially, 150 (50 x 3 biological replicates) experimental wines were produced by the same semi-industrial process, which covered 8 different Chilean valleys. The wine classification made by experts, divided the wines into two major groups (“Varietal” and “Premium”) and four subgroups (two for each major group). All the samples were analyzed according to a robust LC-MS based untargeted work-flow (Arapitsas et al 2018), and the proposed minimum reporting standards for chemical analysis of the Metabolomics Standards Initiative (Sumner et al 2007)

In a context of a sustainable viticulture, a new uprising strategy to improve grape and wine composition (or quality) is the exogenous application of plant activators(Gil-Muñoz et al., 2017)

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).

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