High-throughput sequencing analysis based on nematode indices revealed healthier soils of organic vineyards 

Winegrowing is still characterized by the extensive use of chemical fertilizers and plant protection products, despite strong recommendations to limit these practices. A part of these xenobiotics and metals are then found in grape juice and wine, causing a major health concern, as well as negatively affecting the fermentation process. In recent years, there has been renewed interest in non-Saccharomyces yeasts. These species have a wide phenotypic diversity, which would be exploited to broaden the aromatic palette of wines.

The application of remote and proximal sensing is a fast and efficient method to monitor grapevine vegetative and physiological parameters and is considered valuable to derive information on associated yield and quality traits in the vineyard. Further details can be obtained by the application of molecular analysis at the gene expression level aiming at elucidating how pathways controlling the formation of different grape quality traits are influenced by spatial variability. This work aims at evaluating intra-vineyard variability in grape composition at harvest and at comparing this with remotely sensed canopy vegetation data and molecular-based approaches.

The presence of undesirable compounds in wines, such as OTA, biogenic amines and pesticides residues, affects wine quality and can cause health problems for the consumer. The main tool that a winemaker has to reduce their content in the wine is fining. However, some of the fining agents commonly used in the winery can cause allergies or even increase the protein content in the wine, increasing the turbidity. To avoid these problems, the use of plant fibers may be an alternative, such as those from grape pomace[1] or other plant origins.

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).

The seminar will examine the complexities and prospects of genomic research on Vitis species, characterize by exceptionally high heterozygosity and common interspecific gene flow. The seminar will showcase case studies highlighting the critical role of diploid genome references in grape research, specifically in areas such as aroma development, disease resistance, and domestication traits. It will also address the emerging focus on pangenomes within the Vitis genus, particularly in the context of genetic studies on naturally interbreeding populations.