Combined abiotic-biotic plant stresses on the roots of grapevine

In order to avoid the loss of grapevine intra-varietal diversity of DOCa Rioja grape varieties, Regional Government of La Rioja established a germplasm bank with more than 1.600 accessions, whose origin lies in the prospecting and sampling of ancient vineyards located throughout the whole region. 30 clones of Tempranillo and 13 clones of Graciano were preselected and multiplied in a new vineyard for further observations. The aim of this work is to describe the first results from the physiological characterization by an optical sensor of these preselected clones, which constitute the base of a new clonal selection that aims to increase the range of available certified clones and to improve the adaptation of these varieties to future objectives and environmental conditions.

Many works have been carried out to identify the key aroma volatile compounds of oak wood (e.g., whisky-lactone, furfural, maltol, eugenol, guaiacol, vanillin) using conventional gas chromatography coupled with olfactometry and mass spectrometry (GC-O-MS). Inspired by recent untargeted approaches in the field of food “omics”, this work aims to extend our knowledge on the impact of cooperage process on the volatile composition of oak wood using two-dimensional comprehensive gas chromatography coupled with time of flight mass spectrometry (GCxGC-ToFMS).

Vines are exposed to environmental conditions that cause abiotic stress on the plants (drought, nutrient and mineral deficits, salinity, etc.). Polyamines are growth regulators involved in various physiological processes, as in abiotic plant stress responses. Stressful conditions can modify grape’s composition, and in this work, we have focused on studying the effect of abiotic stress on the composition of polyamines and amino acids in grapes. In addition, the effect of grape variety on these compounds has been studied.

The Agenda 2030 of the EU sets out the main guidelines for transitioning towards a resilient, green and safe economy. To this regard, the wine sector is experiencing an ecological transition in different ways such as increasing the production of ecological crops, or promoting the production of wines under more environmental-friendly and healthier (i.e., lower levels of SO2) products. These alternatives to conventional production are a smaller proportion of wines, in constant growth and demand, and follow alternative and minority practices, which range from sustainable to deeply philosophical thoughts. Among these methods there are organic, biodynamic and, more recently, natural 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).