Interactions « Terroir x Vigne » : facteurs de maîtrise du micro-environnement et de la physiologie de la plante en rapport avec le niveau de maturité et les éléments de typicité

The production of grapes with a balanced composition is one of the main goals that agronomists and oenologists pursue to produce premium quality wines.

Vitamins are major compounds, involved in several prime yeast metabolic pathways. Yet, their significance in oenology has remained mostly unexplored for several decades and our current knowledge on the matter still remaining obscure to this day. While the vitaminic contents of grape musts have been approached in these ancient investigation

A novel and efficient Dispersive Liquid-Liquid Microextraction (DLLME) method coupled with gas chromatography–mass spectrometry (GC–MS) was developed to determine 33 key aroma compounds (esters, alcohols, aldehydes, terpenes, norisoprenoids, fatty acids and phenols) present in Pinot noir (PN) wine. Four critical parameters including extraction solvent type, disperse solvent type, extraction solvent volume and disperse solvent volume were optimised with the aid of D-optimal design.

The main challenge of regulated deficit irrigation (RDI) research is to isolate the factors that come with RDI, the direct effect of plant water status from the indirect ones like increased radiation and temperature changes on the cluster zone. This study aims to isolate the effects of vine water status from the effects of increased radiation on the phenolic composition of grapes subjected to RDI.
A three-year study on an RDI experiment where radiation was controlled was implemented in a commercial vineyard of Cabernet Sauvignon in Chile. Four RDI treatments based on partial evapotranspiration (ET) irrigation were established. Irrigation treatments were 100% ET, 70% ET, 50-100% ET (50% ET before veraison and 100% ET afterward), and 35-100% ET (35% ET before veraison and 100% ET afterward).

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.