Il piano regolatore delle citta’ del vino: una metodologia di lavoro

A part, at least, of red wines fruity expression may be explained by perceptive interactions involving particularly various substituted ethyl esters and acetates present at concentration far below their olfactory threshold, specifically thanks to synergistic effects. Wine sensory perception is directly linked to the stimulation of the taster at the level of olfactory epithelium by volatiles. These compounds are liberated from the matrix to the atmosphere, and will then be smelt. From a physico-chemical point of view, these volatiles ability to be released may be evaluated by their partition coefficients, which correspond to the volatile concentration ratio between the liquid and gas phase. Our goal is, through these coefficients determination, to assess if volatile matrix composition is able to impact the volatility of some compounds, and then explain sensory perception, i.eto evaluate what is called the pre-sensorial level impact.

The black box of taste is getting unlocked. The starting point is to distinguish taste from tasting. Consider taste as a product characteristic; tasting is a sensorial activity. Consequently, taste can be studied on a molecular level and therefore be assessed more objectively, whilst tasting is a human activity and by definition subjective.

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.

[English version below]

Les terroirs viticoles, et plus particulièrement ceux des vignobles AOC, sont aujourd’hui menacés par de multiples agressions. Ces territoires sont non seulement l’outil de production

Drought and heat waves deriving from climate change have been affecting grapevine plants and altering wine characteristics in the past years, and effects are expected to get worst. Innovative approaches to address this problem have been undertaken in several varieties, that consist in exploring intravarietal variability to identify genotypes that are tolerant to abiotic stress. Such is the case of the variety Arinto, where an experimental population of 165 clones installed according to a resolvable row-column design with 6 replicates, was scanned for several parameters, including surface leaf temperature (SLT). Linear mixed models were fitted to the data of the traits evaluated, and the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each trait were obtained as well as the coefficient of genotypic variation (CVG) and broad sense heritability.