Characterization of four Chenin Blanc-rootstock combinations to assess grapevine adaptability to water constraint

AIM. Wine yeast lees constitute a winemaking by-product that, unlike grape skins and seeds, are not sufficiently exploited to add value to the winemaking sector, as their treatment and disposal generally represents a cost for wineries [1].

In grape berry pedicel, xylem hydraulic conductance can be impaired by blockage deposition in the lumen of xylem elements. However, the varietal difference of the interruptions has not yet been characterized. In this preliminary work, we utilized synchrotron x-ray computed microtomography experiments performed at MOGNO beamline (LNLS – Brazil) to identify possible blockage sites in natural grape pedicel xylem. For this, we imaged dehydrated pedicel’s stem portion from the Niagara Rosada variety in three different phenological stages (Pre-veraison (PreV), veraison (V) and post-veraison (PostV). The reconstructed tridimensional images with a voxel size of 1.16 µm were segmented for the identification of xylem vessel lumens. After analysing one pedicel stem per stage, we identified 658 vessels without occlusion throughout his axial plane and 41 in which we could identify possible interruptions.

Red wines constitute the majority of the wines produced in Bordeaux. All along the winemaking process, many microorganisms may develop in wine. A lot of them are useful but a common defect found in wine is linked to the development of Brettanomyces bruxellensis, a yeast that produces volatile phenols. These molecules are responsible for an unwanted sensorial defect described as similar to “horse sweat”, “burnt plastic” or “leather”. It has been shown that while some wines are very permissive and easily contaminated, others are pretty resistant to Brettanomyces development. However, common parameters such as pH, alcohol or sugars composition cannot fully explain the differences observed in wine permissiveness.

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.

Las uvas aromáticas se pueden dividir en dos clases, Moscateles y Malvasías, dependiendo del hecho de que el linalol o el geraniol, respectivamente, sean los alcoholes terpénicos monohidroxilados que