VviSOC1a and VviAG1 act antagonistically in the regulation of flower formation

To be accepted by the market and consumers table grapes need to meet certain requirements in terms of physical and chemical quality parameters.

AIM: A genetic study of four wine T. delbrueckii strains was done. Spore clones free of possible recessive growth‐retarding alleles with enhanced resistance to winemaking stressing conditions were obtained from these yeasts. METHODS: The genetic marker of resistance to cycloheximide (cyhR) allows easy monitoring of the new mutants obtained from these yeasts.

The purpose of this research was to study the interaction between oenotannins and wine matrix in order to design a targeted oenotannins addition for modulating the redox status of wine. It is in fact known that oenotannins can regulate the redox potential of musts and wines since they are electroactive substances (1).

While the trend in winemaking is toward reducing the inputs and especially sulphites, the development of While the trend in winemaking is toward reducing the inputs

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.