The pedoclimatic conditions impact the yeast assimilable nitrogen concentration in the grapevine must and the valorisation of foliarnitrogen fertilisation

There is evidence in the literature that non-volatile wine matrix can modify the release and therefore the perception of the compounds involved in wine aroma [1-3].

Grape and wine phenolic compounds have been shown to be highly related to both wine quality (color, flavor, and taste) and health-promoting properties (antioxidant and cardioprotective, among others). The aim of this work was to evaluate and compare the phenolic contents of Cabernet Sauvignon wines from different geographical areas and climatic conditions, namely from Argentina, Portugal and Spain vintage 2022. In addition, the phenolic profiles of the Portuguese wines from three vintages (2020, 2021, 2022) was compared.

Simgi® platform pursues the need for dynamic in vitro simulation of the human gastrointestinal tract optimized and adapted to food safety and health fields. The platform has confirmed the model’s suitability since its first’s studies with the consistency between the simulated colonic metabolism of wine polyphenols and the metabolic evolution observed with the intake of wine in human intervention studies [1]. 

The structural diversity is one of the most remarkable characteristics of proanthocyanidins (PA). Indeed, PA in wines may vary in the B-ring and C-ring substitutes, the C-ring stereochemistry, the degree of polymerization (DP) and the linkage between the monomers. Knowing in detail the structural characteristics of the PA of a wine can help us to understand and modulate several sensorial characteristics of the wine, such as color, antioxidant properties, flavor, and mouthfeel properties. In the last years was discovered and confirmed the presence of sulfonated monomeric and oligomeric flavan-3-ols in wine [1], as well as was pointed out their importance in wine quality [1,2].

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.