Impact of chitosan treatment on the physico-chemical features of a sangiovese red wine

It is quite common nowadays to carry out analyses which allow to control the ageing of spirits that are aged in wood casks. Many control parameters have been previously studied, such as the concentration of different phenolic compounds or the Total Polyphenol Index, in order to better understand the ageing process of wood aged spirits. On the other hand, it is frequent to analyse as a physical parameter the colour of those spirit samples, by stating them as an array of three coordinates from various colour spaces as CIE L*a*b* or CIE L*C*H*.

It is important nowadays for wine producers to create a product that is an expression of their terroir, a concept including the interaction between a place (topography, climate, soil), the people (tradition, winemaking and viticultural practices) and the resulting product (grape varieties, wines) [1]. Nonetheless, wine’s typicity linked to those terroirs must be easily recognizable by consumers thanks to distinctive sensory characters and composition [2]. Among the compounds of interest, aromatic compounds and polyphenols play an important role in the quality of red wines, by impacting on the odour, color and astringency. To explore the influence of terroir factors, including climate, soil and human practices, on the chemical and sensory profile of wines, red wines from five terroirs of the Corbières appellation were subjected to a general study approach.

Microsatellite markers are a valuable tool to facilitate the management of germplasm collections and assess genetic diversity. This study reports the genetic characterization of a large collection of 379 rootstocks and other non-viniferaaccessions maintained at the University of Milan, Italy.

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.

Brazil has a recent history on geographical indications and product regulation for high quality wines. The first geographic indication implemented was the Vale dos Vinhedos Indication of Procedence (