Guyot or pergola for dehydration of Rondinella grape

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.

Establish relationships between taste and mouthfeel properties of grapes and tannin-related chemical parameters. Tempranillo Tinto and Garnacha Tinta grapes were harvested from distinct blocks in different dates; each sample collection date was separated by one week. Grapes were destemmed and macerated in 15% of ethanol for one week. The polyphenolic fraction (PF) of samples was submitted to solid phase extraction on C18 cartridges and recovered with ethanol. PFs were reconstituted in wine model and their taste and mouthfeel properties were characterised by rate-K-attributes methodology. In parallel, concentration (TC) and activity (TAc) of tannins as well as the concentration of tannins linked to anthocyanins (T-A) were determined using HPLC-UV–VIS.

Grape seeds account for around 5% of the weight of the whole grape berry, representing approximately 40%-50% of the solid by-products that the different wine industries generate during the winemaking process.

Wines made with grapes submitted to postharvest dehydration are often referred to as “passito” or “straw wines.” This distinct style of winemaking consists of a process of water loss that allows the berries to undergo a mild water stress and senescence process [1].

Aim: In this work, we have optimized a robust methodology for investigating possible correlations between the phytochemical profile of wine and the terroir (including the climate), considering the specific wine-growing area. In particular, the untargeted metabolomic and sensorial profiles of Gutturnio DOC commercial wines (both still and “frizzante” types) from different production areas in the Piacenza province were determined. The geographical areas taken into consideration for this study consisted in Val Tidone, Val Nure and Val d’Arda.