Analysis of mousy off-flavour wines

The precise molecular mechanisms underlying the domestication of grapevine (Vitis vinifera L.) Are still not fully understood. In the recent years, next-generation sequencing (NGS) of plastid genomes has emerged as a powerful and increasingly effective tool for plant phylogenetics and evolution. To uncover the biological profile of the grapevine domestication process comprehensively, an investigation should encompass both the cultivated varieties (V. vinifera subsp. Vinifera) and their wild ancestors V. vinifera subsp. Sylvestris) across all potential sites of their distribution and domestication.

Changes in bioclimatic indices in wine-growing region of Serbia are analyzed under the RCP 8.5 IPCC scenario.

AIM: ‘Nebbiolo’ (Vitis vinifera L.) is one of the most ancient and prestigious Italian grape cultivars. It is renowned for its use in producing monovarietal high-quality red wines, such Barolo and Barbaresco. Wine quality and value can be heavily modified if cultivars other than those allowed are employed.

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.

Acetaldehyde is a volatile carbonyl compound synthetized by yeast during alcoholic fermentation, but it can also be formed by oxidation of ethanol during wine aging [1]. At low concentration, it enhances the fruity aroma, however, at higher levels, it can generate the appearance of notes of bruised and rotten apple [2]. From a chemical point of view, acetaldehyde is a reactive low-