Towards the definition of a detailed transcriptomic map of grape berry development

The aim of the study was to study the volatile substances and ellagitannins released to wine by barrels of Quercus pyrenaica (Spanish Oak) in comparison with barrels of Quercus petraea (French Oak) and Quercus alba (American Oak) as well as to determine their sensory impact.

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.

As an industry that thrives more on, but may also be more affected by, vintage variation and regionality than any other agricultural enterprise, grape and wine production is ever more being impacted challenged by climate change.

Brettanomyces bruxellensis, commonly referred to as “Brett,” is one of the most notorious microorganisms implicated in wine spoilage. This yeast species has developed a noteworthy resistance to sulfur dioxide, a widely used preservative in winemaking, prompting the wine industry to seek new antimicrobial agents.

AIM: The composition and quality of wine are directly linked to microorganisms involved in the alcoholic fermentation. Several studies have been conducted on the impact of Saccharomyces cerevisiae on volatile compounds composition after fermentation. However, if different studies have dealt with combined sensory and volatiles analyses, few works have compared so far the impact of distinct yeast strains on the global metabolome of the wine.