From precursor identification to the study of the distribution of 3-methyl-2,4-nonanedione in red wines and spirits

Microbiomes have crucial roles in maintaining life on Earth, and their functions drive human, animal, plant and environmental health. The microbiome research landscape is developing rapidly and is performed in many different science fields using similar concepts but mostly one (eco)system at-a-time. Thus, we are only starting to unravel and understand the interconnectedness of microbiomes across the (eco)systems.

The challenges of the century for viticulture relate to coping with climate change and the loss of biodiversity in a downturning socio-economic context. Now more than ever, the vine and wine industry needs to be resilient to maintain and ensure a future for its heritage. An innovation of capital importance, in line with recently published research, deals with developing new methods of training our inherited and newly planted vineyards to better withstand environmental variations such as drought and heatwaves but also unevenly distributed rains and temperatures.

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.

Genetic variants known to produce single residue missense mutations have been associated with phenotypic traits of commercial interest in grapevine. This is the case of the K284N substitution in VviDXS1 associated with muscat aroma, or the R197L in VviAGL11 causing stenospermocarpic seedless grapes. The impact of such mutations on protein structure, stability, dynamics, interactions, or functional mechanism can be studied by computational methods, including our pyDock scoring, previously developed. For this, knowledge on the 3D structure of the protein and its complexes with other proteins and biomolecules is required, but such knowledge is not available for virtually none of the proteins and complexes in grapevine.

During a three-year period, the vineyards of the Carnuntum wine district are investigated for their terroir characteristics. The interdisciplinary study is aimed at the description of the physiogeographic