Freeze-thaw treatment to enhance phenolic ripening and tannin oxidation of seeds

The wine sector plays a significant role in the international agri-food industry, with the winemaking process leading to the generation of considerable amounts of by-products. Among these by-products, grape pomace, is one of the most abundant resources, mainly finding application in the production of distillates, fertilizers, and animal feed.

Wine research in the past years has mainly been focused on laboratory scale due to the possibility of controlling winemaking variables. Conversely, studies on wine quality in relation to the winemaking variables at the winery scale may be able to better account for the actual challenges encountered during wine production. Winemaking problems are recently arising from progressive changes in environmental conditions in relation to the terroir. It is important to realize that each wine region may have specific winemaking protocols and that winemakers often base their decisions on subjective, emotional, and empirical opinions. Due to all the above-mentioned issues, taking the correct decision in winemaking to achieve the desired goals may become even more challenging.

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.

Climate change impacts on both yields and quality have increased over the past decades, with the effects of extreme climate events having the most dramatic and obvious impacts. Increasing length and intensity of heatwaves associated with increased water stress necessitates a reevaluation of climate change responses of grapevine and, ultimately, a reconsideration of vineyard management practices under future conditions. Here we summarize results from a three-year field trial manipulating irrigation prior to and during heatwave events to assess impacts of water application rates on vine health and physiology, berry chemistry, and wine quality. We also highlight potential mitigation strategies for extreme heat, both in terms of water application, as well as other cultural practices that could be widely applicable.

This study investigates the effect of wood chips addition during the alcoholic fermentation on the phenolic
composition of the produced wines. A series of wood chips, originating from American, French, Slavonia
oak and Acacia were added at the beginning of wine alcoholic fermentation. Besides, a mixture consisting
of 50% French and 50% Americal oak chips were added during the experimentation. The wine samples
were analyzed one month after the end of malolactic fermentation, examining various chemical
parameters such as total anthocyanins, total phenolic content, tannins combined with protein (BSA) and
ellagitannin content.