Grape seed powder as an alternative to bentonite for wine fining

The production of fermented alcoholic beverages involves numerous processes in which microorganisms and enzymes convert components derived from the raw material into a wide range of compounds that affect the sensory characteristics of the resulting product. It is estimated that there may be as many as 800 to 1,000 such compounds in wine. These compounds belong to different chemical groups such as esters, alcohols, carboxylic acids, carbonyl compounds, polyphenols, sugars and many others.

Analytical methods for dating wines often rely on assessing anthropogenic and cosmogenic radionuclides, including 14C and 137Cs [1,2].

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.

In this video recording of the IVES science meeting 2021, Johanna Döring (Hochschule Geisenheim University Department of General and Organic Viticulture, Geisenheim, Germany) speaks about the comparison of integrated, organic and biodynamic viticultural practices. This presentation is based on an original article accessible for free on OENO One.

Since the emergence of phylloxera, grafting has been the most used propagation method in viticulture. Despite all the improvement measures implemented in the nurseries, it is frequent that graft success rates vary depending on the nursery process and scion/rootstock combinations. The reasons behind this unsatisfactory behaviour are still unknown and can be diverse, although carbohydrate reserves might be hypothesised to be crucial, since callus, root, and new tissue formation will be built based on them. In order to identify the effect of carbohydrates on grafting success, nine combinations were established based on the starch content in grapevine scionwoods (cv. Tempranillo clone VN69) and rootstocks cuttings (110 Richter clone 237) used for grafting: Low (L), Medium (M), High (H).