Bio-protection by one strain of M. Pulcherrima: microbiological and chemical impacts in red wines

Vine water status was measured on 96 plots of three vines inside a vineyard block of 0.28 ha during three years: 2003, 2004 and 2005. Three physiological indicators were implemented: stem water potential, carbon isotope discrimination measured on grape sugars at ripeness (δ13C) and canopy temperature measured by high resolution remote sensing. For stem water potential, measurements were taken on every single vine of each plot.

La zonazione viticola rappresenta un importante strumento di indagine per valutare e interpretare le potenzialità produttive e qualitative di un territorio. Con l’obiettivo di studiare come l’ambiente influisca sulla qualità dell’uva nell’areale di Brisighella, sono stati monitorati, nelle annate 2007, 2008 e 2009, 14 vigneti per la varietà Albana e 38 per la varietà Sangiovese, rappresentativi di una area vitata di circa 1000 ha.

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.

Oenological tannins are products extracted from various botanical sources, such as mimosa,
acacia, oak gall, quebracho, chestnut and tara. The polyphenolic component is obtained through a solid-liquid extraction also using specific solvents, then removed by evaporation or freeze-drying. Tannins are employed in two phases of winemaking, during the pre-fermentative phase or during fining with different purposes such as modulate antioxidant activity, colour stabilization, bacteriostatic activity, protein stabilization and modulation of sensory properties. To date, the current regulatory framework is not very clear. In fact, the Codex Alimentarius classifies commercial tannins as “food additives” but also as
“processing aids”. The main distinction is that “additives” have a technological function in the final food, whereas “processing aids” do not. In this sense, oenological tannins, despite the technological treatments, could contain aromatic compounds of the botanical species they belong to and release them to the wine.

Grapevine leaf removal (LR) in the cluster area is typically done between fruit set and cluster closure to create an unfavorable microclimate for fungal diseases, such as Botrytis cinerea and powdery mildew. Grape growers are now turning their attention to pre-flowering LR, which has additional benefits under certain conditions. When applied before flowering, LR strongly affects fruit set and thus the number of berries per cluster. It is therefore a good yield control tool, replacing time-consuming manual cluster thinning (Poni et al. 2006). It also improves berry structure, that is, skin thickness, skin-to-pulp ratio, and berry composition (total soluble solids, titratable acidity, and polyphenols) (Palliotti et al. 2012; Komm and Moyer 2015). By exacerbating competition for assimilates between reproductive and vegetative organs, pre-flowering LR also poses some risks. Excessive yield loss at the same year’s harvest due to a too low fruit set rate is the main concern: intensive pre-flowering LR (100% of the cluster area) can induce up to 50% yield loss in potted vines (Poni et al. 2005). Other parameters, such as cool climatic conditions during flowering, also affect fruit set rate and make it difficult to predict potential yield at harvest. Repeated and overly intensive preflowering LR can have repercussions over time and induce a decline in bud fruiting and plant vigor (Risco et al. 2014).