Saccharomyces cerevisiae – Oenococcus oeni – Lactiplantibacillus plantarum: focus on malolactic fermentation during production of Catarratto and Riesling white wines

The metabolomics era started about 22 years ago, and wine was one of the first foodstuff subjects of analysis and investigation by this technique.

The use of high-power ultrasound (US) during the winemaking process has been extensively studied at laboratory scale in order to demonstrate its possible use to improve the extraction of compounds of interest. However, studies on semi-industrial and industrial scale are needed to confirm this positive effect, since the International Organization of Vine and Wine approved its industrial use in 2019 [1].

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

The seminar will examine the complexities and prospects of genomic research on Vitis species, characterize by exceptionally high heterozygosity and common interspecific gene flow. The seminar will showcase case studies highlighting the critical role of diploid genome references in grape research, specifically in areas such as aroma development, disease resistance, and domestication traits. It will also address the emerging focus on pangenomes within the Vitis genus, particularly in the context of genetic studies on naturally interbreeding populations.

The content of 3-sulfanyl-1-hexanol and its acetate ester in wine is affected by a number of factors, including the concentration of its precursors S-3-(hexan-1-ol)-L-glutathione (G-3SH),