Biotic interactions: case of grapevine cultivars – the fungal pathogen Neofusicoccum parvum – biocontrol agents 

Quercetin (Q) is present in grape in form of glycosides and as aglycone. These compounds are extracted from grape skins during winemaking. In wines, following the hydrolysis reactions, the amount of quercetin aglycon can exceed its solubility value. Unfortunately, a threshold solubility concentration for quercetin in wine is not easy to determine because it depends on wine matrix (Gambuti et al., 2020).

Against the background of climate change and the increasing impact of phytopathogenic agents of mycotic origin on the vine favors the appearance and toxicity of mycotoxins in wine.

The main scope of this research has been to investigate what values are attributed to the concept of “sustainability” by the wine producers of two different wine territories of Piedmont; the terroir of the Barolo DOCG and the the terroir of the Gavi DOCG. The research wants to emphasize how much the characteristic elements of each terroir influence the perception of the concept of sustainability among producers.

In mediterranean agriculture, sustainability and productivity are seriously threatened by climate change and water scarcity. This situation is exacerbated by poor management practices such as excessive use of agrochemicals, overgrazing, and monoculture. The Douro demarcated region (ddr) is an emblematic region, classified world heritage site by UNESCO in 2001. Viticulture is the main agricultural activity in DDR, widely known to produce port wine.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.