L’effetto paesaggio sul sistema delle preferenze: i vini veneti tra evocazioni di consumo e determinanti di scelta

AIM To determine the effect of both ripeness and enzyme maceration on the astringency and bitterness perception of Cabernet Sauvignon winesRecent work has contributed to a more detailed understanding of the grape cell wall deconstruction process from ripening through crushing and fermentation, providing a better understanding of what role polysaccharides play in post-harvest fermentation of grapes(1,2). Current research on glycomics in red wine making suggest polysaccharides are important sensory impact molecules (3–6). METHODSOur experimental system harvests Cabernet Sauvignon grapes at three different ripeness levels and makes wine both with and without enzyme treatment.

PPhloem transport of assimilates provides the materials needed for the growth and development of reproductive structures, storage and developing organs, and has long been recognized as a major determinant in crop yield.

Wine quality and safety are the main concerns of consumers and health agencies. Biogenic amines and polyamines, depending on their concentration and on individuals, in wine can constitute a potential public health concern due to their physiological and toxicological effects

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.

AIM: Combined investigation of the influence of berry maturity, maceration time and wine maturation on the changes in polyphenols and sensory characteristics of Pinot noir and Cabernet-Sauvignon.