Advancing grapevine science through genomic research

Context and Purpose of the Study. In many countries, the preservation of grape varieties with heritage value is ensured by genebanks of outstanding significance, which allow for the study of these varieties and the assessment of their future roles in response to environmental, market, and social challenges.

This study present a detailed analysis of the rainfall and temperature changes in the Penedès region in the period 1995/ 96 – 2008/09, in comparison with the trends observed during the last 50 years, and its implications on phenology and yield.

Right after the pouring of wine in a glass, a myriad of volatile organic compounds, including ethanol, overwhelm the glass headspace, thus causing the so-called wine’s bouquet [1]. Otherwise, it is worth noting that during wine tasting, most people automatically swirl their glass to enhance the release of aromas in the glass headspace [1]. About a decade ago, Swiss researchers revealed the complex fluid mechanics underlying wine swirling [2]. However, despite mechanically repeated throughout wine tasting, the consequences of glass swirling on the chemical space found in the headspace of wine glasses are still barely known.

Rosé is leading the fastest growth wine category which hit a 40% increase since 2002. France accounts for over a third (34%) of global consumption followed by the US

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.