Characterization of the thiol aromatic potential of a new resistant grape variety: Floreal

Vitis vinifera L. is the most widely cultivated Vitis species which includes numerous cultivars. Owing to their superior quality of grapes, these cultivars were long considered the only suitable for the production of fine wines. However, the lack of resistance genes in V. vinifera against major grapevine pathogens, requires for its cultivation frequent spraying with large amount of fungicides. Thus, the search for alternative and more sustainable methods to control the grapevine pathogens have brought the breeders to focus their attention on other Vitis species. In fact, wild Vitis genotypes present multiple resistance traits against pathogens, such as powdery mildew, downy mildew and phylloxera.

Bordeaux is the largest appellation vineyard in France. This contrasting vineyard with varied terroirs offers all styles of wine, resulting from the blending of several grape varieties. If these different profiles make the renown of Bordeaux wines, it can appear as a constraint when the aim is to study Bordeaux wines in their diversity. The selection of a representative sample can be performed by a sensory analysis carried out by trained panelists or by wine professionals, which can take several forms: consensus among experts, conventional descriptive analysis, typicality or quality evaluation. However, because of time, economic, and logistical constraints, these methods have limited applications. As an alternative to classical descriptive analysis, more intuitive methods that do not require training have been proposed recently to describe wines using an expert panel such as Napping, Free Choice or Flash Profiling, CATA or RATA.

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.

Many high quality Open Source scientific applications have been available for a long time. Some of them have proved to be particularly useful for carrying out the usual activities involved in viticultural research projects, such as statistical analyses (including spatial analyses), GIS work, database management (possibly integrated with statistical and spatial analysis) and even “low-level” often highly time-consuming activities (e.g. repetitive task on text files).

AIM. Assess the impact of different vineyards and winemaking variables on the phenolic and volatile profiles of Pinot Blanc musts and young wines from South Tyrol.