Analyse de la perception du terroir et de sa valorisation par les viticulteurs de l’Anjou

Amino acids play a crucial role in determining grape and wine quality [1]. Recently, research has suggested their metabolism is key to plant abiotic stress tolerance [2]. Therefore, the study of amino acid accumulation in grape berries and its response to environmental factors is of both scientific and economic importance.

Grapes’ sun-drying is one of the most critical steps in the production of ‘Commandaria’, a dessert wine with Protected Designation of Origin that is exclusively produced in Cyprus from grapes of the two indigenous cultivars (Vitis vinifera L.), namely ‘Mavro’ and ‘Xynisteri’. Despite its significant economic importance, no data regarding the primary and secondary metabolites of the aforementioned cultivars exist.

Sulphur dioxide (SO2) is a well-established preservative in the wine industry. Its ability to act in different stages of the process as an antioxidant and an antiseptic as main characteristics makes it versatile. However, the need for its reduction or even its replacement has been increasing by the regulatory authorities as well as by the final consumer. To understand the impact of SO2 during ageing on volatile organic compounds (VOCs) and amino acids (AAs) profiles, two white wines (one varietal and one blend) were aged under the same conditions, in the presence of different doses of SO2. After fermentation (t=0), 0, 30, 60, 90 and 120 mg/L of SO2 were applied, wines were kept over lees for 3 months (t=3), then were bottled after 3 (t=6) and 9 (t=12) months.

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.

Wine aroma is a crucial quality criterion. A multitude of volatile compounds have been identified and correlated to the aroma attributes perceived in wine.