GC-O and olfactoscan approaches to reveal premature aging markers in Chardonnay wine

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.

The purpose of the study was to monitor berry development as a function of site, vine water status and climate in order to improve our understanding of the role played by climate change on secondary metabolites relevant to wine quality.

The wine industry worldwide faces more and more challenges due to climate change, such as increased dryness in some areas, water stress, sunburn and early harvesting during hot summer temperatures¹. One of the resulting problems for the wine quality might be a higher prevalence of the untypical aging off-flavor (ATA)². A substance, which Rapp and Versini made responsible for ATA, is the 2-aminoace-tophenone (2-AAP)³. 2-AAP in wine causes a naphthalene, wet towels, wet wool, acacia flower or just a soapy note⁴.

Dans la region Nord-Est du Brésil, située à la Vallée du São Francisco, localiséee entre les paralleles 8-9º HS, la production de vins tropicaux a commencé il y a une vigntaine d’années. Dans cette région, il est possible d’avoir au minimum deux récoltes par an, car la moyenne de température est de 26 ºC, avec une pluviosité moyenne de 550 mm entre les mois de janvier-avril.

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.