Making sense of a sense of place: precision viticulture approaches to the analysis of terroir at different scales

Cork taint decreases the commercial value of wine as tainted wines are rejected by consumers. Although other compounds in wine and cork can also be responsible for causing a taint, 2,4,6-trichloroanisole (TCA) is regarded as the primary cause of cork taint. As cork taint is often used in marketing campaigns against natural cork closures,

Global trends in the wine industry are changing, which is caused by consumer demands for aroma and flavour innovation. Producers in Australia, the sixth globally ranked wine producing country, are embracing this trend by exploring non-conventional yeast species to improve sensory qualities and achieve fermentation advantages.

The production of organically grown crops developed exponentially in the last few decades based on consumer demands for healthy food

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

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.