| Popis: |
Due to climate change, the higher incidence and severity of bushfires is a significant challenge for wine producers worldwide as an increase in smoke contamination negatively affects the physicochemical components that contribute to the lower quality of fresh produce and final products (smoke taint in wines). This reduces prices and consumer acceptability, impacting the producers and manufacturers. Current methods available to winemakers for assessing contamination in berries and wine consist of costly laboratory analyses that require skilled personnel and are time-consuming, cost prohibitive, and destructive. Therefore, novel, rapid, cost-effective, and reliable methods using digital technologies such as the use of near-infrared (NIR) spectroscopy, electronic nose (e-nose), and machine learning (ML) have been developed by our research group. Several ML models have been developed for smoke taint detection and quantification in berries and wine from different varieties using NIR absorbance values or e-nose raw data as inputs to predict glycoconjugates, volatile phenols, volatile aromatic compounds, smoke-taint amelioration techniques efficacy, and sensory descriptors, all models with >97% accuracy. These methods and models may be integrated and automated as digital twins to assess smoke contamination in berries and smoke taint in wine from the vineyard for early decision-making. |
