Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.
Sensors (Basel). 2022 Mar 16;22(6):2303. doi: 10.3390/s22062303.
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94-96%; 92-97%, respectively) and white wines (96-97%; 90-97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.
酿酒行业可以通过实施数字技术来大大受益，以避免在最终葡萄酒中出现猜测和异味。本研究展示了近红外光谱（NIR）和低成本电子鼻（e-nose）与机器学习相结合来检测和评估葡萄酒缺陷的实施结果。为此，使用了红葡萄酒和白葡萄酒基酒，处理方法包括用 12 种传统上形成于葡萄酒中的缺陷对样品进行加标。结果表明，使用 NIR 和电子鼻对红葡萄酒（分别为 94-96%和 92-97%）和白葡萄酒（分别为 96-97%和 90-97%）的分类模型具有很高的准确性。实施新的和新兴的数字技术可能是酿酒行业的一个转折点，可以使其在决策方面更具预测性，并在不断变化和具有挑战性的气候下保持和提高葡萄酒质量特性。
