Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand.
Sensors (Basel). 2020 Jun 27;20(13):3618. doi: 10.3390/s20133618.
Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008-2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R = 0.92; slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R = 0.98; slope = 0.93) and wine color (Model 3; R = 0.99; slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.
重要的葡萄酒质量特征，如感官特征和颜色，是土壤、葡萄藤、环境、管理和酿酒实践之间复杂相互作用的产物。人工智能（AI），特别是机器学习（ML），可以提供强大的工具，通过季节评估这些复杂的相互作用及其模式，以便在接近收获和酿酒之前向葡萄酒种植者预测质量特征。本研究考虑了九个年份（2008-2016 年），使用葡萄酒的近红外光谱（NIR）以及相应的天气和管理信息作为人工神经网络（ANN）建模感官特征（分别为模型 1 和模型 2）的输入。此外，天气和管理数据被用作预测葡萄酒颜色（模型 3）的输入。结果表明，使用 NIR 对垂直年份葡萄酒的感官特征进行预测具有很高的准确性（模型 1；R = 0.92；斜率 = 0.85），而使用天气/管理数据获得了更好的模型来预测感官特征（模型 2；R = 0.98；斜率 = 0.93）和葡萄酒颜色（模型 3；R = 0.99；斜率 = 0.98）。对于所有模型，根据 ANN 特定测试，没有过度拟合的迹象。这些模型可以作为强大的工具，供葡萄酒种植者和酿酒师在接近收获和酿酒之前使用，以保持高品质和消费者可接受的特定葡萄酒风格。
