The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

中文翻译：

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使用机器学习和高光谱成像预测波特酒年份葡萄中的糖含量

波特酒的高品质是一系列酿酒操作的结果，如收获、浸渍、发酵、提取和陈酿。这些阶段需要适当的监测和控制，以始终如一地实现所需的葡萄酒特性。目前的工作重点是收获阶段，葡萄的含糖量作为关键成熟度参数之一起着关键作用。我们的方法利用高光谱成像技术从酿酒葡萄浆果中快速提取信息；收集到的光谱被送入机器学习算法，产生糖水平的估计值。一致的预测能力对于确定收获日期以及选择最好的葡萄来生产特定的高品质葡萄酒非常重要。我们比较了四种不同的机器学习方法（包括深度学习），评估了它们对训练过程中未包括的不同年份和品种的泛化能力。岭回归、偏最小二乘法、神经网络和卷积神经网络是进行这种比较的方法。结果表明，估计模型可以成功地从高光谱数据预测糖含量，卷积神经网络优于其他方法。