Several dimensionality reduction techniques were applied to hyperspectral reflectance images of wine grape berries, leading a study of the machine learning models’ efficiency in the prediction of sugar content for training, validation and independent test sets, and for generalization sets with vintages not previously seen in the training phase. The results obtained across all settings were up to par, either matching or improving state-of-the-art results, and showcasing that a model capable of generalizing predictions from one vintage year to another without further training is achievable in a very accurate way. For the dimensionality reduction techniques studied, the results show that the PCA outperforms the nonlinear techniques for the case of real-world hyperspectral data while also suggesting that, for the case of hyperspectral images of wine grape berries, local nonlinear techniques more frequently have a better performance than their global nonlinear counterparts. This review highlights that more complex methods for dimensionality reduction may not be necessary for the case of hyperspectral images, since the PCA still yields the best results when using the transformed dataset for the prediction of oenological parameters.

将几种降维技术应用于酿酒葡萄浆果的高光谱反射率图像，引导了机器学习模型在预测糖含量方面的效率研究，用于训练、验证和独立测试集，以及具有以前从未见过的年份的泛化集训练阶段。在所有设置中获得的结果都达到了标准，要么匹配要么改进了最先进的结果，并展示了一种模型可以以非常准确的方式实现从一个年份到另一个年份的预测而无需进一步训练。对于所研究的降维技术，结果表明 PCA 在真实世界高光谱数据的情况下优于非线性技术，同时还表明，对于酿酒葡萄浆果的高光谱图像，局部非线性技术通常比全局非线性技术具有更好的性能。该评论强调，对于高光谱图像，可能不需要更复杂的降维方法，因为在使用转换后的数据集预测酿酒参数时，PCA 仍能产生最佳结果。