为保证在摘酒过程中基酒分段的准确，研究通过利用傅里叶变换近红外设备对整个摘酒过程中的基酒样品进行光谱采集，使用支持向量机（SVM）对最优预处理的基酒光谱建立基酒分段模型，其模型训练集的正确率为93.02%，测试集判别率为90.08%。为减少建模时间和提高模型的可靠性，使用核主成分分析（KPCA）对基酒光谱数据降维，并对此建立基酒分段模型。其训练集正确率为94.81%，测试集判别率为90.75%，相比无KPCA分析时的分段模型训练集高1.79%，测试集高0.67%。为进一步提高模型的判别能力，使用马氏距离（MD）剔除了降维后的异常数据样品，创建的基酒分段模型训练集对基酒段数的正确率为98.72%，测试集正确率为98.75%。剔除异常样品后的分段模型的训练集正确率提高了3.91%，测试集判别率提高了8%。以上研究表明了KPCA+MD+ SVM基酒分段模型能对基酒进行快速判别，为近红外光谱在自动化摘酒方面提供了一种理论可能。

To ensure the accuracy of base liquor segmentation in the process of liquor picking, the spectra of base liquor samples during the whole liquor picking process were collected using Fourier transform near-infrared spectroscopy, and support vector machines were used to establish a segmentation model for the spectrum of the base liquor with the optimal pretreatment. The accuracy of the model training set was 93.02%, and the discrimination rate for the test set was 90.08%. To reduce the modeling time and increase the reliability of the model, kernel principal component analysis was used to reduce the dimensionality of the base liquor spectral data. A segmentation model for the base liquor was established. The accuracy of the model for the training set was 94.81%, and the discrimination rate for the test set was 90.75%, which were 1.79% and 0.67% higher than those of the model without kernel principal component analysis, respectively. To further improve the discrimination ability of the model, the Mahalanobis distance was used to eliminate abnormal data samples after dimensionality reduction. The accuracy of the new model for the training set in terms of the number of base liquor segments was 98.72%, and the accuracy for the test set was 98.75%. The accuracy for the training set and discrimination rate for the test set were improved by 3.91% and 8%, respectively. Thus, the kernel principal component analysis + Mahalanobis distance + support vector machine-based liquor segmentation model can quickly distinguish base liquor, suggesting that near infrared spectroscopy can be applied in automatic liquor picking.

四川省重大科技专项项目(2018GZDZX0045)；四川省科技成果转移转化示范项目(2020ZHCG0040)；四川省科技计划项目(2016SZ0074)