提出出了一种基于近红外光谱分析技术快速无损测定苜蓿秋眠类型的新方法.应用近红外光谱漫反射技术测定苜蓿样本的光谱并对其进行主成分分析(PCA),根据主成分的累积贡献率选取前10个主成分建立支持向量机(SVM)分类模型,并对其参数及核函数类型进行了详细的分析和讨论.试验结果表明,当c=0.339 2,g=32时,测试集的预测准确率可达98.182%,可以作为初步测定苜蓿秋眠类型的手段之一.同时,与主成分回归分析、偏最小二乘法、BP神经网络、LVQ神经网络等方法相比较的结果表明,PCA-SVM模型可以有效地解决小样本问题,且可以避免陷入局部极小.%The present study proposes a new approach to producing accurate estimates of fall dormancy (FD) in alfalfa in a rapid manner. Using near infrared spectroscopy, the approach produces results fast without causing damage to samples. Near infrared reflectance spectroscopy was applied to measuring the spectra of samples. Then principal component analysis (PCA) was conducted on the measurements. The top ten principal components were selected based on their cumulative contribution rates to build a support vector machine (SVM) model. Detailed analysis and discussions were conducted over their parameter and kernel classifications. The experiment found that when c=0. 339 2 and g=32, the accuracy of the predictions of the test set can reach 98. 182％. Therefore the approach can estimate the FD in alfalfa in a rapid and accurate manner. Moreover, it was compared with other approaches such as principal component regression, partial least squares regression, BP neural networks, and LVQ neural networks. The comparisons have shown that the PCA-SVM model can effectively address the small-sample-size problem and avoid local minimum.
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