为了实现对不同品种白菜种子的快速无损鉴别, 应用近红外光谱技术获取白菜种子的光谱反射率, 首先采用变量标准化校正和多元散射校正对原始光谱进行预处理; 其次, 采用主成分分析法（PCA）对光谱数据进行聚类分析, 从定性分析的角度得到三种不同白菜种子的特征差异, 并采用连续投影算法（SPA）选取特征波长; 最后, 分别基于全波段光谱、 PCA分析得到的前3个主成分变量以及SPA算法选取的特征波长, 建立了最小二乘支持向量机（LS-SVM）和偏最小二乘判别（PLS-DA）模型进行白菜种子不同品种的鉴别。 从主成分PC1、 PC2得分图中可以看出, 主成分1和2对不同种类白菜种子具有很好的聚类作用。 基于特征波长建立的PLS-DA和LS-SVM模型的判别结果优于基于主成分变量建立的模型, 其中基于特征波长建立的LS-SVM模型识别效果最优, 建模集和预测集的品种识别率均达到100%。 结果表明, 通过SPA算法选取的6个特征波长变量能够很好的反映光谱信息, 提出的SPA算法结合LS-SVM预测模型能获得满意的分类结果, 为白菜种子品种的识别提供了一种新方法。

The varieties of cabbage seeds directly affect the yield and quality of cabbage, in order to rapidly and nondestructively identify the varieties of cabbage seeds, near infrared spectra technique were applied in this study and reflectance spectrum of the cabbage seeds was obtained. Firstly, to excavate the effective information in the spectral data and improve signal to noise ratio, the raw spectra was pre-processed with the method of standard normal variate (SNV) and multiplicative scatter correction (MSC). Secondly, principal component analysis (PCA) was used to analyze the clustering of cabbage samples, then the characteristic differentia of three cabbage varieties was obtained through qualitative analysis. Six Effective wavelengths were selected by successive projections algorithm (SPA). Finally, the full spectra variable, the first three principal components (PCs) using PCA and selected effective wavelengths using SPA were respectively set as inputs of the partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models for the classification of cabbage seeds. As can be seen from the two dimensional plot drawn with the scores of PC1 and PC2 (the first two principle components), PC1 and PC2 had a good clustering effect for different kinds of cabbage seeds. LS-SVM models performed better than PLS-DA models, the correct rates of discrimination were 100% achieved with LS-SVM models. PLS-DA and LS-SVM models built based on the selected wavelengths performed better than the models built based on the first three principal components, moreover, the SPA-LS-SVM model obtained the best results among all models, with 100% discrimination accuracy for both the calibration set and the prediction set. The overall results show that SPA can extract wavelengths, and the LS-SVM model combined with SPA can obtain optimal classification results. So the present paper could offer an alternate approach for the rapid discrimination of cabbage seeds variety.