小波包熵和Fisher判别在近红外光谱法鉴别中药大黄真伪中的应用

利用傅里叶变换近红外光谱仪采集了中药大黄的近红外漫反射光谱，提取光谱的主成分和小波包熵等特征信息，再以特征信息为依据，利用Fisher分类器对中药大黄的真伪进行了鉴别。通过比较得出：采用小波包熵特征信息建模和预测误判率比采用主成分低。用小波包熵进行特征提取和Fisher分类器相结合对中药大黄真伪进行鉴别，其建模集交叉验证的误判率为6.52%，预测集的误判率是2.04%，为中药大黄的近红外快速真伪鉴别提供了参考。

Application of Wavelet Packet Entropy and Fisher Classifier to the Identification of Medicinal Rhubarbs with Near-Infrared Spectrum

The diffused-reflectance near-infrared(NIR)spectrum of medicinal rhubarbs was collected by Fourier transform spectroscopy instrument.Principal components(PC)and wavelet packet entropy(WPE)were then calculated from the spectrum.Based on these two kinds of features,the models of identification of medicinal rhubarbs were developed using Fisher classifier.The results show that the error rates of cross-validation and prediction using WPE are all lower than those using PC.The model was built by WPE feature extraction method combined with Fisher classifier,the error rate of cross-validation is 6.52%,while that for prediction is 2.04%.The research result provides a method for identifying medicinal rhubarbs quickly.