1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China 2. Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China 3. College of Science, China Agricultural University, Beijing 100094, China
Abstract：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.
赵龙莲1,2,张录达3*,李军会1,杨帆1. 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[J]. 光谱学与光谱分析, 2008, 28(04): 817-820.
ZHAO Long-lian1,2,ZHANG Lu-da3*,LI Jun-hui1,YANG Fan1. Application of Wavelet Packet Entropy and Fisher Classifier to the Identification of Medicinal Rhubarbs with Near-Infrared Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(04): 817-820.
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