title: [Application of wavelet packet entropy and Fisher classifier to the identification of medicinal rhubarbs with near-infrared spectrum].
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.
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China. zll02@mails.tsinghua.edu.cn
authors.forename: Long-Lian
authors.initials: LL
authors.lastname: Zhao
authors.forename: Lu-Da
authors.initials: LD
authors.lastname: Zhang
authors.forename: Jun-Hui
authors.initials: JH
authors.lastname: Li
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
mesh_terms: D004365:Drugs, Chinese Herbal / Q000032:analysis*; D019277:Entropy; D012250:Rheum / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D004365:Drugs, Chinese Herbal