title: [Radial basis function networks and IR spectrometry applied for identification of official rhubarb samples].
abstract: The Fourier transform infrared spectrometry (FTIRS) and radial basis function neural network (RBF-NN) have been applied to develop classification models for identifying official and unofficial rhubarb samples. The original data were compressed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of the IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results show that the combination of IRS and ANN can be used as fast and convenient tool for identification of Chinese herbal samples.
authors.affiliation: Faculty of Chemistry, Northeast Normal University, Changchun 130024, China.
authors.forename: Shu-min
authors.initials: SM
authors.lastname: Ma
authors.forename: Si-dong
authors.initials: SD
authors.lastname: Liu
authors.forename: Zhuo-yong
authors.initials: ZY
authors.lastname: Zhang
authors.forename: Guo-qiang
authors.initials: GQ
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D004365:Drugs, Chinese Herbal / Q000032:analysis* / Q000592:standards; D016571:Neural Networks, Computer; D012015:Reference Standards; D015203:Reproducibility of Results; D012250:Rheum / Q000737:chemistry* / Q000145:classification; D013045:Species Specificity; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*
chemical_list: D004365:Drugs, Chinese Herbal