title: [Identification of official rhubarb samples based on NIR spectra and neural networks].
abstract: Rhubarb is one of the most widely used Chinese medicinal herbs in China. Fast and accurate identification of official and unofficial rhubarb samples is most critical for quality control of Chinese medicine production. In the present paper near-infrared reflectance spectrometry (NIRS) and artificial neural network (ANN) were combined to develop classification models for identifying 52 official and unofficial rhubarb samples. The measured spectra were compressed by wavelet transformation (WT) and then the ANN classification models were trained with the reduced-variables spectral data. The rate of correct classification was over 96%. The effects of neurons in hidden layer and the momentum were also discussed. Owing to its fast and nondestructive properties, NIRS is a promising approach to identifying Chinese medicinal herbs.
authors.affiliation: Department of Chemistry, MOE Key Lab for 3-D Information Acquisition and Application, Capital Normal University, Beijing 100037, China.
authors.forename: Yan-feng
authors.initials: YF
authors.lastname: Tang
authors.forename: Zhuo-yong
authors.initials: ZY
authors.lastname: Zhang
authors.forename: Guo-qiang
authors.initials: GQ
authors.lastname: Fan
mesh_terms: D001185:Artificial Intelligence; D056667:Biometric Identification / Q000379:methods; D004365:Drugs, Chinese Herbal / Q000737:chemistry*; D016571:Neural Networks, Computer; D012250:Rheum / Q000737:chemistry / Q000145:classification*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013057:Spectrum Analysis
chemical_list: D004365:Drugs, Chinese Herbal