title: [Identification of official rhubarb samples based on IR spectra and neural networks].
abstract: The Fourier transform infrared (IR) spectrometry and neural networks have been used to identification of official and un-official rhubarb samples in the present work. The IR spectra were compressed by using wavelet transform and then were normalized prior to network training. Spectra with 700 data points were compressed to 44 variables, therefore, the training process of neural networks were speed up. 52 rhubarb samples in which 25 official and 27 unofficial rhubarb samples are included have been used to network modeling. The effects of neuron number in hidden layer and momentum parameter on classification have been investigated. Results showed that about 98% rhubarb samples could be identified correctly when optimized parameters were used. This method can be useful for quality control in rhubarb-contained Chinese medicine production.
authors.affiliation: Department of Chemistry, Resources Environment and GIS Key Lab of Beijing, 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
authors.forename: Hui-Ju
authors.initials: HJ
authors.lastname: Zhu
authors.forename: Xin-Yue
authors.initials: XY
authors.lastname: Wang
mesh_terms: D004365:Drugs, Chinese Herbal / Q000032:analysis* / Q000737:chemistry / Q000592:standards; D016571:Neural Networks, Computer; D012015:Reference Standards; D012250:Rheum / Q000737:chemistry* / Q000145:classification; D013045:Species Specificity; D017550:Spectroscopy, Fourier Transform Infrared
chemical_list: D004365:Drugs, Chinese Herbal