Identification of Official Rhubarb Samples Based on IR Spectra and Neural Networks
TANG Yan-feng1, ZHANG Zhuo-yong1*, FAN Guo-qiang2, ZHU Hui-ju1，WANG Xin-yue1
1. Department of Chemistry，Resources Environment and GIS Key Lab of Beijing, Capital Normal University, Beijing 100037, China 2. Institute for Chinese Medicine, Beijing Tongrentang Group Co. Ltd., Beijing 100011, China
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.
汤彦丰1，张卓勇1*，范国强2，朱惠菊1，王新越1 . 基于大黄的红外光谱的人工神经网络鉴别研究[J]. 光谱学与光谱分析, 2005, 25(05): 715-718.
TANG Yan-feng1, ZHANG Zhuo-yong1*, FAN Guo-qiang2, ZHU Hui-ju1，WANG Xin-yue1 . Identification of Official Rhubarb Samples Based on IR Spectra and Neural Networks. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(05): 715-718.
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