| 光谱学与光谱分析 |
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| Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network |
| PAN Sha-sha1, HUANG Fu-rong1*, XIAO Chi1, XIAN Rui-yi1, MA Zhi-guo2 |
1. Opto-electronic Department of Jinan University，Guangzhou 510632，China
2. College of Pharmacy of Jinan University，Guangzhou 510632，China |
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Abstract To explore rapid reliable methods for detection of Epicarpium citri grandis(ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy(FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected.According to the differences in tspectrum, the spectra data in the 550～1 800 cm-1 wavenumber range and 400～720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction(SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that:after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.
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Received: 2014-10-28
Accepted: 2015-02-10
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Corresponding Authors:
HUANG Fu-rong
E-mail: furong_huang@163.com
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[1] The Pharmacopoeia Committee of the People’s Republic of China(国家药典委员会). Pharmacopoeia of the People’s Republic of China(中华人民共和国药典). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2005. 90.
[2] Liang Yong, Huang Zhangfeng, Chen Hongwei, et al. Journal of Liquid Chromatography & Related Technologies, 2007, 30(3): 419.
[3] CHEN Zhi-xia, LIN Li(陈志霞，林 励). Journal of Chinese Medicinal Material(中药材), 2004, 27(8): 577.
[4] WU Song-xia(吴宋夏). Journal of Zhanjiang Medical College(湛江医学院学报), 1988, 6(2): 54.
[5] Luu H, Caple R, Kamperdick C, et al. Journal of Chemistry, 2007, 41(1): 115.
[6] Mokbel M, Suganuma T. European Food Research and Technology, 2006, 224(1): 39.
[7] DENG Shao-dong, WANG Lian-jing, et al(邓少东，王莲婧，等). Chinese Traditional and Herbal Drugs(中草药), 2013, 44(9): 1195.
[8] SU Wei-wei, WU Zhong, QUAN Jian(苏薇薇，吴 忠，全 健). Journal of Chinese Medicinal Material(中药材), 2001, 24(4): 295.
[9] HAN Feng-mei, CAI Min, CHEN Yong(韩凤梅，蔡 敏，陈 勇). Journal of Analytical Science(分析科学学报), 2004, 20(6): 647.
[10] JIANG Yan, SHEN Yi, WU Pei-yi(江 艳，沈 怡，武培怡). Progress in Chemistry(化学进展), 2007, 19(1): 173.
[11] WU Xi, YANG Yu-hong, XU Zhao-li, et al(吴 茜，杨宇虹，徐照丽，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(8): 2102.
[12] HE Miao, QUAN Yu, LI Jian-hua, et al(何 苗，全 宇，李建华，等). Chinese Journal of Health Statistics(中国卫生统计), 2006, 23(4): 293. |
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