| 光谱学与光谱分析 |
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| Discrimination of Varieties of Borneol Using Terahertz Spectra Based on Principal Component Analysis and Support Vector Machine |
| LI Wu, HU Bing, WANG Ming-wei* |
| Institute of Modern Optics, Key Laboratory of Optical Information Science & Technology, Nankai University, Tianjin 300071, China |
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Abstract In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer’s right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor，borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1×PC2) and 3D plots (PC1×PC2×PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model. In addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species identification of borneol in Chinese medicine.
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Received: 2014-02-16
Accepted: 2014-06-08
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Corresponding Authors:
WANG Ming-wei
E-mail: wangmingwei@nankai.edu.cn
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[1] XIONG Zhen-yu，XIAO Fu-ming，XU Xu，et al(熊振宇，肖复明，徐 旭，等). China Journal of Chinese Materia Media(中国中药杂志)，2013，38(6)：786.
[2] ZHANG Zhi-jun，RAO Wei-wen(张治军，饶伟文). Drug Standard of China(中国药物标准)，2006，7(3): 58.
[3] Lionel Duvillaret，Fre′de′ricGaret，Jean-Louis Coutaz. Appl. Opt.，1996，2(3): 739.
[4] Ioachim Pupeza，Rafal Wilk，Martin Koch. Optics Express，2007，15(7): 4335.
[5] Mei Yanliang，Jing Lingshen，Wang Guangqin. J. Phys. D: Appl. Phys.，2008, 135306(41): 6.
[6] He Ting，Shen Jingling，Liang Meiyan. Measurement，2011，44: 391.
[7] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag，1995.
[8] HE Xiao-qun(何晓群). Multivariate Statistical Analysis(多元统计分析). Beijing: China Renmin University Press(北京：中国人民大学出版社)，2008. 152.
[9] Nello Cristianini，John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kerne-l Based Learning Methods(支持向量机导论). Translated by LI Guo-zheng，WANG Meng，ZENG Hua-jun(李国正，王 猛，曾华军，译). Beijing: Publishing House of Electronic Industry( 北京: 电子工业出版社)，2004.
[10] Timothy D Dorney，Richard G Baraniuk，Daniel M Mittleman. Opt. Soc. Am. A，2001，18(7): 1562.
[11] HE Yong，LI Xiao-li(何 勇，李晓丽). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2006，24(3): 192. |
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