Abstract：Identification is very important for the development of Chinese traditional medicines. In recent years, rapid progress in ultrafast laser technology provides a steady and available source for terahertz pulses generation, which greatly promotes the development of THz spectroscopy and imaging technique. SVM is a method for recognition of two kinds of samples. Appling SVM to the identification of Chinese traditional medicines through THz spectrum is a new way. The experiment on three groups of Chinese traditional medicines (zhigancao and shengancao, nanchaihu and beichaihu, shandougen and beidougen) was studied. The THz frequency spectrum and absorptivity were obtained and used to construct the feature space of Chinese traditional medicines. Three kinds of SVM were build, which used three kinds of kernel functions. By comparison, a model of BP artificial neural network was constructed. The result of using three kinds of SVM and BP artificial neural network to identify the Chinese traditional medicines showed that both methods have good prediction ability, but obviously the effect of SVM is better than BP artificial neural network for small samples. Using SVM in terahertz spectrum is a efficacious way for classification of Chinese traditional medicines.
陈艳江,刘艳艳,赵国忠,王卫宁,李福利* . 基于支持向量机的中药太赫兹光谱鉴别[J]. 光谱学与光谱分析, 2009, 29(09): 2346-2350.
CHEN Yan-jiang, LIU Yan-yan, ZHAO Guo-zhong, WANG Wei-ning, LI Fu-li*. Chinese Traditional Medicine Recognition by Support Vector Machine (SVM) Terahertz Spectrum . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(09): 2346-2350.
[1] Cai Y, Brener I, et al. Appl. Phys. Lett., 1998, 73: 444. [2] Liu Haibo, Chen Yunqing, Glenn J Bastiaans, et al. Optics Express, 2006, 14: 415. [3] Hua Zhong, Albert Redo-Sanchez, Zhang X C. Optics Express, 2006, 14, 20: 913. [4] Wang S, Ferguson B, Abbott D, et al. Journal of Biological Physics, 2003, 29: 247. [5] Jiang Zhiping, Zhang X C. Appl. Phys. Lett., 1998, 72: 16. [6] SUN Su-qin, YUAN Zhi-ming, et al(孙素琴，袁子民, 等). Computers and Applied Chemistry(计算机与应用化学), 2002, 19(1): 77. [7] MA Shu-min, LIU Si-dong, ZHANG Zhuo-yong(马书民，刘思东，张卓勇). Computers and Applied Chemistry(计算机与应用化学), 2007, 24(1): 121. [8] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995. [9] CHEN Quan-sheng, et al(陈全胜，等). Acta Optica Sinica(光学学报), 2006，6(26): 933. [10] BIAN Zhao-qi, ZHANG Xue-gong(边肇祺, 张学工). Pattern Recongnition(模式识别). Beijing: Tsinghua University Press(北京：清华大学出版社)，2002. [11] Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-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. [12] http://www.csie.ntu.edu.tw/-cjlin/
