1. 首都师范大学化学系，北京 100048
2. 中央民族大学理学院，北京 100081
3. Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701-2979, USA
Identification of Rhubarb Samples by Terahertz Time Domain Spectroscopy Combined with Principal Component Analysis-Linear Discriminant Analysis and Support Vector Machine
WANG Jing-rong1, ZHANG Zhuo-yong1*, YANG Yu-ping2, XIANG Yu-hong1, Peter de B. HARRINGTON3
1. Department of Chemistry, Capital Normal University, Beijing 100048, China
2. College of Science, Minzu University of China, Beijing 100081,China
3. Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701-2979, USA
Abstract：Terahertz time domain spectroscopy (THz-TDS) combined with principal component analysis-linear discriminant analysis (PCA-LDA) and support vector machine (SVM) was used for identification of official rhubarb samples. Terahertz time domain transmittance spectra of 41 official and unofficial rhubarb samples were measured in time domain and then were transformed to absorption coefficients in frequency domain. Qualitative classification models of PCA-LDA and SVM were established based on the absorption coefficients and cross validated for identifying official and unofficial rhubarb samples. The predictive ability and stability of the models were evaluated using bootstrapped Latin-partitions method with 50 bootstraps and 4 Latin-partitions. Satisfactory results were obtained by using both PCA-LDA and SVM. The proposed method proved to be a convenient, non-polluting, accurate, and non-chemical treatment approach for identifying rhubarb samples. The developed procedure can be easily implemented for quality control in other herbal medicine classification and production.
Key words：Principal component analysis linear discriminant analysis; Support vector machine; Terahertz time domain spectroscopy; Rhubarb
基金资助: the National Instrumentation Program (2012YQ140005)，Natural Science Foundation of China (21275101)
通讯作者:
张卓勇
E-mail: gusto2008@vip.sina.com
作者简介: WANG Jing-rong, (1986—), master, Department of Chemistry, Capital Normal University
引用本文:
汪景荣，张卓勇，杨玉平，相玉红，Peter de B. HARRINGTON. 太赫兹时域光谱结合主成分分析线性判别和支持向量机用于大黄样品鉴定[J]. 光谱学与光谱分析, 2017, 37(05): 1606-1611.
WANG Jing-rong, ZHANG Zhuo-yong, YANG Yu-ping, XIANG Yu-hong, Peter de B. HARRINGTON. Identification of Rhubarb Samples by Terahertz Time Domain Spectroscopy Combined with Principal Component Analysis-Linear Discriminant Analysis and Support Vector Machine. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1606-1611.
[1] Chinese Pharmacopoeia Commission, Chinese Pharmacopoeia for 2010 Edition. Beijing：China Medical Science and Technology Press, 2010.
[2] Chen Yanjiang, Liu Yanyan, Zhao Guozhong, et al. Spectroscopy and Spectal Analysis, 2009, 29(9): 2346.
[3] Yang Y, Harsha S S, Shutler A J, et al. J. Pharm. Biomed. Anal., 2012, 62: 177.
[4] Zhao X L, Li J S, J. Physics: Conference Series. IOP Publishing, 2011, 276: 012233.
[5] Hua Y F, Zhang H J, Zhou H L, IEEE Trans. on Instrum. Measur., 2010, 59: 1414.
[6] Makoto O, Jun-ichi N, Jiro S I，et al. J. Pharm. Sic., 2010, 99: 4048.
[7] Cao Binghua, Fan Mengbao, Jing Shengyu. Spectroscopy and Spectral Analysis, 2010, 30(7): 1748.
[8] Hao Guohui, Guo Changsheng, Liu Jianjun，et al. Spectroscopy and Spectral Analysis, 2012, 32(5): 1184.
[9] Ma Y H, Wang Q, Li L Y. J. Quant. Spectrosc. Radiat. Transfer., 2013, 117: 7.
[10] Haddad J E, Bousquet B, Canioni L, et al. Trends in Anal. Chem., 2013, 44: 98.
[11] Wang J, Zhang Z, Zhang Z, et al. Anal. Methods, 2014, 6: 7695.
[12] Harrington P B. Trends Anal. Chem., 2006, 25: 1112.
[13] Duvillaret L, Garet F, Coutaz J L. Appl. Opt., 1999, 38: 409.
[14] Timothy D D, Richard G B, Daniel M M. J. Opt. Soc. Am. A, 2001, 1562.
[15] Feng G H. Comput. Eng. Appl., 2011, 47: 123.
[16] Wan C H, Harrington P B. Anal. Chim. Acta, 2000, 408: 1.
