60种植物类中药提取物的红外光谱分析及其与寒热药性相关性的模式识别评价研究

doi:10.3964/j.issn.1000-0593(2014)01-0058-06

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摘要：对60种植物类中药提取物的红外光谱药性特征标记及其模式识别模型进行评价筛选。利用傅里叶变换红外光谱结合(linear discriminant analysis, LDA), (logistic discriminant analysis, Logistic-DA), (principal component analysis-linear discriminant analysis, PCA-LDA), (partial least-squares discriminant analysis, PLS-DA), (random forest, RF), (support vector machine, SVM)六种模式识别技术进行研究。水提取组采用加热回流提取1.5 h，无水乙醇、氯仿、石油醚提取组采用室温超声提取45 min。首先分别建立六种模式识别模型，然后采用四种统计方法综合识别，包括60味中药组内回代、60味中药10次迭代5折交叉验证、48味中药训练集、12味中药测试集。选取组内回代识别正确率、交叉验证识别正确率、组外预测正确率同时很高，且理论图谱反映寒热中药原始图谱分布特征者为适宜模型。LDA和SVM是水提取物红外光谱的适宜模式识别模型，LDA是无水乙醇提取物红外光谱的适宜模式识别模型，SVM是氯仿提取物红外光谱的适宜模式识别模型，石油醚提取识别效果不佳。结论：根据适宜识别模型，通过红外光谱数据可识别表征中药寒热成分和寒热程度的特征参数，寒热成分特征参数为与红外光谱吸收位置波谱相对应的识别模型的识别系数，识别系数大于零为寒性标记，识别系数小于零为热性标记;寒热程度特征参数为识别模型的识别得分，得分越大(正值)则寒性越强，得分越小(负值)则热性越强。

关键词：傅里叶变换红外光谱;中药;寒热药性;模式识别

Abstract：By using the Fourier transform infrared spectroscopy and linear discriminant analysis(LDA), logistic discriminant analysis(Logistic-DA), principal component analysis-linear discriminant analysis(PCA-LDA), partial least-squares discriminant analysis(PLS-DA), random forest(RF), support vector machine(SVM), infrared spectra of 60 kinds of plant extract of Chinese traditional medicine were analyzed and the identification and evaluation of characteristics of the regional markers associated with cold and heat nature were studied. Results indicated that LDA and SVM are suitable for the recognition model of water extract infrared spectral data, LDA is suitable for the identification model of anhydrous ethanol extract infrared spectral data, SVM is suitable for the identification model of chloroform extract infrared spectral data, while petroleum ether extract group recognition effect is not ideal. According to the suitable characteristic parameters identification model, data were analyzed by infrared spectroscopy, and parameters and resistance characteristics of the traditional Chinese drug composition can be obtained. Regional characteristics of these two parameters can be used to identify drug ingredients, and can also be used to indicate different degrees of resistance characteristics of traditional Chinese medicine. Component parameter is model identification coefficient corresponding to the position of spectrum and infrared, with a value greater than zero it is cold nature marker, while with a value less than zero it is heat nature marker; model identification score is a parameter reflecting the degree of cold and heat nature, the greater the score (positive), the more it is cold, while the smaller the score, the more it is hot.a parameter reflecting the degree of cold and heat,the greater the score (positive) is cold more strong, the score is small (negative) heat stronger.

Key words：Fourier transform infrared spectroscopy;Traditional Chinese medicine;Cold and heat nature of a medicine;Pattern recognition

收稿日期: 2013-04-07 修订日期: 2013-08-08

中图分类号:

R285.1

通讯作者: 王振国 E-mail: zhenguow@126.com

引用本文:

王鹏¹，周洪雷²，薛付忠³，王振国^2* . 60种植物类中药提取物的红外光谱分析及其与寒热药性相关性的模式识别评价研究 [J]. 光谱学与光谱分析, 2014, 34(01): 58-63.
WANG Peng¹, ZHOU Hong-lei², XUE Fu-zhong³, WANG Zhen-guo^2* . Analysis of Infrared Spectra of 60 Kinds of Plant Extract of Traditional Chinese Medicine and Study on the Identification and Evaluation of Characteristics of the Regional Markers Associated with Cold and Heat Nature. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(01): 58-63.

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[1] OUYANG Bing，WANG Peng，WANG Zhen-guo(欧阳兵，王鹏，王振国). Journal of Beijing University of Traditional Chinese Medicine(北京中医药大学学报)，2006，29(9)：592.
[2] FU Xian-jun，WANG Peng，WANG Zhen-guo(付先军，王鹏，王振国). World Science and Technology(Modernization of Traditional Chinese Medicine and Materia Medica(世界科学技术-中医药现代化)，2011，32(5)：94.
[3] LIU Jiang，CHEN Xing-fu，ZOU Yuan-feng(刘江，陈兴福，邹元锋). China Journal of Chinese Materia Medica(中国中药杂志)，2012，37(8)：1081.
[4] QI Fang，RONG Rong，XUE Fu-zhong(齐方，蓉蓉，薛付忠). Chinese Journal of Health Statistics(中国卫生统计)，2011，28(6)：628.
[5] Yu F，Kong L，Zou H，et al. Comb. Chem. High Throughput Screen，2010，13(10)：855．
[6] DAI Yun，YE Yan-qing，FENG Wei-bo，et al(戴云，叶艳青，冯伟博，等). Journal of Yunnan University of Nationalities(云南民族大学学报)，2007，16(4)：334.
[7] YU He-dan(于鹤丹). Journal of Mathematical Medicine(数理医药学杂志)，2006，19(1)：885.
[8] WANG Xiao-yan，LI Feng(王晓燕，李峰). Journal of Shandong University of Traditional Chinese Medicine(山东中医药大学学报)，2012，36(5)：439.
[9] WANG Xiao-yan，LI Feng，XUE Fu-zhong(王晓燕，李峰，薛付忠). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志)，2012，27(4)：943.
[10] DENG Jia-gang，LIU Jin，QIN Ji-ping，et al(邓家刚，刘进，覃吉萍，等). Chinese Journal of Spectroscopy Laboratory(光谱实验室)，2010，27(2)：741.
[11] LIU Jin，DENG Jia-gang，QIN Ji-ping，et al(刘进，邓家刚，覃吉萍，等). Lishizhen Medicine and Materia Medica Research(时珍国医国药)，2010，21(3)：561.
[12] WANG Peng, WANG Zhen-guo(王鹏，王振国). Tianjin Medical Journal(天津医药)，2012，40(10): 1.
[13] QI Fang, RONG Rong, XUE Fu-zhong(齐芳，容蓉，薛付忠). Journal of Shangdong University(山东大学学报)，2011，49(5)：147.