近红外光谱技术结合竞争自适应重加权采样算法用于中药定量分析  

Quantitative Analysis of Traditional Chinese Medicine by Near-infrared Spectroscopy Combined with Competitive Adaptive Reweighted Sampling

作　　者：聂黎行[1] 戴忠[1] 马双成[1] 张晓楠[2] 解素花[2] 

机构地区：[1]中国食品药品检定研究院,北京100050 [2]北京同仁堂股份有限公司,北京100051

出　　处：《中国实验方剂学杂志》2017年第11期45-49,共5页Chinese Journal of Experimental Traditional Medical Formulae

基　　金：国家自然科学基金项目(81303194);国家发改委现代中药高技术产业发展专项[(2011)51]

摘　　要：目的:基质复杂、谱带重叠严重,影响了中药近红外定量模型的准确性。为解决以上问题,探讨竞争自适应重加权采样(Competitive adaptive reweighted sampling,CARS)变量筛选方法在中药材、中药提取物和中成药的定量分析中的应用。方法:采集葛根药材、葛根提取物和愈风宁心滴丸的近红外漫反射光谱,测定葛根素含量。分别优化光谱前处理方式,剔除奇异样本后,运用CARS法筛选出的相关变量,建立偏最小二乘法(PLS)校正模型。结果:原料、中间体和制剂的定量模型交互验证均方差(RMSECV)分别为0.35%,1.76%,0.54%,与基于全光谱建立的模型比较,原料、中间体和制剂的CARS-PLS模型的预测准确度均有提高。结论:竞争自适应重加权采样变量筛选方法可以提高模型的预测能力,并有效简化运算过程,为中药的快速、无损检测提供了新的思路。Objective： Accuracy of NIR model is restricted by complex matrix and overlapping bands of traditional Chinese medicine （TCM）. To solve the above problem, the application of competitive adaptive reweighted sampling （CARS） in quantitative analysis of Chinese medicinal materials, TCM extract and patent medicines was discussed in this paper. Method： Near-infrared diffuse reflectance spectra of Puerariae Lobatae Radix, Puerarin Lobata Radix extract and Yufeng Ningxin dripping pills were collected to determine the contents of puerarin. Then the spectra were pre-processed with optimized methods to exclude uncommon samples, and CARS method was used to screen relevant variables and establish partial least squares （PLS） correction models. Result： Root mean square error of cross-validation （RMSECV） was 0.35% , 1.76% and 0.54% , respectively in models for raw material, semi-product and finished product. As compared with the models based on the full spectra, increased accuracy was obtained from the CARS-PLS models. Conclusion： CARS could increase prediction accuracy of the models and simplify calibration, which would offer a new approach for rapid and non-destructive determination of TCM.

关 键 词：近红外光谱 愈风宁心滴丸 葛根提取物 葛根 定量分析 竞争自适应重加权采样 

分 类 号：R284.1[医药卫生—中药学]