基于波长优选的小柴胡颗粒黄芩苷近红外校正模型建立

NIR Calibration Model to Detect Baicalin Content of Xiaochaihu Granules by Using Characteristic Spectrum Selection

目的:为提高小柴胡颗粒中黄芩苷近红外校正模型的准确性和预测精度。方法:基于模型的校正均方根误差(RMSEC)、预测均方根误差(RMSEP)、预测平均相对误差(PMRE)和模型对预测集的解释能力(Q2)参数,对比评价竞争自适应重加权法(CARS),蒙特卡洛-无信息变量消除法(MC-UVE),遗传算法(GA),子窗口重排(SPA)算法筛选和全波长变量,采用模群集群分析(MPA)+偏最小二乘法(PLS)方法的建模效果。结果:校正模型准确性和预测精度:CARS>MC-UVE>GA>全波长变量>SPA;CARS算法所建立校正模型预测均方根误差为1.700 4,决定系数R2为0.908 7,在α=0.05水平经配对t检验,50个外部验证样品实测值与预测值间无显著差异。结论:CARS算法筛选波长变量有效简化模型,提高模型预测的准确性和精度,适于小柴胡颗粒中黄芩苷含量的快速、无损检测。

Objective： To improve the precision and accuracy of near-infrared spectroscopy（ NIR）calibration model for determination of baicalin content in Xiaochaihu granules. Method： Four NIR characteristic spectrum selection methods were used including competitive adaptive reweighted sampling method（ CARS）,monte carlo uninformative variables elimination（ MC-UVE）, genetic algorithm（ GA） and subwindow permutation analysis（ SPA）. Model population analysis（ MPA） combined with partial least squares（ PLS） was used to build five types of models,including CARS-PLS,MCUVE-PLS,GA-PLS,SPA-PLS and all NIR wavelength-PLS. Four model parameters including root mean square error of calibration（ RMSEC）,root mean square error of prediction（ RMSEP）,prediction mean relative error（ PMRE） and the capability of model interpretation for testing set（ Q2）were analyzed to evaluate the precision and accuracy of those NIR calibration models. Result： precision and accuracy of the calibration models： CARS MC-UVE GA all NIR wavelength SPA. The values of RMSEP and decisive coefficients of the calibration models were 1. 700 4 and 0. 908 7 respectively,whose characteristic spectrum had been selected by CARS. According to the result of paired-t tests at the level of α of 0. 05,there was nosignificant difference between prediction values and measure values in 50 samples. Conclusion： CARS algorithm to screen wavelength can be used to effectively simplify the models,and improve the precision and accuracy of the models,suitable for fast and nondestructive detecting the baicalin content of Xiaochaihu granules.