黄芩提取物有效成分的近红外光谱定量分析

目的:利用黄芩提取物样品的近红外漫反射光谱( NIRS)信息,建立能够快速分析其3种有效成分含量的校正模型.方法:共收集12个不同厂家的100批样品,其中80批样品作为校正集,20批样品作为验证集,结合偏最小二乘法(PLS),建立了黄芩提取物中黄芩苷、黄芩素和汉黄芩素3种有效成分的近红外定量校正模型.结果:3个校正模型的建模效果均较好,交叉检验决定系数(R2CV)分别为o.994 8,0.998 7,0.994 8,校正均方差(RMSEC)分别为0.440,0.022 5,0.011 1,交互验证均方差(RMSECV)分别为2.259,0.055 3,0.048 3.用验证样品进行外部验证,预测相关系数(r2)分别为0.998 2,0.996 5,0.990 9,预测均方差(RMSEP)分别为0.486,0.027 1,0.011 0.结论:结果表明,近红外光谱技术可对黄芩提取物中黄芩苷、黄芩素和汉黄芩素含量进行简便、快速、准确分析.

Near-infared Determination of Active Components in Scutellaria Extract

Objective: To rapidly analyse the three active components in Scutellaria extract by establishing calibration models with near-infrared reflectance spectroscopy(NIRS). Method: One hundred batches of Scutellaria extract samples from 12 different pharmaceutical factories were collected and they were divided into a calibration set(80 samples) and a validation set(20 samples).In combination with the partical least square(PLS),the quantitative calibration models were established for baicalin, baicalein and wogonin. Result: All models had great calibration performance. The correlation coefficients of cross-validation(R²_cv) were 0.994 8, 0.998 7 and 0.994 8,the root-mean-square error of calibration(RMSEC) were 0.440,0.022 5 and 0.011 1, the root-mean-square error of cross-validation(RMSECV) were 2.259,0.055 3 and 0.048 3.The rest 20 samples were used to evaluate the performances of the models,the correlation coefficients of prediction(r²) were 0.988 2, 0.996 5 and 0.990 9 and the root-mean-square error of prediction(RMSEP) were 0.486,0.027 1 and 0.011 0. Conclusion: The results indicated that the NIRS was simply; rapidly and exactly method for analysis of baicalin; baicalein and wogonin contents in Scutellaria extract.