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纸质出版日期:2012
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封银曼, 张威, 白杨, 等. 黄芩提取物有效成分的近红外光谱定量分析[J]. 中国实验方剂学杂志, 2012,18(7):84-87.
FENG Yin-man, ZHANG Wei, BAI Yang, et al. Near-infared Determination of Active Components in Scutellaria Extract[J]. Chinese journal of experimental traditional medical formulae, 2012, 18(7): 84-87.
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0.994 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。
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(R2cv) 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.
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