摘要
目的:建立近红外漫反射光谱法快速测定积雪草总苷中积雪草苷的含量方法。方法:采用高效液相色谱法测定市场上流通的66批次积雪草总苷中积雪草苷的含量,同时采集近红外漫反射光谱,对原始光谱进行多元散射校正(MSC)、二阶导数(2D)和Norris导数(ND)平滑等光谱预处理方法,采用偏最小二乘回归法(PLSR)建立近红外定量校正模型,实现积雪草总苷中指标性成分积雪草苷含量的快速测定。结果:所建立的积雪草苷近红外定量校正模型,验证集相对误差RMSEP、标准偏差与标准误差的比值RPD分别为0.0352和6.47,表明所建近红外模型预测准确度高。同时,校正集均方根误差RMSEC和验证集均方根误差RMSEP接近,分别为1.17和1.25,交叉验证均方根误差RMSECV为3.43,表明所建近红外模型稳健性好。结论:所建立的积雪草苷近红外定量校正模型具有很好的预测准确度和稳健性,为市场上积雪草总苷质量的快速评价提供方法。
Objective:To establish near - infrared spectrometry quantitative models for the determination of asiaticoside in Centella Total Glucosides. Methods: The contents of asiaticoside of 66 samples of Centella Total Glueosides were determinated by high - performance liq- uid chromatography. And the near - infrared spectrogram of 66 samples of Centella Total Glucosides was collected at the same time. After smoothing pretreatment by Multiple scattering correction(MSC), the second derivative (2D)and Norris derivative (ND), partial least square regression (PLS) was used to establish near - infrared spectrometry quantitative models for the determination of asiaticoside. Results:In validation set, the relative error( RSEP), and the ratio of standard deviation and standard error of prediction were 0. 0352 and 6.47 ,respectively,which indicated that the models had good accuracy. The root mean square error (RMSEC) in calibration set and the root mean square error(RMSEP) in validation set were 1.17 and 1.25, respectively, and the root mean square error of cross validation(RMSECV) was 3.43 ,which showed that the near infrared model was robust. Conclusion :The established models have good ac-curacy and robustness,which provide an efficient approach for the fast determination of asiaticoside in Centella Total Glucosides.
出处
《世界中医药》
CAS
2013年第11期1280-1282,1286,共4页
World Chinese Medicine
基金
"重大新药创制"国家科技重大专项(编号:2010ZX09502-002)
2013北京市青年英才计划
北京中医药大学青年教师专项(编号:2012-QNJSZX009)
关键词
近红外光谱
积雪草总苷
积雪草苷
漫反射
Near-infrared spectroscopy(NIR)
Centella Total Glucosides
Asiaticoside
Diffuse reflectance