Li Yang, Wu Zhi-Sheng, Shi Xin-Yuan, Pan Xiao-Ning, Zhang Qiao, Qiao Yan-Jiang
Zhongguo Zhong Yao Za Zhi. 2014 Oct;39(19):3753-6.
The on-line monitor for the changes in the content of baicalin in Scutellariae Radix formula particles during the extraction process was conducted by using near infrared spectroscopy (NIR). High performance liquid chromatography (HPLC) was used as a reference method. Kennard-Stone (KS) was used to divide sample sets, so as to compare different pretreatment methods. The synergy interval partial least squares (SiPLS) was used to screen out modeling wave band to establish partial least-squares models. The relative error method was applied to predict forecast set samples of Scutellariae Radix in three extraction phases. The results showed that the model established by Savitzky-Golay smoothing with 11 points (SG11 points) was the best, with the root mean square with cross validation (RMSECV), root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP) of baicalin of 0.092 7, 0.134 4 and 0.114 8, respectively, the determination coefficient R2 of greater than 0.99, and the relative error of baicalin content of less than 5%. This indicates that the on-line near infrared reflectance spectroscopy could be applied in on-line monitor and quality control of the extraction process of Scutellariae Radix formula particles.
采用近红外光谱(NIR)对黄芩配方颗粒提取过程中黄芩苷含量变化进行在线监测。以高效液相色谱(HPLC)作为参照方法。采用Kennard-Stone(KS)法划分样本集,以比较不同的预处理方法。运用协同区间偏最小二乘法(SiPLS)筛选建模波段,建立偏最小二乘模型。应用相对误差法对黄芩三个提取阶段的预测集样本进行预测。结果表明,采用11点Savitzky-Golay平滑(SG11点)建立的模型最佳,黄芩苷的交叉验证均方根误差(RMSECV)、校正均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为0.092 7、0.134 4和0.114 8,决定系数R2大于0.99,黄芩苷含量相对误差小于5%。这表明在线近红外反射光谱可应用于黄芩配方颗粒提取过程的在线监测和质量控制。