Jia Shuai-Yun, Xu Bing, Yang Chan, Cui Xiang-Long, Shi Xin-Yuan, Qiao Yan-Jiang
Research Center of Traditional Chinese Medicine Information Engineering, Beijing University of Chinese Medicine, Beijing 100029, China.
Beijing Key Laboratory for Traditional Chinese Medicine Production Process Control and Quality Evaluation, Beijing 100029, China.
Zhongguo Zhong Yao Za Zhi. 2016 Mar;41(5):823-829. doi: 10.4268/cjcmm20160511.
The near-infrared (NIR) spectroscopy for offline monitoring of alcohol extraction process of Salvia miltiorrhiza was investigated, with high performance liquid chromatography (HPLC) determination of value for reference. The partial least squares method was adopted to establish the tanshinone ⅡA quantitative calibration model, so as to detect extraction process of Salvia miltiorrhiza. Because the differences between batches of raw materials may endanger the robustness of the original model, the simple interval calculation (SIC) was applied in updating the near-infrared quantitative model for traditional Chinese medicine extraction process for the first time, and compared with Random Selection (RS) method. SIC's final updating results showed that root mean square with cross validation (RMSECV), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of tanshinone ⅡA were 0.006 8 g•L⁻¹, 0.005 4 g•L⁻¹ and 3.14, respectively; but RS' final updating results showed that RMSECV, RMSEP and RPD were 0.006 4 g•L⁻¹, 0.006 8 g•L⁻¹ and 2.50, respectively. This study suggested that SIC is superior to RS, and provided a research foundation for quality control and monitoring of S. miltiorrhiza extraction process in the future.
研究了用于丹参醇提过程离线监测的近红外（NIR）光谱法，并以高效液相色谱（HPLC）测定值作为参考。采用偏最小二乘法建立丹参酮ⅡA定量校正模型，以检测丹参的提取过程。由于原料批次间的差异可能危及原模型的稳健性，首次将简易间隔计算（SIC）应用于更新中药提取过程的近红外定量模型，并与随机选择（RS）法进行比较。SIC的最终更新结果显示，丹参酮ⅡA的交叉验证均方根误差（RMSECV）、预测均方根误差（RMSEP）和剩余预测偏差（RPD）分别为0.006 8 g•L⁻¹、0.005 4 g•L⁻¹和3.14；而RS的最终更新结果显示，RMSECV、RMSEP和RPD分别为0.006 4 g•L⁻¹、0.006 8 g•L⁻¹和2.50。该研究表明SIC优于RS，为今后丹参提取过程的质量控制和监测提供了研究基础。
