Hou Xiang-Mei, Zhang Lei, Yue Hong-Shui, Ju Ai-Chun, Ye Zheng-Liang
Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.
Tianjin Tasly Pride Pharmaceutical Co., Ltd., Tianjin 300402, China.
Zhongguo Zhong Yao Za Zhi. 2016 Jul;41(13):2435-2441. doi: 10.4268/cjcmm20161311.
To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing.
以近红外光谱（NIR）作为过程分析技术（PAT），研究并建立丹参酚酸大孔树脂柱色谱过程的监测方法。基于7个正常运行批次建立多元统计过程控制（MSPC）模型，并用2个测试批次（包括1个正常运行批次和1个异常运行批次）验证该模型的监测性能。结果表明，MSPC模型对柱色谱过程具有良好的监测能力。同时，采用偏最小二乘法（PLS）算法建立了3个关键质量指标（迷迭香酸、紫草酸和丹酚酸B）的近红外定量校准模型。验证结果表明该模型具有满意的预测性能。上述两种模型的联合应用可有效实现丹参酚酸大孔树脂柱色谱过程的实时监测，并可用于关键质量指标的在线分析。所建立的过程监测方法可为中药制造过程分析技术的发展提供参考。
