A feasibility research on the monitoring of traditional Chinese medicine production process using NIR-based multivariate process trajectories

作者：Li, Wenlong; Han, Haifan; Cheng, Zhiwei; Zhang, Yan; Liu, Shaoyong; Qu, Haibin^*

来源：Sensors and Actuators B: Chemical , 2016, 231: 313-323.

DOI：10.1016/j.snb.2016.03.023

• Multivariate statistical process control
• Process analytical technology
• Batch-to-batch reproducibility
• In-line near-infrared spectroscopy
• Process trajectory
• Traditional Chinese medicine

Traditional Chinese medicine (TCM) products are usually manufactured through batch processes. To improve the batch-to-batch reproducibility, practical approaches for the real-time monitoring of batch evolution need to be developed. In-line near-infrared (NIR) spectroscopy combined with multivariate statistical process control (MSPC) method, as an efficient process analytical technology, is presented for the real-time batch process monitoring. Two representative TCM technical processes, the alkaline precipitation of the compound E-Jiao oral liquid and the liquid preparation process of Tanreqing injection, were taken as examples. The NIR spectra collected in-line was "variable-wise" unfolded into two-dimensional matrix, and multi-way principal component analysis (MPCA) model were developed based on the rearranged data of the normal operation condition (NOC) batches. Three kinds of multivariate control charts (PC scores, Hotelling T-2 and DModX) were used to monitor the evolution of test batches with artificial batch variations, including the change of starting material quality attributes and abnormal operation conditions. As illustrated with test batches, the established model can identify NOC or AOC (abnormal operation condition) batches accurately, and detect different kinds of deviations from NOC batches using these control charts. The results indicated that the NIR-based multivariate process trajectories can reflect the batch-to-batch reproducibility effectively, and can also help for the diagnosis of the failure batches in the TCM production.

• 出版日期2016-8
• 单位浙江大学