Abstract
Purpose
The control of batch-to-batch quality variations remains a challenging task for herbal medicine (HM) pharmaceutics. In this study, a novel methodology consisting of Multivariate Statistical Process Control (MSPC) charts and 1H nuclear magnetic resonance (NMR) is developed for the process study of HM reflux extraction.
Methods
To well characterize the process and build an accurate model, the preprocessing methods of NMR batch data are first screened exhaustively. Statistical control charts, including partial least squares factor score, distance to the model X (DModX), and Hotelling T2, are then jointly employed as batch trajectory monitoring tools for fault monitoring. Finally, the reflux extraction process is characterized on comprehensive metabolite level through the characteristic information in the 1H NMR spectra.
Results
The dissolution, degradation, and transformation of primary and secondary metabolites in extract are traced based on the variation of metabolites’ concentration throughout the extraction process. Thirty-five metabolites are identified in the NMR spectra, and six of them are selected as chemical markers to demonstrate the impacts of abnormal operating conditions on the metabolite variation.
Conclusion
The current study demonstrates that the combination of multivariate batch modeling and 1H NMR technology realizes not only the process monitoring and fault detection based on MSPC theory but also the process characterization based on the rich qualitative and quantitative information in NMR data. Strategies demonstrated in this study are highly appealing to the research of manufacture process for HM preparations because of the improved process understanding and increased process control.
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This work was supported by the National S&T Major Project of China (2018ZX09201011-002).
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Li, W., Zhao, F., Pan, J. et al. Process Monitoring and Characterization for Extraction of Herbal Medicines Based on Proton (1H) Nuclear Magnetic Resonance Spectroscopy and Multivariate Batch Modeling: a Case Study. J Pharm Innov 18, 102–117 (2023). https://doi.org/10.1007/s12247-022-09629-x
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DOI: https://doi.org/10.1007/s12247-022-09629-x