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.

中文翻译：

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基于质子 (1H) 核磁共振光谱和多变量批处理建模的草药提取过程监测和表征：案例研究

目的

批次间质量变化的控制对于草药 (HM) 药剂学来说仍然是一项具有挑战性的任务。在这项研究中，开发了一种由多变量统计过程控制 (MSPC) 图表和¹ H 核磁共振 (NMR) 组成的新方法，用于 HM 回流提取的过程研究。

方法

为了更好地表征过程并建立准确的模型，首先对核磁共振批量数据的预处理方法进行了详尽的筛选。统计控制图，包括偏最小二乘因子得分、到模型 X 的距离 (DModX) 和 Hotelling T ²，然后被联合用作故障监测的批量轨迹监测工具。^{最后，通过1} H NMR 光谱中的特征信息对回流提取过程进行综合代谢物水平的表征。

结果

基于提取过程中代谢物浓度的变化，追踪提取物中初级和次级代谢物的溶解、降解和转化。在核磁共振光谱中鉴定出35种代谢物，其中6种被选为化学标记物，以证明异常操作条件对代谢物变异的影响。

结论

目前的研究表明，多元批处理建模与¹ H NMR 技术的结合不仅实现了基于 MSPC 理论的过程监控和故障检测，而且还实现了基于 NMR 数据中丰富的定性和定量信息的过程表征。由于改进的过程理解和增加的过程控制，本研究中展示的策略对HM制剂的制造过程研究非常有吸引力。