Application of multisensor fusion technology for online monitoring of traditional Chinese medicine extraction process: A case study of Xiaochaihu capsules
10.1097/st9.0000000000000124
- Author:
Feng DING
1
;
Shaohua WU
2
;
Xingchu GONG
3
Author Information
1. Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou, China; Jinhua Institute of Zhejiang University, Jinhua, China
2. Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou, China
3. Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou, China; Jinhua Institute of Zhejiang University, Jinhua, China; National Key Laboratory of Chinese Medicine Modernization, Zhejiang University, Hangzhou, China
- Publication Type:Journal Article
- Keywords:
Data fusion;
Extraction;
Multi-sensor fusion;
Process analytical technology;
Xiaochaihu capsules
- From:
Science of Traditional Chinese Medicine
2026;4(2):163-173
- CountryChina
- Language:English
-
Abstract:
Background: Xiaochaihu capsules are a widely used traditional Chinese patent medicine. The extraction process, a critical step in their production, necessitates online monitoring to determine the levels of key indicators, thereby informing subsequent processing stages. Objective: The objective of this study was to establish online quantitative models for 6 key indicators (solid content, liquiritin, baicalin, wogonoside, glycyrrhizic acid, and saikosaponin B
) during the extraction process of Xiaochaihu capsules. This approach aims to provide a technical foundation for enhanced process control and to serve as a methodological reference for the application of multisource information fusion in monitoring the extraction of multi-herb traditional Chinese medicine formulations. Methods: Online quantitative models were developed based on multiple sensors, ultraviolet-visible spectroscopy (UV-Vis), and near-infrared spectroscopy (NIR) to monitor 6 indicators during the extraction process of Xiaochaihu capsules. Different preprocessing methods for spectroscopic data were optimized, and data fusion strategies were integrated to improve the predictive performance of the online quantitative models. Results: The optimal modeling strategies for the 6 indicators were as follows: for solid content, a second-order polynomial model was established using the measurement results from 2 sensors (pH and conductivity), with a predictive coefficient of determination (R
) of 0.9821. For baicalin, the optimal model was developed using UV-Vis spectra pretreated with the first derivative (1stD), achieving a predictive R
of 0.9841. For wogonoside, modeling was also conducted after 1stD preprocessing of the UV-Vis spectra, yielding a predictive R
of 0.9929. For liquiritin, a high-level fusion strategy combining UV-based feature extraction via the random frog (RF) algorithm and NIR-based feature extraction via competitive adaptive reweighted sampling (CARS) was used, achieving an R
of 0.9406 in the validation set. For glycyrrhizic acid, the optimal approach involved a combination of UV-CARS feature extraction and NIR-RF feature extraction, coupled with high-level data fusion, resulting in an R
of 0.9859 in the validation set. For saikosaponin B
, the optimal scheme utilized UV-RF feature extraction combined with NIR-CARS feature extraction and high-level fusion (R
= 0.7702 in the validation set). Conclusion: The integration of process analytical technologies and data fusion strategies enabled the successful establishment of online quantitative models for 6 indicators during the extraction process of Xiaochaihu capsules. Overall, the established online quantitative models effectively captured and reflected the dynamic changes of these indicators throughout the extraction process.
