Rapid non-destructive detection technology for traditional Chinese medicine preparations based on machine learning: a review.
10.19540/j.cnki.cjcmm.20240903.301
- Author:
Xin-Hao WAN
1
;
Qing TAO
2
;
Zi-Qian WANG
3
;
Dong-Yin YANG
1
;
Zhi-Jian ZHONG
4
;
Xiao-Rong LUO
4
;
Ming YANG
5
;
Xue-Cheng WANG
1
;
Zhen-Feng WU
5
Author Information
1. Key Laboratory of Modern Preparation of TCM,Ministry of Education,Jiangxi University of Chinese Medicine Nanchang 330004, China.
2. Jiangxi University of Chinese Medicine Nanchang 330004, China.
3. Key Laboratory of Modern Preparation of TCM,Ministry of Education,Jiangxi University of Chinese Medicine Nanchang 330004, China Jiangxi Drug Inspection Center Nanchang 330000, China.
4. Jiangzhong Pharmaceutical Co., Ltd. Nanchang 330096, China.
5. Key Laboratory of Modern Preparation of TCM,Ministry of Education,Jiangxi University of Chinese Medicine Nanchang 330004, China National Key Laboratory of Creation of Modern Chinese Medicine with Classical Formulas Nanchang 330004, China.
- Publication Type:English Abstract
- Keywords:
hyperspectral imaging technology;
machine learning;
rapid non-destructive detection technology;
terahertz time-domain spectroscopy;
traditional Chinese medicine preparations
- MeSH:
Machine Learning;
Drugs, Chinese Herbal/analysis*;
Medicine, Chinese Traditional/methods*;
Humans;
Quality Control
- From:
China Journal of Chinese Materia Medica
2024;49(24):6541-6548
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
