Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

Abstract

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

Keywords:

• Data processing
• data fusion
• deep learning
• data fusion strategy
• machine learning
• traditional Chinese medicine
• quality assessment

Authors’ contributions

Rong Ding collected the relevant works of literature and compiled the review. Lianhui Yu and Chenghui Wang were involved in the data collection and visualization. Shihong Zhong and Rui Gu were involved in the conception, supervision, manuscript review, and editing.

Disclosure statement

The author declares that there are no competing interests.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 82073964), National Natural Science Foundation of China (No. 82274208), Key R&D Projects of Sichuan Science and Technology Department (No. 20ZDYF2376), and Nationalities Introduces Talented Research Startup Project of Southwest Minzu University (No. RQD2021055).