Quality Control of Herbal Medicine Based on Analytical Techniques and Machine Learning: Current Advances and Future Perspectives.

Yihong Shen; Huan Du; Fenglian Liu; Xiaolin Gou; Yiwen Tao; Xinge Lan; Jing Zhang; Yinghao Yin; Qi Li; Gang Fan

doi:10.1002/pca.70065

February 23, 2026

Quality Control of Herbal Medicine Based on Analytical Techniques and Machine Learning: Current Advances and Future Perspectives.

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Key Points

The aim is to outline the research landscape for quality control of herbal medicine using advanced techniques and machine learning.
Conducted a synthesis of existing research
Focused on analytical techniques and machine learning applications
Evaluated current standards in herbal medicine quality control
Identified gaps in current quality assurance practices
Highlighted the potential of data-driven approaches
Suggested future directions for advancing herbal medicine standards

Implication

This synthesis maps current advances in quality assurance of herbal medicine, suggesting improved standards through data-driven methods.

Abstract

This synthesis maps the current research landscape and is a foundational reference for guiding future efforts toward standardized, data-driven, and intelligent quality assurance in HMs.

Discussion

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Cite This Study

Shen et al. (2026) studied this question.

synapsesocial.com/papers/699e912ef5123be5ed04e947 https://doi.org/10.1002/pca.70065

View at Publisher

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Quality Control of Herbal Medicine Based on Analytical Techniques and Machine Learning: Current Advances and Future Perspectives.

Key Points

Implication

Abstract

Discussion

Cite This Study

Quality Control of Herbal Medicine Based on Analytical Techniques and Machine Learning: Current Advances and Future Perspectives.

Key Points

Implication

Abstract

Discussion

Cite This Study

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