AI-Enabled Metabolite Marker Discovery for Authentication and Quality Control of Herbal Medicines

Abstract

The authentication and quality control of herbal medicines present persistent analytical and regulatory challenges because herbal materials exhibit complex chemical profiles, taxonomic ambiguity, and substantial variation arising from geographic origin, cultivation practice, harvest timing, and post-harvest processing. Metabolomics has emerged as a powerful strategy for capturing this chemical complexity, yet the transformation of high-dimensional spectral data into reliable, operationally usable marker panels remains difficult. Artificial intelligence offers considerable promise for metabolite marker discovery by learning latent patterns from large analytical datasets, but its deployment in herbal medicine quality systems requires attention to far more than predictive accuracy. This paper examines the system-level architecture of AI-enabled marker discovery, emphasizing data infrastructure, standardization, modeling trade-offs, interpretability, integration into authentication workflows, robustness, fairness, governance, and sustainable deployment. It argues that AI-based marker discovery should be understood as a socio-technical infrastructure rather than as an isolated computational exercise. The discussion connects analytical chemistry, machine learning, regulatory science, and supply chain governance to identify structural conditions under which AI can produce defensible and durable markers for herbal authentication. Particular attention is given to the need for metadata completeness, transparent model explanations, staged validation across biological and operational variability, and alignment with international regulatory expectations. The paper concludes by outlining forward-looking strategies for federated data governance, lightweight model deployment, and continuous post-market surveillance in distributed herbal supply chains.