Spectroscopic Machine Learning for Rapid Discrimination and Quality Assessment of Salvia Medicinal Resources
Keywords:
Salvia miltiorrhiza; spectroscopic machine learning; quality assessment; chemometrics; botanical authentication; data governance; sustainabilityAbstract
The rapid authentication and quality evaluation of Salvia medicinal resources remain persistent challenges because morphological similarity, variable cultivation conditions, post-harvest processing, and complex chemical composition can obscure botanical identity and therapeutic consistency. Spectroscopic platforms generate information-dense signals that are suitable for machine learning, but the translation of raw spectral responses into reliable regulatory-grade decisions requires careful attention to data architecture, algorithmic selection, robustness, and governance. This paper presents a systems-oriented analysis of spectroscopic machine learning for Salvia miltiorrhiza and related taxa. It examines spectral preprocessing, representation learning, data partitioning, model comparison, deployment infrastructure, and fairness across geographical and agronomic contexts. The discussion emphasizes that no single algorithm provides universal superiority; rather, performance emerges from the alignment between spectroscopic measurement physics, data curation strategy, and institutional data governance. The paper further considers how cell wall component markers can complement spectral models for resolving closely related Salvia species. It concludes by identifying policy and standardization pathways for integrating machine learning into the quality assessment of medicinal plant resources, with particular attention to reproducibility, auditability, and sustainable supply chains.
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