Machine Learning-Based Quality Evaluation and Authenticity Identification of Traditional Chinese Medicinal Materials
Keywords:
traditional Chinese medicine; machine learning; authenticity identification; quality evaluation; data infrastructure; system governanceAbstract
The authentication and quality evaluation of traditional Chinese medicinal materials remain deeply challenging because these materials exhibit complex biological, chemical, and geographic variability, while commercial supply chains are vulnerable to substitution, adulteration, and mislabeling. Machine learning offers a systematic pathway to integrate heterogeneous analytical signals derived from spectroscopy, chromatography, imaging, genomic markers, and chemical fingerprinting. However, real-world deployment demands attention to far more than predictive accuracy. This paper presents a system-level examination of machine learning architectures for medicinal material quality evaluation and authenticity identification. It discusses data infrastructure, multi-source sensing, representation learning, interpretability, deployment trade-offs, governance, fairness, and long-term sustainability. The analysis emphasizes that robust systems must balance high-dimensional model capacity against operational constraints such as limited reference samples, evolving adulteration patterns, regulatory traceability, and cross-regional fairness. Deep neural networks, ensemble classifiers, sequence models, and attention-based architectures all provide distinct advantages depending on the sensing modality and reliability requirements. At the same time, interpretability tools and data provenance mechanisms are essential for regulatory acceptance and scientific credibility. The paper therefore foregrounds socio-technical integration rather than isolated algorithmic performance. By treating quality evaluation as a data-intensive infrastructure problem, the discussion offers architectural guidance for researchers, standards bodies, pharmaceutical regulators, and supply chain stakeholders seeking to build trustworthy machine learning systems for traditional medicine.
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