AI in Detecting Adulteration in Herbal Drugs

Herbal medicines are widely used across the world because of their therapeutic value and natural origin. However, the increasing demand for herbal products has also increased the risk of adulteration, substitution, and contamination. Conventional analytical techniques such as microscopy, chromatography, and DNA barcoding are effective but often require sophisticated instrumentation, skilled personnel, and significant processing time. Artificial intelligence (AI) has recently emerged as a promising approach for rapid, accurate, and non-destructive authentication of herbal drugs. Machine learning, deep learning, computer vision, and chemometric models are being combined with spectroscopic and metabolomic techniques for efficient detection of adulterants in medicinal plants. AI-assisted methods can improve quality control, reduce human error, and support real-time monitoring in the herbal industry. This review discusses different forms of herbal drug adulteration, conventional analytical approaches, and recent advancements in AI-based authentication systems. The advantages, limitations, and future perspectives of AI technologies in herbal drug standardization are also discussed.