MULTIMODAL ARTIFICIAL INTELLIGENCE FOR AUTHENTICATION AND QUALITY ASSURANCE OF RASASHASTRA RAW MATERIALS: A REVIEW

Dr. Pathan Saniya Khan, Dr. Shahadat Khan*, Dr. Ravi Pratap Singh

Rasashastra, the Ayurvedic branch of alchemy and herbo-mineral therapeutics, relies on raw materials, including metals, minerals, gemstones, and toxic herbs, which require rigorous authentication before processing. Adulteration, substitution, and misidentification pose safety risks to consumers. Current quality assurance methods (organoleptic, physicochemical, and chromatographic) are often destructive, time-consuming-, or require expert interpretation. Multimodal artificial intelligence integrates data from multiple analytical sources, including spectroscopy, imaging, elemental analysis, and traditional organoleptic parameters, to enable rapid and non-destructive authentication. This review examines existing analytical techniques for Rasashastra raw materials, evaluates AI models applied to similar pharmaceutical and food authentication tasks, and proposes a multimodal AI framework combining near-infrared- spectroscopy, laser-induced- breakdown spectroscopy, high-resolutionimaging, and machine learning classifiers (support vector machines, convolutional neural networks, ensemble methods). Performance metrics from comparable applications (accuracy, 92–98%; sensitivity, 89–96%) suggest feasibility. Validation pathways, regulatory considerations, and integration into good manufacturing practice workflows are also discussed. This framework offers a pathway toward objective, reproducible, and real-time- raw material authentication for Rasashastra manufacturing.