AI/ML-Based Quality Control Testing for Standardization of Herbals

Abstract: The aggregation of Artificial Intelligence (AI) and Machine Learning (ML) in herbal formulation presents a significant opportunity to improve herbal drug discovery, tailor medicine to individual needs, and expand healthcare access. The growing demand for herbal products highlights the necessity for robust quality control measures to ensure their safety and efficacy. This study presents an innovative AI-ML-based method for quality control testing in herbal standardization, employing ML algorithms to evaluate complex phytochemical profiles. It emphasizes the potential of an ML-based approach to standardize herbal formulations through advanced quality control testing. The study involves the compilation of various AI-ML-based techniques-or existing techniques integrated with AI-ML systems-for analyzing complex datasets comprising chemical profiles, spectral data, and chromatographic results from various herbal samples. The AI-ML models effectively identify critical bioactive compounds and detect adulteration, thereby improving the accuracy of quality assessment. By training these models on a wide variety of herbal species, high predictive accuracy can be achieved, underscoring the method's applicability in realworld settings. These novel techniques contribute to enhancing the quality control process, assist in meeting regulatory requirements, and enhance consumer safety. Based on the literature, it is suggested that integrating AI-based methods into herbal standardization can significantly enhance product reliability and promote market confidence.