Chapter 5: AI in Crude Drug Identification

Author

Mrs. Udaya Kumari Tula,

Research Scientist, DSK Biopharma Inc., Morrisville, North Carolina, USA

Abstract

The authentication of crude drugs serves as the primary checkpoint for safety in the herbal industry, transitioning from subjective human sensory evaluation to objective, high-throughput computational analysis. Artificial Intelligence, specifically Computer Vision, digitizes the expert taxonomist's eye by utilizing Convolutional Neural Networks (CNNs) to classify botanical samples based on intricate morphological features. These deep learning architectures analyze macroscopic textures and microscopic structures, such as pollen grains and cellular anatomy, to distinguish genuine species from look-alikes with high precision. Beyond visual inspection, AI-driven chemometrics revolutionizes quality control by interpreting complex chemical fingerprints generated via HPLC, GC-MS, and vibrational spectroscopy. Machine learning algorithms untangle these multivariate datasets, identifying specific spectral signatures that correlate with quality and potency, effectively mapping the chemical diversity of the sample. This data-driven approach excels at detecting adulteration and substitution, using one-class classifiers to flag anomalies that deviate from the established chemical norm. Implementing these automated verification systems secures the global supply chain, ensuring that raw materials entering the production line are authentic, safe, and chemically consistent with regulatory standards, thus preventing economic fraud and public health risks associated with misidentified herbs.

Keywords: Computer Vision, Convolutional Neural Networks (CNNs), Chemometrics, Semantic Segmentation, Data Fusion, Adulteration Detection

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