Identification method for prohibited drugs based on x-ray absorption spectroscopy and machine learning

This study explores a new method for identifying prohibited drugs using X-ray Absorption Spectroscopy (XAS) detection and machine learning algorithms. A laboratory-developed scientific equipment was employed to obtain x-ray absorption spectra of ten different chemical substances, including various isomers of prohibited drugs. Principal Component Analysis (PCA) was then applied to extract the spectral features, minimizing data redundancy. Subsequently, the Extreme Learning Machine (ELM) combined with the Sparrow Search Algorithm (SSA) was utilized for analysis. The results demonstrate that this method can accurately identify prohibited drugs, even excelling in automatically identifying isomers. This research offers a novel and promising technique for quick non-destructive drug detection.