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

Zheng Fang; Jingxuan Xu; Shiliang Song; Mingke Lu; Huangpin Yan; Shunren Li; Siyuan Chen

doi:10.1117/12.3056972

10 January 2025 Identification method for prohibited drugs based on x-ray absorption spectroscopy and machine learning

Zheng Fang, Jingxuan Xu, Shiliang Song, Mingke Lu, Huangpin Yan, Shunren Li, Siyuan Chen

Author Affiliations +

Proceedings Volume 13507, Seventeenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2024); 1350708 (2025) https://doi.org/10.1117/12.3056972
Event: Seventeenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2024), 2024, Shaya, China

Abstract

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.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Fang, Jingxuan Xu, Shiliang Song, Mingke Lu, Huangpin Yan, Shunren Li, and Siyuan Chen "Identification method for prohibited drugs based on x-ray absorption spectroscopy and machine learning", Proc. SPIE 13507, Seventeenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2024), 1350708 (10 January 2025); https://doi.org/10.1117/12.3056972

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