Deep learning-assisted three-dimensional fluorescence difference spectroscopy for rapid identification and semi-quantification of illicit drugs in bio-fluids.

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Abstract

The fast identification and quantification of illicit drugs in bio-fluids are of great significance in clinic detection. However, existing drug detection strategies cannot fully meet clinic needs and the on-site identification and quantification of various illicit drugs in bio-fluids remains a great challenge. Here we report the development of a deep learning-assisted three-dimensional (3D) fluorescence difference spectroscopy for rapid identification and semi-quantification of illicit drugs in bio-fluids. This strategy introduces highly fluorescent silver nanoclusters into the bio-fluids with illicit drugs as signal sources. The interaction between silver nanoclusters and drug molecules changed the fluorescence performance of the mixture. Deep learning methods were applied to grasp the subtle fingerprint information from the 3D fluorescence difference spectra to identify and semi-quantify various illicit drugs in bio-fluids, including codeine, 4,5-methylene-dioxy amphetamine, 3,4-methylene dioxy methamphetamine, meperidine and methcathinone. This approach can achieve a high prediction accuracy rate of 88.07 % and a broad detection range from 2 μg/ml to 100 mg/ml. It opens up a new way for the detection of small molecules with or without fluorescence in complicated matrices.