Deep Learning-Assisted Three-Dimensional Fluorescence Difference Spectroscopy for Identification and Semiquantification of Illicit Drugs in Biofluids.

Abstract

The fast identification and quantification of illicit drugs in biofluids are of great significance in clinical detection. However, existing drug detection strategies cannot fully meet clinical needs, and the on-site identification and quantification of various illicit drugs in biofluids remain a great challenge. Here, we report the development of a deep learning-assisted three-dimensional (3D) fluorescence difference spectroscopy for rapid identification and semiquantification of illicit drugs in biofluids. This strategy introduces highly fluorescent silver nanoclusters into the biofluids 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 semiquantify various illicit drugs in biofluids, 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 matrixes.