Detection of 1-OHPyr in human urine using SERS with injection under wet liquid–liquid self-assembled films of β-CD-coated gold nanoparticles and deep learning

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid–liquid extraction. Gold nanoparticles with β-cyclodextrin (β[email protected]) were synthesized, and homogeneous and ordered β[email protected] films were prepared through a liquid–liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 μg mL−1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of β-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.