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

胶体金 分析物 表面增强拉曼光谱 纳米颗粒 萃取(化学) 卷积神经网络 尿 材料科学 化学 色谱法 拉曼光谱 分析化学(期刊) 纳米技术 拉曼散射 计算机科学 人工智能 生物化学 光学 物理
作者
Mengqing Qiu,Shouguo Zheng,Pan Li,Le Tang,Qingshan Xu,Shizhuang Weng
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:290: 122238-122238 被引量:7
标识
DOI:10.1016/j.saa.2022.122238
摘要

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.
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