Rapid detection of perfluorooctanoic acid by surface enhanced Raman spectroscopy and deep learning

Perfluorooctanoic acid (PFOA) has received increasing concerns in recent years due to its wide distribution and potential toxicity. Existing detection techniques of PFOA require complex pre-treatment, therefore often taking several hours. Here, we developed a rapid PFOA detection mode to detect approximate concentrations of PFOA (ranging from 10^-15 to 10^-3 mol/L) in deionized water, and detecting one sample takes only 20 min. The detection mode was achieved using a deep learning model trained by a large surface enhanced Raman spectra dataset, based on the agglomeration of PFOA with crystal violet. In addition, transfer learning approach was used to fine tune the model, the fine-tuned model was generalizable across water samples with different impurities and environments to determine whether meet the safety standards of PFOA, the accuracy was 96.25 % and 94.67 % for tap water and lake water samples, respectively. The mechanism and specificity of the detection mode were further confirmed by molecular dynamics simulation. Our work provides a promising solution for PFOA detection, especially in the context of the increasingly widespread application of PFOA.