Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning

Abstract

Per- and polyfluoroalkyl substances (PFAS) are a large group of human-made chemicals that have been widely used in industry and consumer products. Perfluorooctanesulfonic acid (PFOS) is a ubiquitous type of PFAS, which is extremely stable chemicals that have been persistent in the environment for many years. The accumulation of PFOS in the human body can lead to various unfavorable health issues related to the immune, metabolic, and endocrine systems. The conventional PFOS detection method utilizes liquid chromatography coupled with a mass spectroscopy system that typically involves a lengthy and complex procedure. Herein, we propose to develop a low-cost and rapid test approach based on surface-enhanced Raman spectroscopy (SERS) and deep learning for PFOS detection. The gold nanoparticle SERS substrates utilized in this study can significantly enhance the Raman signal of PFOS in solution at a low concentration. PFOS detection and quantification in water using the SERS-based substrate are carried out by measuring Raman peak intensities of PFOS in solution at a range of low concentrations and comparing them to the signal of a blank SERS substrate background. The results show that the SERS substrate can achieve a detection limit as low as 0.0005 ppb. In addition, we propose a demultiplexing deep learning model, which can generate high signal-to-noise ratio (SNR) PFOS spectra from the noisy mixture of PFOS and background Raman spectra. Average cross-correlation and mean absolute error (MAE) are utilized to evaluate the similarity between the demultiplexed and denoised PFOS Raman spectra (output of deep learning) and their ground truths. The proposed model can achieve an encouraging result with high average cross-correlation and low average MAE of 0.9622 ± 0.0667 and 0.0034 ± 0.0024, respectively.