Ultra-sensitive detection of PFASs using surface enhanced Raman scattering and machine learning: a promising approach for environmental analysis

The contamination of per- and polyfluoroalkyl substances (PFAS) in drinking water presents a significant concern and requires a simple, portable detection method. This study aims to demonstrate the effectiveness of Raman and surface-enhanced Raman scattering (SERS) spectroscopies for identifying and quantifying various PFASs in water. Experimental Raman spectra of different PFASs reveal unique characteristic peaks that enable their classification. While direct SERS measurements from silver nanorod (AgNR) substrates may not exhibit distinct PFAS characteristic peaks, the presence of PFAS on SERS substrates induces noticeable spectral changes. By integration with machine learning (ML) techniques, these SERS spectra can be used to successfully differentiate and quantify PFOA in water, achieving a limit of detection (LOD) of 1 ppt. Modifying the AgNR substrates with cysteine and 6-mercapto-1-hexanol enhances the differentiation and quantification capabilities of SERS-ML. Despite alkanethiol molecules affecting spectral features, PFAS and PFOS concentrations produce observable spectral variations. A support vector machine model achieves 93% accuracy in differentiating PFOA, PFOS, and references, independent of concentration. A support vector regression model further establishes LODs of 1 ppt for PFOA and 4.28 ppt for PFOS. By removing spectra with concentrations lower than LODs, the classification accuracy is improved to 95%. SERS combined with machine learning was employed using AgNR substrates. The method demonstrates high sensitivity and specificity in detecting and differentiating PFASs in water or methanol samples.