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Updated: May 2, 2026

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

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

Aniwat Juhong^1,2, Bo Li^1,2, Yifan Liu^1,2

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States.

|October 13, 2025

Summary

This summary is machine-generated.

This study introduces a rapid, low-cost method for detecting Per- and polyfluoroalkyl substances (PFAS), specifically Perfluorooctanesulfonic acid (PFOS), using surface-enhanced Raman spectroscopy (SERS) and deep learning, achieving a detection limit of 0.0005 ppb.

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Area of Science:

Analytical Chemistry
Environmental Science
Biotechnology

Background:

Per- and polyfluoroalkyl substances (PFAS) are persistent, human-made chemicals with widespread industrial and consumer use.
Perfluorooctanesulfonic acid (PFOS), a common PFAS, poses health risks due to its persistence and bioaccumulation.
Conventional PFOS detection methods are time-consuming and complex.

Purpose of the Study:

To develop a cost-effective and rapid detection method for PFOS.
To leverage surface-enhanced Raman spectroscopy (SERS) and deep learning for enhanced PFOS analysis.
To establish a sensitive and accurate quantification technique for PFOS in environmental samples.

Main Methods:

Utilized gold nanoparticle SERS substrates to amplify PFOS Raman signals.
Quantified PFOS by measuring Raman peak intensities against a SERS substrate background.
Developed a demultiplexing deep learning model to denoise and extract PFOS spectra from mixed signals.

Main Results:

Achieved a low detection limit of 0.0005 ppb for PFOS using SERS.
Demonstrated high signal-to-noise ratio (SNR) PFOS spectra generation via deep learning.
Validated the deep learning model with high average cross-correlation (0.9622 ± 0.0667) and low MAE (0.0034 ± 0.0024).

Conclusions:

The SERS-based approach combined with deep learning offers a rapid and sensitive method for PFOS detection.
This innovative technique significantly improves upon conventional PFOS analysis limitations.
The developed method holds promise for efficient environmental monitoring of PFOS contamination.