Title:

On-Site PFAS Detection using Spectrometry and a Machine Learning Approach

Since the 1940s, Per- and polyfluoroalkyl substances, or "PFAS," have become increasingly popular in household products and other industrial applications. Studies have shown that exposure to these chemicals can lead to many adverse health effects. The current methods for detecting these chemicals are expensive and tedious and must be done in a laboratory. The new proposed system uses a low-cost, Raspberry Pi-powered spectrometer in a small, portable, 3D-printed housing. This spectrometer is used to analyze water samples. Two different methods were used for detection. One analyzes hemoglobin's absorption spectrum and emission intensity, and the other uses a carrier protein found in hemoglobin, bovine serum albumin, or BSA. When these molecules are in the presence of PFOA, their absorption spectrum and emission intensity were found to change significantly between 300-400nm. These methods were used in conjunction with the low-cost spectrometer. The results showed a significant change in peak wavelengths when BSA was in the presence of different concentrations of PFOA. When testing on hemoglobin, there was less change in peak wavelength, but there was a notable change in emission intensity at different concentrations of PFOA.

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