2023
DOI: 10.1021/acs.est.3c03770
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Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints

Nayantara T. Joseph,
Trever Schwichtenberg,
Dunping Cao
et al.
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Cited by 7 publications
(6 citation statements)
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“…(ref. 4 and 11–16)). Some proposed methods have potential pitfalls, such as susceptibility to changing PFAS composition with transport or transformation of precursors, or potential challenges associated with detection limits, where specific compounds are too low in concentration to be detected in some samples.…”
Section: Resultsmentioning
confidence: 97%
“…(ref. 4 and 11–16)). Some proposed methods have potential pitfalls, such as susceptibility to changing PFAS composition with transport or transformation of precursors, or potential challenges associated with detection limits, where specific compounds are too low in concentration to be detected in some samples.…”
Section: Resultsmentioning
confidence: 97%
“…RASER has been used to process mass spectra of CP-containing household plastic, electronic cables and consumer products (Mendo Diaz et al, 2023). Such data can be further processed to obtain characteristic fingerprints of CPs and as recently shown for polyfluoroalkyl substances (Joseph et al, 2023). We hypothesize that CPs and COs can be released from such common plastic items during direct contact with humans.…”
Section: Introductionmentioning
confidence: 78%
“…Several new PFAS methods have been published in the last two years, including two using machine learning tools (which, of course, is a hot field now with many applications). First, Joseph et al applied machine learning with target and suspect screening to discover new PFAS source fingerprints . Machine-learning classifiers were used to identify PFAS that were diagnostic of different sources, including AFFF-impacted groundwater, landfill leachate, biosolids leachate, municipal WWTP effluent, and pulp-and-paper mill wastewater effluent.…”
Section: Methodsmentioning
confidence: 99%
“…First, Joseph et al applied machine learning with target and suspect screening to discover new PFAS source fingerprints. 52 Machine-learning classifiers were used to identify PFAS that were diagnostic of different sources, including AFFF-impacted groundwater, landfill leachate, biosolids leachate, municipal WWTP effluent, and pulp-andpaper mill wastewater effluent. Results showed that two target PFAS (5:3 and 6:2 fluorotelomer carboxylic acids) and two suspect PFAS (4:2 fluorotelomer-thia-acetic acid and Nmethylperfluoropropane sulfonamido acetic acid) were diagnostic of landfill leachate and fipronil (a fluorinated insecticide) was diagnostic of WWTP effluent.…”
mentioning
confidence: 99%