Multi-class machine learning classification of PFAS in environmental water samples: a blinded test of performance on unknowns-Reference-Cited by-同舟云学术

Multi-class machine learning classification of PFAS in environmental water samples: a blinded test of performance on unknowns

Published:2024 Issue: Volume: Page:
ISSN:2754-7000
Container-title:Environmental Science: Advances
language:en
Short-container-title:Environ. Sci.: Adv.

Author:

Kibbey Tohren C. G.¹^ORCID,O'Carroll Denis M.²^ORCID,Safulko Andrew³^ORCID,Coyle Greg⁴

Affiliation:

1. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA

2. School of Civil and Environmental Engineering, Water Research Centre, University of New South Wales, Sydney, NSW 2052, Australia

3. Brown and Caldwell, Lakewood, Colorado 80401, USA

4. Brown and Caldwell, Andover, Massachusetts 01810, USA

Abstract

A multi-class method was developed to identify PFAS origin based on chemical composition, and performance of the method was evaluated in a blinded test against unknowns. The method showed great promise in its ability to recognize sample origin.

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2024/VA/D3VA00266G

Reference16 articles.

1. Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples

2. Source allocation of per- and polyfluoroalkyl substances (PFAS) with supervised machine learning: Classification performance and the role of feature selection in an expanded dataset

3. Predicting the relationship between PFAS component signatures in water and non-water phases through mathematical transformation: Application to machine learning classification

4. Integration of Per- and Polyfluoroalkyl Substance (PFAS) Fingerprints in Fish with Machine Learning for PFAS Source Tracking in Surface Water