Machine Learning Pattern Recognition for Forensic Analysis of Detected Per- and Polyfluoroalkyl Substances in Environmental Samples

Abstract

This proof of concept project explored the use of modern machine learning algorithms for forensic source allocation of detected per- and polyfluoroalkyl substances (PFAS) in environmental samples. A challenge with allocating PFAS source by composition is that PFAS composition varies substantially even from a single source, as a result of differing compound mobility, as well as transformation of precursor compounds. Work involved compiling an extensive dataset from a range of worldwide PFAS concentration data sources, and then using a portion of the dataset to train supervised machine learning classifiers to recognize patterns that can distinguish between PFAS from AFFF and non-AFFF sources. Results show that a number of very different supervised learning classifiers work extremely well for this purpose, and are able to identify AFFF in samples even in difficult-to-recognize categories of samples. The results of this project show that supervised machine learning has significant promise as a means of distinguishing between PFAS from AFFF and non-AFFF sources.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2020
Accession Number
AD1154374

Entities

People

  • Denis M O'Carroll
  • Rafal Jabrzemski
  • Tohren Kibbey

Organizations

  • University of Oklahoma

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Data Mining
  • Deep Learning
  • Department Of Defense
  • Dimensionality Reduction
  • Information Science
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Analytical Chemistry
  • Computer Vision.
  • Fire Suppression Systems Design.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks