Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods
AffiliationUniv Arizona, Dept Biosyst Engn
Univ Arizona, Dept Biomed Engn
Univ Arizona, Stat Grad Interdisciplinary Program
Univ Arizona, Dept Biostat & Epidemiol
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CitationKlug, K. E., Jennings, C. M., Lytal, N., An, L., & Yoon, J. Y. (2019). Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods. Royal Society open science, 6(5), 190001.
JournalROYAL SOCIETY OPEN SCIENCE
Rights© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
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AbstractA straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 mu m was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.
NoteOpen access journal
VersionFinal published version
SponsorsU.S. National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) [DGE-1143953]; U.S. National Institutes of Health - National Institute of Environmental Health Sciences (NIH-NIEHS) [R25ES025494]; Western Alliance to Expand Student Opportunities (WAESO) at Arizona State University; U.S. National Institutes of Health -National Institute of General Medical Sciences (NIH-NIGMS) [T32GM084905]; Korea Institute of Ocean Science and Technology (KIOST)