10.5061/dryad.62n8p0q
https://zenodo.org/records/4957023
oai:zenodo.org:4957023
Klug, Katherine
Katherine
Klug
University of Arizona
Jennings, Christian
Christian
Jennings
University of Arizona
Lytal, Nicholas
Nicholas
Lytal
University of Arizona
An, Lingling
Lingling
An
University of Arizona
Yoon, Jeong-Yeol
Jeong-Yeol
Yoon
University of Arizona
Data from: Mie scattering and microparticle based characterization of heavy metal ions and classification by statistical inference methods
Zenodo
2019
support vector machines
polystyrene
statistical classification
heavy metal
light scattering
2019-04-10
10.1098/rsos.190001
https://zenodo.org/communities/dryad
Creative Commons Zero v1.0 Universal
A 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 μm, 0.75 μm, and 0.40 μm were 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.
Raw scattering data
STATA functions for LDA
R code for SVM