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A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers

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Abstract

A new yet little understood threat to our ecosystems is microplastics. These microscopic particles accumulate in our oceans and in the end may find their way into the food chain. Even though their origin and the laws governing their formation have become ever more clear fast and reliable methodologies for their analysis and identification are still lacking or at an early stage of development. The first automatic approaches to analyze μFTIR images of microplastics which have been enriched on membrane filters are promising and provide the impetus to put further effort into their development. In this paper we present a methodology which allows discrimination between different polymer types and measurement of their abundance and their size distributions with high accuracy. In particular we apply random decision forest classifiers and compute a multiclass model for the polymers polyethylene, polypropylene, poly(methyl methacrylate), polyacrylonitrile and polystyrene. Further classification results of the analyzed μFTIR images are given for comparability. The study also briefly discusses common issues that can arise in classification such as the curse of dimensionality and label noise.

Graphical abstract: A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers

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Publication details

The article was received on 02 Feb 2019, accepted on 25 Mar 2019 and first published on 26 Mar 2019


Article type: Paper
DOI: 10.1039/C9AY00252A
Citation: Anal. Methods, 2019, Advance Article
  • Open access: Creative Commons BY license
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    A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers

    B. Hufnagl, D. Steiner, E. Renner, M. G. J. Löder, C. Laforsch and H. Lohninger, Anal. Methods, 2019, Advance Article , DOI: 10.1039/C9AY00252A

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