at Google ScholarTitle data
Hufnagl, Benedikt ; Steiner, Dieter ; Renner, Elisabeth ; Löder, Martin G. J. ; Laforsch, Christian ; Lohninger, Hans:
A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers.
In: Analytical Methods.
Vol. 11
(2019)
Issue 17
.
- pp. 2277-2285.
ISSN 1759-9679
DOI: https://doi.org/10.1039/C9AY00252A
Project information
| Project title: |
Project's official title Project's id Sonderforschungsbereich 1357 Mikroplastik
Verständnis der Mechanismen und Prozesse der biologischen Effekte, des Transports und der Bildung: Von Modell- zu komplexen Systemen als Grundlage neuer Lösungsansätze DFG Project Number 391977956 – SFB 1357 PLAWES – The Journey of Microplastics from the River Weser to the North Sea BMBF Project PLAWES, grant 03F0789A |
|---|---|
| Project financing: |
Bundesministerium für Bildung und Forschung Deutsche Forschungsgemeinschaft |
Abstract in another language
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.