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

Title data

Hufnagl, Benedikt ; Steiner, Dieter ; Renner, Elisabeth ; Löder, Martin ; 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 titleProject'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 LösungsansätzeDFG Project Number 391977956 – SFB 1357
PLAWES – The Journey of Microplastics from the River Weser to the North SeaBMBF 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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Animal Ecology I
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Animal Ecology I > Chair Animal Ecology I - Univ.-Prof. Dr. Christian Laforsch
Profile Fields > Advanced Fields > Polymer and Colloid Science
Profile Fields > Advanced Fields > Ecology and the Environmental Sciences
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Research Institutions > Collaborative Research Centers, Research Unit > SFB 1357 - MIKROPLASTIK
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 500 Natural sciences
500 Science > 540 Chemistry
500 Science > 570 Life sciences, biology
Date Deposited: 21 Nov 2019 08:05
Last Modified: 21 Nov 2019 08:05
URI: https://eref.uni-bayreuth.de/id/eprint/53359