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A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers

Authors: 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

Abstract

{"references": ["Hufnagl, B., Steiner, D., Renner, L\u00f6der, M. G. J., Laforsch, C. and Lohninger, H. A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers, Analytical Methods, 2019, DOI:10.1039/C9AY00252A", "Primpke, S., Wirth, M., Lorenz, C., Gerdts, G. Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy, Anal Bioanal Chem, 410: 5131, DOI:10.1007/s00216-018-1156-x"]}

This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019). If you reuse this video please cite Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers, Analytical Methods, 2019, DOI:10.1039/C9AY00252A

Research funding was provided by Deutsche Forschungsgemeinschaft (DFG) – project number 391977956 – SFB 1357 and the German Federal Ministry of Education and Research (project PLAWES, grant 03F0789A)

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Keywords

polymer identification, microplastics, random decision forest, classification, particle detection

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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