Published January 16, 2019
| Version v1
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A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
Creators
- 1. Vienna University of Technology
- 2. University of Bayreuth
Description
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
Notes
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)
Files
microplastics.mp4
Files
(29.3 MB)
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Additional details
Related works
- Cites
- 10.1007/s00216-018-1156-x (DOI)
- Is cited by
- 10.1039/C9AY00252A (DOI)
- Is supplemented by
- 10.5281/zenodo.2555732 (DOI)
References
- 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
- 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