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Microplastic
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This FTIR hyperspectral image dataset shows a plankton environmental sample spiked with microplastics of five polymer types (PE, PP, PS, PMMA, PAN) in the 10-200 micrometer size range, used to develop and train random decision forest classifiers for automated microplastic identification. The dataset supports machine learning approaches to faster and more consistent microplastic detection in complex biological samples.
This FTIR hyperspectral image shows an environmental plankton sample which has been spiked with microplastics in the size range between 10 and 200 µm. The added particles are one of the following polymer types: polyethylene polypropylene polystyrene poly(methyl methacrylate) polyacrylonitrile This hyperspectral image has been used as a source for training data for the creation of random decision forest classifiers. For a closer description of the dataset and the sample preparation see Hufnagl et al. (2019). If you reuse this dataset 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
More Papers Like This
Microplastic
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This entry describes a FTIR hyperspectral image dataset of an environmental plankton sample spiked with microplastics ranging from 10 to 200 micrometers, covering common polymer types including polyethylene, polypropylene, and polystyrene. The dataset was used to train machine learning classifiers for automated microplastic identification.
Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images
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A random forest classification model was built using spectral subsamples of FT-IR hyperspectral data to rapidly identify the 11 most common environmental microplastic polymer types. By selecting highly discriminative wavenumbers, the model reduced computational load while maintaining strong classification accuracy.
Random Forest single channel classifier for FTIR Microplastic images
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Researchers applied random decision forest machine learning to identify microplastics from Fourier-transform infrared (FTIR) spectral data, using a small number of highly informative wavelengths to classify particles accurately and rapidly. Faster automated spectral classification could significantly increase the throughput of microplastic analysis in environmental monitoring.
Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy
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A dual-modality classification system combining FTIR spectral data and microscope images achieved 99% accuracy in automatically identifying five common microplastic polymer types. The study deployed a web application (MPsSpecClassify) that enables researchers to efficiently classify microplastics, addressing the time-consuming and error-prone nature of manual spectral analysis.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
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Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.