Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy
Abstract This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | Microplastics and Nanoplastics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43591-023-00057-3 |
| _version_ | 1797836643794681856 |
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| author | Felix Weber Andreas Zinnen Jutta Kerpen |
| author_facet | Felix Weber Andreas Zinnen Jutta Kerpen |
| author_sort | Felix Weber |
| collection | DOAJ |
| description | Abstract This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (one single model and one model per class) with the Rectified Linear Unit function (ReLU) (hidden layer) as the activation function and the sigmoid function as the output layer were evaluated and compared to human-only annotation. Based on the one-model-per-class algorithm, an approach for human–machine teaming was developed. This method makes it possible to analyze microplastic (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) spectra with high recall (≥ 99.4%) and precision (≥ 97.1%). Compared to human-only spectra annotation, the human–machine teaming reduces the researchers’ time required per sample from several hours to less than one hour. |
| first_indexed | 2024-04-09T15:13:19Z |
| format | Article |
| id | doaj.art-d177d3dd4ddf4270b530eebc9d60693c |
| institution | Directory Open Access Journal |
| issn | 2662-4966 |
| language | English |
| last_indexed | 2024-04-09T15:13:19Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Microplastics and Nanoplastics |
| spelling | doaj.art-d177d3dd4ddf4270b530eebc9d60693c2023-04-30T11:06:32ZengSpringerOpenMicroplastics and Nanoplastics2662-49662023-04-013111410.1186/s43591-023-00057-3Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopyFelix Weber0Andreas Zinnen1Jutta Kerpen2Institute for Environmental and Process Engineering, RheinMain University of Applied SciencesInstitute for Environmental and Process Engineering, RheinMain University of Applied SciencesInstitute for Environmental and Process Engineering, RheinMain University of Applied SciencesAbstract This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (one single model and one model per class) with the Rectified Linear Unit function (ReLU) (hidden layer) as the activation function and the sigmoid function as the output layer were evaluated and compared to human-only annotation. Based on the one-model-per-class algorithm, an approach for human–machine teaming was developed. This method makes it possible to analyze microplastic (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) spectra with high recall (≥ 99.4%) and precision (≥ 97.1%). Compared to human-only spectra annotation, the human–machine teaming reduces the researchers’ time required per sample from several hours to less than one hour.https://doi.org/10.1186/s43591-023-00057-3Environmental samplesMachine learningMicroplasticsµ-Raman spectroscopyWaste water |
| spellingShingle | Felix Weber Andreas Zinnen Jutta Kerpen Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy Microplastics and Nanoplastics Environmental samples Machine learning Microplastics µ-Raman spectroscopy Waste water |
| title | Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy |
| title_full | Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy |
| title_fullStr | Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy |
| title_full_unstemmed | Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy |
| title_short | Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy |
| title_sort | development of a machine learning based method for the analysis of microplastics in environmental samples using µ raman spectroscopy |
| topic | Environmental samples Machine learning Microplastics µ-Raman spectroscopy Waste water |
| url | https://doi.org/10.1186/s43591-023-00057-3 |
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