Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study

Petr Dolezel, Jiri Rolecek, Daniel Honc, Dominik Stursa, Bruno Baruque Zanon

2022

Abstract

Microplastics, i.e. synthetic polymers that have particle size smaller than 5 mm, are emerging pollutants that are widespread in the environment. In order to monitor environmental pollution by microplastics, it is necessary to have available rapid screening techniques, which provide the accurate information about the quality (type of polymer) and quantity (amount). Spectroscopy is an indispensable method, if precise classification of individual polymers in microplastics is required. In order to contribute to the topic of autonomous spectra matching when using spectroscopy, we decided to demonstrate the quality and efficiency of neural networks. We adopted three neural network architectures, and we tested them for application to spectra matching. In order to keep our study transparent, we use publicly available dataset of FTIR spectra. Furthermore, we performed a deep statistical analysis of all the architectures performance and efficiency to show the suitability of neural networks for spectra matching. The results presented at the end of this article indicated the overall suitability of the selected neural network architectures for spectra matching in microplastics classification.

Paper Citation

in Harvard Style

Dolezel P., Rolecek J., Honc D., Stursa D. and Zanon B. (2022). Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS; ISBN 978-989-758-552-4, SciTePress, pages 283-289. DOI: 10.5220/0010986900003123

in Bibtex Style

@conference{biosignals22,
author={Petr Dolezel and Jiri Rolecek and Daniel Honc and Dominik Stursa and Bruno Baruque Zanon},
title={Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS},
year={2022},
pages={283-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010986900003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS
TI - Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study
SN - 978-989-758-552-4
AU - Dolezel P.
AU - Rolecek J.
AU - Honc D.
AU - Stursa D.
AU - Zanon B.
PY - 2022
SP - 283
EP - 289
DO - 10.5220/0010986900003123
PB - SciTePress