Authors:
Petr Dolezel
1
;
Jiri Rolecek
1
;
Daniel Honc
1
;
Dominik Stursa
1
and
Bruno Baruque Zanon
2
Affiliations:
1
Faculty of Electrical Engineering and Informatics, University of Pardubice, Studentska 95, Pardubice, Czech Republic
;
2
Universidad de Burgos Escuela Politécnica Superior, Burgos, Castilla y Leon, Spain
Keyword(s):
Microplastics, FTIR Spectra, Spectroscopy, Neural Network, Deep Learning, Spectra Matching.
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 fo
r 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.
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