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Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
Summary
Researchers built a database of IR spectra from microplastic particles collected across Arctic marine environments and applied machine learning methods to enable faster and less labor-intensive chemical composition analysis, identifying polymer types from spectral signatures at broad regional scales.
Understanding microplastic marine pollution is gradually becoming the most important problem for the modern environmental science. A decisive step towards its solution requires the detailed analysis of the microplastic samples with respect to their chemical composition in various marine regions. Typically, such analysis is performed by various spectral methods and takes a lot of time. In recent years, machine learning (ML) has emerged as a powerful tool for overcoming this limitation, thus paving the way for less labor-intensive investigations. However, the development of the ML models also relies on the manual collection of the polymer samples database. Up to the data, there are only a limited number of freely available polymer datasets containing spectra of the real environmental polymer. Given the fact that all of these datasets are typically used at the stage of model development and testing, the question of the final verification of the proposed models is typically left for further research. In the present work, we examine this question by focusing on the analysis of the novel database of more than 1500 microplastic particles collected from the seas in the East Siberian Arctic during expeditions in 2019-2023. All of the microplastic particles were selected from the sea surface layers using a neuston net ranging from 5 to 0.5 mm, and subsequently analyzed using a Perkin Elmer FT-IR ATR Spectrum Two in the four-time scanning mode without any preliminary mineral/organic purification of the particles. The collected database is utilized for estimating the accuracy of the microplastic classification performed by the ML models already reported in the literature. Also, an analysis of the database diversity is performed with respect to other open-source microplastic IR spectra datasets. This study was supported by the Ministry of Science and Higher Education of the Russian Federation (state contract no. 075-15-2024-629, MegaGrant). Also see: https://micro2024.sciencesconf.org/559724/document