Abstract
Microplastics are environmental contaminants that put marine and aquatic ecosystems at serious risk. Monitoring microplastics is necessary to understand the level of microplastic pollution in our environment. However, the lack of a standard protocol for quantifying and classifying microplastics causes problems in the reliability and comparability of results. Previous literature has employed deep learning models to classify and quantify microplastic polymers with great success, but the ability of these models to classify microplastics from new domains is unanswered. This paper presents an innovative approach to microplastic classification that employs a deep learning approach using a transformer neural network. Our specific contributions are: (1) A novel way to pre-process FTIR spectral data to dramatically increase classification accuracy. (2) Developed a transformer neural network for classifying microplastic polymer FTIR spectra. With the inclusion of a wider range of data, future deep learning approaches will improve the classification and quantification of microplastic polymers, subsequently reducing the costs and labour involved.
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References
Barboza, L.G.A., et al.: Microplastics pollution in the marine environment. In: World Seas: An Environmental Evaluation Volume III: Ecological Issues and Environmental Impacts (2018)
Cohen, M.: Transformers for time series (2019). https://github.com/maxjcohen/transformer
Cowger, W., et al.: Critical review of processing and classification techniques for images and spectra in microplastic research. Appl. Spectrosc. 74(9), 989–1010 (2020)
Harrison, J.P., Ojeda, J.J., Romero-González, M.E.: The applicability of reflectance micro-Fourier-transform infrared spectroscopy for the detection of synthetic microplastics in marine sediments. Sci. Total Environ. 416, 455–463 (2012)
Jenner, L.C., Rotchell, J.M., Bennett, R.T., Cowen, M., Tentzeris, V., Sadofsky, L.R.: Detection of microplastics in human lung tissue using \(\mu \)FTIR spectroscopy. Sci. Total Environ. 831, 154907 (2022)
Lamichhane, G., et al.: Microplastics in environment: global concern, challenges, and controlling measures. Int. J. Environ. Sci. Technol. (2022)
Leslie, H.A., Van Velzen, M.J., Brandsma, S.H., Vethaak, A.D., Garcia-Vallejo, J.J., Lamoree, M.H.: Discovery and quantification of plastic particle pollution in human blood. Environ. Int. 163, 107199 (2022)
Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Liu, M., et al.: Gated transformer networks for multivariate time series classification. arXiv preprint arXiv:2103.14438 (2021)
Lorenzo-Navarro, J., Castrillón-Santana, M., Gómez, M., Herrera, A., MarÃn-Reyes, P.A.: Automatic counting and classification of microplastic particles. In: Proceedings of the ICPRAM (2018)
Prata, J.C., da Costa, J.P., Lopes, I., Duarte, A.C., Rocha-Santos, T.: Effects of microplastics on microalgae populations: a critical review. Sci. Total Environ. 665, 400–405 (2019)
Primpke, S., Lorenz, C., Rascher-Friesenhausen, R., Gerdts, G.: An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis. Anal. Methods 9(9), 1499–1511 (2017)
Rußwurm, M., Körner, M.: Self-attention for raw optical satellite time series classification. ISPRS J. Photogramm. Remote Sens. 169, 421–435 (2020)
Sana, S.S., Dogiparthi, L.K., Gangadhar, L., Chakravorty, A., Abhishek, N.: Effects of microplastics and nanoplastics on marine environment and human health. Environ. Sci. Pollut. Res. 27(36), 44743–44756 (2020)
Schymanski, D., Goldbeck, C., Humpf, H.U., Fürst, P.: Analysis of microplastics in water by micro-Raman spectroscopy: release of plastic particles from different packaging into mineral water. Water Res. 129, 154–162 (2018)
Shi, B., et al.: Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning. Sci. Total Environ. 825, 153903 (2022)
Tuli, S., Casale, G., Jennings, N.R.: TranAD: deep transformer networks for anomaly detection in multivariate time series data. arXiv preprint arXiv:2201.07284 (2022)
Um, T.T., et al.: Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the ACM ICMI, pp. 216–220 (2017)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the NeurIPS, vol. 30 (2017)
Wagner, M., et al.: Microplastics in freshwater ecosystems: what we know and what we need to know. Environ. Sci. Eur. 26(1), 1–9 (2014)
Wen, Q., et al.: Transformers in time series: a survey. arXiv preprint arXiv:2202.07125 (2022)
Willans, M.: Developing a protocol for microplastic detection using micro-reflectance FTIR spectroscopy. Honours thesis, Curtin University (2021)
Yurtsever, M., Yurtsever, U.: Use of a convolutional neural network for the classification of microbeads in urban wastewater. Chemosphere 216, 271–280 (2019)
Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI, vol. 35, pp. 11106–11115 (2021)
Zhu, Z., Parker, W., Wong, A.: PlasticNet: deep learning for automatic microplastic recognition via FT-IR spectroscopy. J. Comput. Vis. Imaging Syst. 6(1), 1–3 (2021)
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Barker, M., Willans, M., Pham, DS., Krishna, A., Hackett, M. (2022). Explainable Detection of Microplastics Using Transformer Neural Networks. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_8
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