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The Use of Vibrational Spectroscopy and Supervised Machine Learning for Chemical Identification of Ingested Plastic Pollution
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2024-09-05
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plastic pollution
Anthropocene
Raman Spectroscopy
Infrared spectroscopy
biological contamination
Anthropocene
Raman Spectroscopy
Infrared spectroscopy
biological contamination
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Abstract
Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested by seabirds and other marine fauna, including methods for identifying different types of plastic. This study presents a robust method for the rapid, reliable chemical characterisation of ingested plastics in the 1–50 mm size range using infrared and Raman spectroscopy. We analysed 246 objects ingested by Flesh-footed Shearwaters (Ardenna carneipes) from Lord Howe Island, Australia, and compared the data yielded by each technique: 92 % of ingested objects visually identified as plastic were confirmed by spectroscopy, 98 % of those were low density polymers such as polyethylene, polypropylene, or their copolymers. Ingested plastics exhibit significant spectral evidence of biological contamination compared to other reports, which hinders identification by conventional library searching. Machine learning can be used to identify ingested plastics by their vibrational spectra with up to 93 % accuracy. Overall, we find that infrared is the more effective technique for identifying ingested plastics in this size range, and that appropriately trained machine learning models can be superior to conventional library searching methods for identifying plastics.
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Joseph Razzell Hollis, Jennifer L. Lavers, Alexander L. Bond, The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds, Journal of Hazardous Materials, Volume 476, 2024, 134996, ISSN 0304-3894, https://doi.org/10.1016/j.jhazmat.2024.134996.
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Copyright © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). The attached file is the published version of the article.
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0304-3894
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1873-3336