The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds
Joseph Razzell Hollis, Jennifer L. Lavers, Alexander L. Bond
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. Plastic pollution is an increasingly common hazard for wildlife, with over 170 trillion particles of plastic now present in the ocean, and ingesting plastic has been implicated in increased morbidity, malnutrition, and new diseases such as ‘plasticosis’. Robust chemical analysis techniques are essential to monitoring plastic ingestion by wildlife and understanding how different plastics cause harm. In this study, we demonstrate how Raman and FTIR spectroscopy combined with machine learning can be used to reliably identify ingested plastics despite extensive biological contamination, and observe how the foraging habits of one species may influence the types of plastic they ingest.