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.
摘要：
塑料污染现在在环境中无处不在，对野生动物的威胁越来越大，谁会把塑料误认为食物并摄入它。解决这个问题需要可靠，监测海鸟和其他海洋动物摄入的塑料污染的一致方法，包括识别不同类型塑料的方法。本研究提出了一种快速，使用红外和拉曼光谱对1-50mm尺寸范围内的摄入塑料进行可靠的化学表征。我们分析了来自豪勋爵岛的肉体足剪力机（Ardennacarneipes）摄入的246个物体，澳大利亚,并比较了每种技术产生的数据：通过光谱学确认了92％的摄入物体在视觉上被识别为塑料，其中98%是低密度聚合物，如聚乙烯，聚丙烯,或它们的共聚物。与其他报告相比，摄入的塑料显示出生物污染的重要光谱证据，这阻碍了传统图书馆搜索的识别。机器学习可用于通过振动光谱识别摄入的塑料，准确率高达93％。总的来说,我们发现红外线是识别这个尺寸范围内摄入的塑料的更有效的技术，并且适当训练的机器学习模型可以优于用于识别塑料的常规库搜索方法。
