使用振动光谱和监督机器学习来化学识别海鸟摄入的塑料
Joseph Razzell Hollis1, Jennifer L Lavers2, Alexander L Bond1
1Bird Group, Natural History Museum, Tring, UK.
Journal of hazardous materials
|July 7, 2024
概括
这项研究开发了一种快速光谱方法,以识别海鸟摄入的塑料污染. 机器学习能够准确地识别塑料,红外光谱技术在这种海洋碎片分析中被证明是最有效的.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 海洋生物学 海洋生物学
背景情况:
- 无处不在的塑料污染通过摄入对海洋野生动物构成重大威胁.
- 需要可靠的方法来监测和识别海洋动物中摄入的塑料.
- 现有方法面临的挑战是摄入塑料的生物污染.
研究的目的:
- 提出一种可靠的方法,用于快速化学表征摄入的塑料 (1-50毫米).
- 为了比较红外和拉曼光谱来识别摄入的塑料.
- 评估机器学习在从振动光谱中识别塑料的有效性.
主要方法:
- 使用红外和拉曼光谱学分析了 246 个肉脚水动物摄入的物体.
- 视觉识别塑料的光谱确认.
- 机器学习模型应用于塑料识别的振动光谱.
主要成果:
- 光谱学证实了92%的视觉识别的摄入塑料.
- 98%的确认塑料是低密度聚合物 (聚乙烯,聚烯,共聚物).
- 机器学习在识别摄入的塑料中达到高达93%的准确性.
- 红外光谱在这个尺寸范围内比拉曼更有效.
- 塑料上的生物污染影响了传统的图书馆搜索,但不是ML模型.
结论:
- 红外光谱学提供了一种有效的方法来识别海洋动物中摄入的塑料.
- 机器学习模型显著提高了从光谱数据中塑料识别的准确性.
- 这种方法有助于理解和减轻塑料污染对海洋生态系统的影响.
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