在海洋哺乳动物中关闭有机质量平衡,使用可疑查和基于机器学习的量化
Mélanie Z Lauria1, Helen Sepman1,2, Thomas Ledbetter1,2
1Department of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden.
Environmental science & technology
|January 25, 2024
概括
机器学习预测电离效率 (IE) 来量化缺乏标准的新型和多基物质 (PFAS). 这种方法显著减少了海洋哺乳动物样本中未识别的有机,有助于质平衡.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 毒理学 毒理学 毒理学
背景情况:
- 高分辨率质谱仪 (HRMS) 揭示了环境中的许多新型和多基物质 (PFAS).
- 由于缺乏分析标准,这些新型PFAS的量化受到阻碍,使暴露分数模糊不清.
- 在预测电离效率 (IE) 方面,机器学习的进步为在没有标准的情况下量化化合物提供了潜在的解决方案.
研究的目的:
- 开发和验证一种机器学习模型,用于预测 PFAS 负模式中的日志IE.
- 在海洋哺乳动物肝脏样本中应用经过验证的模型来量化非标准化可疑的PFAS.
- 评估该模型在减少未识别的有机的比例和改善质量平衡方面的有用性.
主要方法:
- 一个梯度增强的基于树的模型被训练来预测使用33个PFAS标准的日志IE.
- 该模型的预测准确性得到了验证,在完整和特定的PFAS测试集中,分别产生0.79和0.29日志IE单位的平方根平均误差.
- 该模型用于分析飞行员鱼和白鼻海豚的肝脏样本,量化了35种未定量可疑的PFAS.
主要成果:
- IE预测模型在量化PFAS方面表现良好.
- 该模型应用于海性哺乳动物样本,确定和量化以前未测量的可疑PFAS.
- 基于IE的量化减少了分析样本中未识别的可提取有机的比例,从最高70%降至0-27%.
结论:
- 基于机器学习的IE预测是一个有价值的工具,用于量化环境污染物,如PFAS,当分析标准是不可用的.
- 这种方法显著改善了对PFAS总暴露的理解,并有助于在环境样本中关闭质量平衡.
- 该方法在环境监测和新兴PFAS风险评估方面具有实用性.
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