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基于离子流动性质谱学和机器学习技术的透和多基基物质的可疑和非目标查

Hongxin Mu¹, Zhongchao Yang¹, Ling Chen¹

¹State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China.

Journal of hazardous materials

|October 5, 2023

概括

这项研究将离子移动性谱学与高分辨率质谱学和机器学习相结合,用于在废水中精确查和多基物质 (PFAS). 新方法显著减少了假阳性,并提高了识别这些环境污染物的信心.

科学领域:

环境化学环境化学
分析化学分析化学
频谱测量是一种光谱测量.

背景情况:

高分辨率质谱仪 (HRMS) 对于识别和多醇基物质 (PFAS) 是至关重要的.
复杂的矩阵,特别是废水,由于干扰信号,对基于HRMS的PFAS选提出了重大挑战.
准确识别PFAS对于环境监测和风险评估至关重要.

研究的目的:

开发和验证一种新的方法,将离子移动性光谱 (IMS) 与HRMS和机器学习相结合,用于在废水中增强PFAS疑似和非目标查.
在复杂的环境样本中提高PFAS识别的准确性和减少虚假阳性.
建立一个强大的信任分配标准,用于PFAS的定性识别.

主要方法:

将离子移动性光谱 (IMS) 与高分辨率质谱 (HRMS) 集成,以获取更清洁的光谱数据.
机器学习算法的应用,包括随机森林与RDKit描述符,用于碰撞横截面 (CCS) 预测.
开发保留时间和CCS预测模型,以提高PFAS识别的可靠性.
实施一个包含多维数据的全面信任赋值标准.

主要成果:

与传统HRMS相比,IMS-HRMS显著减少了光谱干扰,产生了更清晰的数据.
将碰撞横截面 (CCS) 数据纳入PFAS搜索中可以过出63%的假阳性结果.
预测保留时间和CCS的预测模型提高了PFAS定性识别的可靠性.
在废水中共发现了56种潜在的PFAS,其中45种超出了可用的参考标准.

结论:

IMS-HRMS和机器学习的结合提供了一种强大的方法,用于在废水等具有挑战性的矩阵中快速准确地选PFAS.
开发的方法提高了PFAS识别的可靠性,并减少了对环境监测至关重要的假阳性.
这些发现强调了先进的可疑和非目标查技术的必要性,以在环境样本中全面检测PFAS.

关键词:

在CCS预测预测.信任分配的分配信任分配的分配在同类搜索中使用同类搜索.这就是IMS-HRMS.在PFAS中,有很多方法.

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