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机器学习增强的分子网络揭示了全球数百种未知的PFAS暴露

Xuebing Wang¹, Nanyang Yu¹, Zhaoyu Jiao¹

¹State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People's Republic of China.

Science advances

|May 23, 2024

概括

识别未知的和多基基物质 (PFAS) 是一个挑战. 一个新的平台,APP-ID,使用增强的网络和机器学习来准确检测各种PFAS,包括废水中的新型 PFAS.

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:

和多醇基物质 (PFAS) 或"永久化学品"是环境中持续存在的污染物.
目前的分析方法很难识别PFAS的巨大结构多样性.
准确识别未知的PFAS对于环境监测和风险评估至关重要.

研究的目的:

开发一个自动化平台 (APP-ID) 用于在环境样本中全面识别PFAS.
提高PFAS结构阐明的准确性和减少虚假阳性.
评估全球环境矩阵中PFAS及其替代品的流行程度.

主要方法:

开发了一种增强的分子网络算法,并改进了碎片匹配.
实现机器学习模型 (支持矢量机器) 来识别未知的PFAS结构.
化废水和公共环境数据库的选.

主要成果:

与现有方法 (2.4-46%) 相比,增强的网络算法实现了较低的假阳性率 (0.7%).
机器学习模型以58.3%的准确度识别了未知的PFAS,性能优于当前的软件.
在废水中,APP-ID检测到733种PFAS,其中包括39种新化合物,并揭示了全球广泛的PFAS替代品.

结论:

应用程序ID平台在自动 PFAS 识别方面取得了重大进展.
该研究强调了PFAS及其替代品的普遍性,强调需要强有力的监测策略.
在环境样本中发现以前未报告的PFAS,APP-ID有助于此.

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