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通过机器学习提升PFAS检测预测FNMR光谱

Dandan Rao¹, Jinyu Gao¹, Huichun Zhang²

¹Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, United States.

Environmental science & technology

|December 24, 2025

概括

机器学习预测-19 NMR对和多基物质 (PFAS) 的化学转移,有助于识别这些持久污染物. 该工具增强了PFAS分析和环境修复工作.

科学领域:

环境化学环境化学
分析化学分析化学
计算化学的计算化学

背景情况:

和多醇基物质 (PFAS) 是持久性环境污染物,需要先进的识别方法.
解释PFAS分析的F NMR光谱是具有挑战性的,因为参考数据有限.
需要新的方法来推进PFAS影响评估和补救技术.

研究的目的:

开发一种机器学习 (ML) 模型,用于预测PFAS的F NMR化学转移.
为了解决PFAS光谱数据解释方面的知识差距.
为识别新出现的PFAS和支持整治提供一个实用的工具.

主要方法:

策划了来自647种化化合物的3616种化学转移的数据集.
探索了各种原子特征描述器,并评估了多个ML算法.
开发了一个前神经网络 (FFNN) 模型和一个信任级别系统.

主要成果:

在FFNN模型中,测试数据的平均绝对误差为2.40 ppm,49%的预测误差小于1.0 ppm.
该模型预测了新型PFAS结构的化学转移,平均误差高达90%低于数据库方法.
通过预测新的PFAS光谱,峰值分配援助和废水中的结构澄清来验证效用.

结论:

机器学习提供了一种强大的方法来克服PFAS的挑战19FNMR光谱解释.
开发的预测工具可以显著支持环境样本中PFAS的识别和分析.
本研究通过提供实用的计算解决方案,推动了PFAS整治和影响评估.

关键词:

在这里,我们可以看到AIAIAI.一个软管.里奇山脊 (英语:Ridge Ridge) 是一个山脊.在XGBoost中使用.数据科学数据科学真实样本的真实样本

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