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一个开放的计算指纹工作流程,用于使用GC × GC-TOFMS和机器学习对纯汽油的源分类

Huy Manh Nguyen¹, Roxana Sühring¹, Caleb Marx²

¹Department of Chemistry and Biology, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada.

Journal of chromatography. A

|September 25, 2025

概括

本研究介绍了一种开放式计算工作流程,用于汽油指纹识别,以识别纵火源. 该方法通过机器学习增强了汽油来源跟踪,提高了法医调查的准确性.

科学领域:

法医科学法医科学法医科学
分析化学分析化学
计算化学计算化学

背景情况:

多维染色学在纵火案件中提升了汽油来源的识别.
来自汽油分析的大型,复杂的数据集带来了数据管理和解释的挑战.
为了有效地跟踪汽油来源,需要强大的计算技术.

研究的目的:

开发一种新的,开放式的计算指纹工作流程,用于汽油来源跟踪.
创建一个区域性汽油资料数据库,用于纵火调查.
提高汽油来源的特征和识别.

主要方法:

在69个清洁的汽油样本上利用了多维气体染色学-飞行质谱时间 (GC × GC-TOFMS).
应用数据减少,规范化,聚类,特征选择 (递归特征添加 - RFA) 和监督机器学习 (ML).
使用基于决策树的ML分类器与RFA用于区分汽油来源.

主要成果:

确定了50个主要的化学特征 (n-基,基,环基,芳) 区分当地加油站.
与使用所有功能相比,使用RFA与决策树分类器的ML准确度平均提高了18%.
证明了工作流在区分汽油来源的有效性,尽管集群重叠.

结论:

开源计算工作流提供了汽油来源区分的透明度和可重复性.
该方法有助于法医分析师识别野火纵火中使用的可燃液体.
工作流程可以在不需要广泛的再培训的情况下,更轻松地集成到现有的协议中,如ASTM E1618-19.

关键词:

纵火调查调查纵火调查计算指纹的指纹采集汽油概况分析 (英语:Gasoline profiling) 是指对汽油进行概况分析.可点燃的液体可点燃的液体机器学习机器学习多维色谱是多维的色谱.二维气体染色学二维气体染色学

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