通过激光诱导分解光谱学和机器学习算法分析非有毒弹药的火药残留物
Rodrigo Wenceslau1, Jader S Cabral2, Gabriel da Silva Souza2
1UFMS - Universidade Federal de Mato Grosso do Sul, Programa de Pós-Graduação em Ciência dos Materiais, Campo Grande, MS, Brazil; SEJUSP - Secretaria de Estado de Justiça e Segurança Pública, Coordenadoria Geral de Perícias, Coordenador Regional da Unidade de Perícias e Identificação de Costa Rica, MS, Brazil.
Talanta
|July 2, 2025
概括
激光诱导分解光谱 (LIBS) 和机器学习 (ML) 可以识别无毒弹药 (GSR-NTA) 的枪支残留物. 这种方法可以检测到和等关键元素,为枪支放电分析提供了一个新的法医工具.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 枪支残留物 (GSR) 是枪支调查中的关键证据.
- 使用SEM-EDS进行常规GSR分析对于无毒弹药是无效的.
- 无毒弹药缺乏传统标记物,如,和.
研究的目的:
- 开发和验证一种新的方法,用于从无毒弹药中识别GSR (GSR-NTA).
- 探索激光诱导分解光谱 (LIBS) 与机器学习 (ML) 结合用于GSR-NTA检测的潜力.
- 为了确定枪手手上发现的GSR-NTA颗粒的化学指纹.
主要方法:
- 从枪手的手中收集了GSR-NTA颗粒.
- 使用LIBS在两个范围 (186-1042 nm和186-570 nm) 获取的光谱数据.
- 应用多变量分析和ML算法,包括线性差别分析 (LDA),用于分类.
主要成果:
- 检测到的元素包括H,N,O,C,Ti,Zn,Cu,Ba,Sr,Fe,Mg和Al.
- 确定了Zn,Ti,Cu和Fe作为样本分化的主要元素.
- 在培训和验证数据集上使用LDA实现了100%的分类准确性.
结论:
- 与ML相结合的LIBS提供了一种有希望的方法来识别GSR-NTA.
- 这项技术可以区分使用无毒弹药开枪或没有开枪的个人.
- 为涉及无毒弹药的枪支相关事件的法医分析提供了潜在的进步.
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