机器学习辅助的LIBS分类燃烧和未燃烧的纸张样本:一个法医的角度
Muhammad Nadeem1, Muhammad Faheem1, Maryam Manzoor1
1Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan.
Talanta
|April 5, 2025
概括
激光诱导分解光谱 (LIBS) 可以在纵火案件中识别烧毁的材料. 机器学习算法在分类燃烧纸样本方面达到100%的准确性,表明LIBS是一个有前途的法医工具.
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:
- 纵火调查需要分析受火影响的材料.
- 激光诱导分解光谱 (LIBS) 为化学分析燃烧物品提供了潜力.
- 纸质文件在法医调查中很常见.
研究的目的:
- 探索LIBS在分析烧纸方面的法医潜力.
- 评估LIBS用于识别燃烧后元素组成的变化.
- 评估机器学习在基于LIBS的法医分类中的作用.
主要方法:
- 使用Nd:YAG激光 (532nm) 对未燃烧的纸张样本记录了LIBS光谱.
- 分析了等离子体参数 (温度,电子数密度),以验证局部热力学平衡.
- 用不同的点火源 (打火机,,燃气) 燃烧纸张样本.
- 机器学习算法 (PCA,LDA,QDA,线性SVM) 应用于LIBS数据.
主要成果:
- 在LIBS分析中,无论点火源如何,烧纸样本的元素组成都是一致的.
- 主要组件分析 (PCA) 显示了轻微的样本歧视.
- 监督算法 (LDA,QDA,线性SVM) 实现了100%的分类准确性.
结论:
- 机器学习辅助的LIBS是法医应用的高效工具.
- 对于法医案例工作,LIBS可以分析元素组成.
- 这种技术对识别燃烧的纸张和潜在的其他材料显示出希望.
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