使用ATR-FTIR光谱学和机器学习从热处理的人类头发中取出性别的法医歧视
B S Gunashree1, Mebin Wilson Thomas2, Suchita Rawat3
1Kristu Jayanti (deemed to be) University, Bangalore, India.
Die Naturwissenschaften
|December 1, 2025
概括
热降解显著改变人类头发的组成. 减弱总反射度里埃变换红外光谱 (ATR-FTIR) 与机器学习相结合,可以准确地从经过处理或未经处理的头发样本中确定性别.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 生物物理学的生物物理.
背景情况:
- 热降解会改变头发的分子结构,影响蛋白质,脂质和二硫化物键.
- 这些分子变化可以作为法医分析中的关键指标.
- 了解这些变化是高级法医调查的关键.
研究的目的:
- 研究热处理对人类头发化学成分的影响.
- 评估减弱总反射度里埃变换红外光谱 (ATR-FTIR) 和机器学习用于法医性别确定的有效性.
- 探索这些方法在分析烧伤头发样本方面的潜力.
主要方法:
- 从100名参与者 (50名男性,50名女性) 的头发样本使用ATR-FTIR光谱分析.
- 样本包括未经处理和经过热处理的头发丝.
- 用多变量模型分析了光谱数据:部分最小平方差异分析 (PLS-DA),支持向量机 (SVM) 和K-最近邻居 (KNN).
主要成果:
- 热暴露引起了与蛋白质 (胺基 I,II,III),脂质 (C-H 拉伸) 和二硫化物 (S-S 拉伸) 相关的光谱波段的显著变化.
- 这些变化表明结构变质,键裂和氧化性修饰.
- PLS-DA,SVM和KNN模型在按性别和热处理状态分类样品时实现了100%的准确性.
结论:
- ATR-FTIR光谱学有效地检测到人体头发的热感应的结构和化学变化.
- 机器学习模型 (PLS-DA,SVM,KNN) 使用ATR-FTIR光谱数据显示高精度的性别确定.
- 这种综合方法为法医分析烧伤头发提供了强大而非破坏性的工具,有助于确定性别和评估损害.
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