多变量回归建模用于性别预测,使用通过HS-SPME-GC-MS通过手部气味配置文件的挥发性有机化合物
Chantrell J G Frazier1,2, Vidia A Gokool1,3, Howard K Holness1
1Department of Chemistry and Biochemistry, Global Forensic and Justice Center, Florida International University, Miami, FL, United States of America.
PloS one
|July 5, 2023
概括
手气中的人类挥发性有机化合物 (VOC) 可以预测性别. 像HS-SPME-GC-MS等先进的分析方法和差异分析显示男性和女性之间有明显的VOC配置文件,建立了一个新的法医模型.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 人类生物学 人类生物学
背景情况:
- 人类挥发性有机化合物 (VOCs) 的犬类检测是为法医而建立的.
- 对于用于法医鉴定的人类VOC档案的实验室分析仍然有限.
- 人类的气味在法医调查中起着至关重要的作用.
研究的目的:
- 分析人类手的气味挥发性有机化合物 (VOCs) 用于性别预测.
- 建立一个基于实验室的模型,根据气味配置文件对个人进行分类.
- 探索VOCs作为性别确定中的法医证据的潜力.
主要方法:
- 头部空间-固体相微提取-气体染色学-质谱学 (HS-SPME-GC-MS) 用于分析来自60个人的手气样本.
- 挥发性有机化合物 (VOC) 签名使用部分最小平方区分分析 (PLS-DA),直角投影到潜在结构区分分析 (OPLS-DA) 和线性区分分析 (LDA) 来分析.
- 使用监督的尺寸缩小技术,根据VOC配置文件对性别进行分类.
主要成果:
- 部分最小平方区分分析 (PLS-DA) 模型显示了男性和女性受试者之间的聚类.
- 与潜结构差异分析的直角预测 (OPLS-DA) 显示了性别群体的明显歧视和集群,并进行了交叉验证.
- 线性差异分析 (LDA) 在从手气味VOC预测性别方面取得了96.67%的准确率.
结论:
- 已经建立了一个工作模型来预测捐赠者的特征,特别是性别,使用人类气味手气味的个人资料.
- 这项研究验证了使用先进的分析技术对人类VOC进行法医分析.
- 人类VOC档案为开发客观的法医识别方法提供了一个有希望的途径.
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