人类指甲的性别确定基于减弱总反射里埃变换红外光谱在法医背景下
Bilkis Mitu1, Václav Trojan2,3, Lenka Halámková1
1Department of Environmental Toxicology, Texas Tech University, Lubbock, TX 79409, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
机器学习与减弱总反射率里埃变换红外光谱学 (ATR FT-IR) 结合,可以从指甲准确地预测捐赠者的性别. 这种法医工具在将个人分类为男性或女性时实现了100%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
- 生物化学 生物化学
背景情况:
- 减弱总反射率里埃变换红外光谱 (ATR FT-IR) 提供了一种快速,非破坏性的生物化学分析方法.
- 指甲分析带来了独特的挑战,因为样本要求最小,光谱变化有限的可能性.
- 先进的多变量技术对于解释复杂的光谱数据和识别微妙的供体特异性差异至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型用于使用ATR FT-IR光谱对指甲样本进行性别分类.
- 评估ML算法的有效性,利用ATR FT-IR光谱数据用于法医应用.
- 为了确定ATRFT-IR光谱学与ML相结合的精度和可靠性,以区分男性和女性捐赠者.
主要方法:
- 收集了63个个体的指甲样本 (37个男性,26个女性).
- 使用减弱总反射功率里埃变换红外光谱 (ATR FT-IR) 来获得光谱数据.
- 开发并比较了分类模型,包括部分最小平方差异分析 (PLS-DA) 和人工神经网络 (ANN),PLS得分被输入到ANN中.
主要成果:
- 拟议的人工神经网络 (ANN) 模型显示出强大的预测潜力.
- 对独立数据集的验证在正确分类的光谱中产生了92%的准确性.
- 该研究在性别分类方面在捐赠者层面实现了令人印象深刻的100%准确性.
结论:
- 机器学习算法可以有效地增强ATRFT-IR光谱的分辨能力.
- 增加了ML的ATRFT-IR光谱学显示出作为可靠的法医工具来确定性别的显著前景.
- 这项概念验证研究验证了在法医调查中具有确定性水平的科学可辩护预测的潜力.
相关概念视频
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