使用手持近红外光谱和深度学习的手持近红外光谱技术,快速对人类和动物骨进行法医差异化
Klemens Weisleitner1, Claudia Wöss2, Lukas Kampik3
1Austrian Polar Research Institute, Djerassiplatz 1, 1030 Vienna, Austria; Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria.
概括
手持近红外光谱仪与人工神经网络可以在法医调查中快速识别骨类. 这种非破坏性的方法为物种确定和尸体分析提供了一个便携式的替代方案.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 在法医调查中,准确识别人类与动物骨至关重要,特别是在碎片化遗骸中.
- 传统的骨识别方法可能具有破坏性和耗时性.
- 需要快速,非破坏性的技术进行现场法医分析.
研究的目的:
- 评估手持近红外 (NIR) 光谱学与人工神经网络 (ANN) 结合用于区分人类和动物骨物种的有效性.
- 评估这项技术对于快速,非破坏性,现场法医应用的实用性.
主要方法:
- 近红外光谱法用于分析225个大腿骨样本 (人类和动物).
- 用二进制和多类人工神经网络模型预处理和分类光谱数据.
- 主要成分分析 (PCA) 用于可视化光谱模式和识别影响因素.
主要成果:
- 人工神经网络模型实现了高的中位准确率:96.3%的二进制分类和77.8%的多类分类.
- 主要成分分析揭示了不同物种之间明显的光谱聚类模式.
- 此外,PCA还指出,死后的间隔影响了人类骨样本的光谱特征.
结论:
- 手持NIR光谱与ANN相结合,提供了一种实用,便携和高效的方法,用于在法医环境中快速识别骨物种.
- 这项技术为传统的法医分析方法提供了一个可行的,非破坏性的替代方案.
- 这种方法的进一步改进有可能在法医物种确定和死后间隔分析方面取得重大进展.
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