Department of Entomology, Texas A&M University, College Station, TX, 77843, USA.
Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
Sci Rep. 2024 Mar 18;14(1):6469. doi: 10.1038/s41598-024-57147-2.
The discovery of clandestine burials poses unique challenges for forensic specialists, requiring diverse expertise to analyze remains in various states. Bones, teeth, and hair often endure the test of time, with hair particularly exposed to the external environment. While existing studies focus on the degradation of virgin hair influenced by soil pH and decomposition fluids, the interaction between artificial dyes on hair and soil remains underexplored. This paper introduces a novel approach to forensic hair analysis that is based on high-throughput, nondestructive, and non-invasive surface-enhanced Raman spectroscopy (SERS) and machine learning. Using this approach, we investigated the reliability of the detection and identification of artificial dyes on hair buried in three distinct soil types for up to eight weeks. Our results demonstrated that SERS enabled the correct prediction of 97.9% of spectra for five out of the eight dyes used within the 8 weeks of exposure. We also investigated the extent to which SERS and machine learning can be used to predict the number of weeks since burial, as this information may provide valuable insights into post-mortem intervals. We found that SERS enabled highly accurate exposure intervals to soils for specific dyes. The study underscores the high achievability of SERS in extrapolating colorant information from dyed hairs buried in diverse soils, with the suggestion that further model refinement could enhance its reliability in forensic applications.
秘密埋葬的发现给法医专家带来了独特的挑战,需要各种专业知识来分析各种状态下的遗骸。骨头、牙齿和头发通常能经受住时间的考验,而头发尤其容易受到外部环境的影响。虽然现有研究集中在土壤 pH 值和分解液对 virgin 头发降解的影响上,但头发上的人工染料与土壤之间的相互作用仍未得到充分研究。本文介绍了一种基于高通量、非破坏性和非侵入性表面增强拉曼光谱 (SERS) 和机器学习的法医头发分析新方法。使用这种方法,我们调查了在三种不同土壤类型中埋藏长达八周的头发上人工染料的检测和识别的可靠性。我们的结果表明,SERS 能够正确预测暴露 8 周内使用的八种染料中的五种的 97.9%的光谱。我们还研究了 SERS 和机器学习在多大程度上可以用于预测埋葬后的周数,因为这些信息可能为死后间隔提供有价值的见解。我们发现,SERS 能够高度准确地预测特定染料暴露于土壤的时间间隔。这项研究强调了 SERS 在从埋在不同土壤中的染色头发中推断出着色剂信息方面的高度可实现性,并表明进一步的模型改进可以提高其在法医应用中的可靠性。