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Using Surface-enhanced Raman Spectroscopy to Probe Artificial Dye Degradation On Hair Buried in Multiple Soils For Up To Eight Weeks

NCJ Number
Scientific Reports Volume: 14 Issue: 6469 Dated: March 2024
Date Published
March 2024

In this article, the authors report on their investigation of the role of SERS in forensic hair analysis and its implications for determining post-mortem intervals through the use of SERS coupled with partial least squares discriminant analysis to assess the detection of artificial dyes on hair buried in different soil types for up to eight weeks.


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, the authors investigated the reliability of the detection and identification of artificial dyes on hair buried in three distinct soil types for up to eight weeks. Their results demonstrated that SERS enabled the correct prediction of 97.9 percent of spectra for five out of the eight dyes used within the eight weeks of exposure. They 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. The authors 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. (Published Abstract Provided)

Date Published: March 1, 2024