RESUMEN
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.
Asunto(s)
Suelo , Espectrometría Raman , Espectrometría Raman/métodos , Colorantes , Reproducibilidad de los Resultados , CabelloRESUMEN
Fabric is a commonly found piece of physical evidence at most crime scenes. Forensic analysis of fabric is typically performed via microscopic examination. This subjective approach is primarily based on pattern recognition and, therefore, is often inconclusive. Most of the fabric material found at crime scenes is colored. One may expect that a confirmatory identification of dyes can be used to enhance the reliability of the forensic analysis of fabric. In this study, we investigated the potential of near-infrared Raman spectroscopy (NIRS) in the confirmatory, non-invasive, and non-destructive identification of 15 different dyes on cotton. We found that NIRS was able to resolve the vibrational fingerprints of all 15 colorants. Using partial-squared discriminant analysis (PLS-DA), we showed that NIRS enabled ~100% accurate identification of dyes based on their vibrational signatures. These findings open a new avenue for the robust and reliable forensic analysis of dyes on fabric directly at crime scenes. Main conclusion: a hand-held Raman spectrometer and partial least square discriminant analysis (PLS-DA) approaches enable highly accurate identification of dyes on fabric.