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Fingermark initial composition and aging using Fourier transform infrared microscopy (μ-FTIR).

Aline Girod1, Linda Xiao2, Brian Reedy2

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Summary

Fourier transform infrared microscopy (μ-FTIR) can analyze fingermark composition and aging. Models show potential for dating fingermarks with ±3 days precision, differentiating new from old marks.

Keywords:
DatingFingerprintKineticsPCAPLSRSpearman correlation

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Fingermark residue analysis is crucial for forensic investigations.
  • Understanding fingermark aging kinetics aids in determining the time of deposition.
  • Fourier transform infrared microscopy (μ-FTIR) offers potential for non-destructive analysis of fingermarks.

Purpose of the Study:

  • To investigate fingermark residues using μ-FTIR for composition and aging kinetics.
  • To evaluate different μ-FTIR modes (ATR and single-point reflection) for fingermark analysis.
  • To explore the potential of μ-FTIR data for fingermark dating applications.

Main Methods:

  • Fourier transform infrared microscopy (μ-FTIR) with Attenuated Total Reflection (ATR) mode was employed.
  • Fresh and aged fingermarks were analyzed on different substrates (aluminium, glass slides) under various storage conditions.
  • Chemometric analyses (including PLS regression) were used to model fingermark aging.

Main Results:

  • Eccrine and sebaceous materials were identified in fingermarks across different aging stages.
  • Spectral regions 1000-1850cm⁻¹ and 2700-3600cm⁻¹ proved most informative for analysis.
  • Fingermark aging was influenced by storage conditions, with substrate impact noted in dark storage.
  • PLS regression models achieved ±3 days precision in differentiating fingermark ages.

Conclusions:

  • μ-FTIR, particularly ATR mode, is suitable for analyzing fingermark composition and aging.
  • The study demonstrates the feasibility of using μ-FTIR data for fingermark dating.
  • Further validation is needed to assess model robustness in real-world forensic scenarios.