Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning

Autor: Geoffrey Z. Thompson, Bishoy Dawood, Tianyu Yu, Barbara K. Lograsso, John D. Vanderkolk, Ranjan Maitra, William Q. Meeker, Ashraf F. Bastawros
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-024-51594-1
Popis: Abstract The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a “match” by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. We utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a “match” and “non-match” among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties.
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