Work

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

Year: 2024

Type: article

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. The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a match by using comparative microscopy. Here the authors leveraged statistical analysis of the surface topography to provide a quantitative description of the fracture surface, enabling forensic article comparisons with quantified probabilities.

Source: Nature Communications

DOI: 10.1038/s41467-024-51594-1