Issue 11, 2023

Wet nitrocellulose membrane for the level 3 feature visualization of various latent fingerprints and gender determination

Abstract

A facile and high-resolution enhancement of latent fingerprints (LFPs) has been developed by using a wet nitrocellulose (NC) membrane as a matrix under natural light. A clear fingerprint pattern was presented on the membrane after a fingertip touch owing to the difference in light transmittance between the ridge residues and the wet-NC-membrane background. Compared with conventional methods, this protocol can provide a higher resolution fingerprint image to extract level 3 details accurately. It is also compatible with commonly used fingerprint visualization techniques (magnetic ferric oxide powder and AgNO3. The modified membrane could be more general to realize the high-resolution visualization of LFP transferred from various substrates, even independent of light projection. Due to the excellent feasibility and reproducibility of level 3 details extracted by the wet NC membrane, the frequency distribution of the distance between adjacent sweat pores (FDDasp) could be used to effectively distinguish the fragmentary fingerprints. Finally, the level 3 features of LFPs from females and males were conveniently extracted by the wet-NC-membrane method for gender identification. The statistical results indicated that females had a higher average sweat pore density (115/9 mm2) than males (84/9 mm2). Taken together, this approach provided a high-resolution, reproducible, and accurate imaging of LFPs, which shows great promise for forensic information analysis.

Graphical abstract: Wet nitrocellulose membrane for the level 3 feature visualization of various latent fingerprints and gender determination

Supplementary files

Article information

Article type
Paper
Submitted
02 Apr 2023
Accepted
17 Apr 2023
First published
20 Apr 2023

Analyst, 2023,148, 2438-2448

Wet nitrocellulose membrane for the level 3 feature visualization of various latent fingerprints and gender determination

L. Tian, H. Chen, X. Sun, L. Liu and M. Zhang, Analyst, 2023, 148, 2438 DOI: 10.1039/D3AN00511A

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