Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation.

Autor: Golovkina AG; Saint Petersburg State University, 7/9 Universitetskaya nab., 199034 St, Petersburg, Russia., Karpukhin OR; Saint Petersburg State University, 7/9 Universitetskaya nab., 199034 St, Petersburg, Russia., Kravchenko AV; Saint Petersburg State University, 7/9 Universitetskaya nab., 199034 St, Petersburg, Russia., Khairullina EM; Saint Petersburg State University, 7/9 Universitetskaya nab., 199034 St, Petersburg, Russia., Tumkin II; Applied Laser Technologies, Ruhr University Bochum, Universitätsstr. 150, Bochum 44801, Germany. Electronic address: ilia.tumkin@ruhr-uni-bochum.de., Kalinichev AV; Aarhus University, Aarhus Institute of Advanced Studies, Høegh-Guldbergs Gade 6B, 8000 Aarhus C, Denmark.
Jazyk: angličtina
Zdroj: Forensic science international [Forensic Sci Int] 2024 Nov; Vol. 364, pp. 112236. Date of Electronic Publication: 2024 Sep 27.
DOI: 10.1016/j.forsciint.2024.112236
Abstrakt: Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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