TY - JOUR

T1 - Digital Color Analysis and Machine Learning for Ballpoint Pen Ink Clustering and Aging Investigation

AU - Головкина, Анна Геннадьевна

AU - Карпухин, Олег Романович

AU - Кравченко, Анастасия

AU - Хайруллина, Евгения Мусаевна

AU - Тумкин, Илья

AU - Калиничев, Андрей

PY - 2024/11/1

Y1 - 2024/11/1

N2 - 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.

AB - 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.

KW - Classification

KW - Digital color analysis

KW - Document degradation

KW - Forensic analysis

KW - Ink

KW - Machine learning

UR - https://www.mendeley.com/catalogue/03564177-a11e-3454-a7ee-5969fc38ed0a/

U2 - 10.1016/j.forsciint.2024.112236

DO - 10.1016/j.forsciint.2024.112236

M3 - Article

VL - 364

JO - Forensic Science International

JF - Forensic Science International

SN - 0379-0738

M1 - 112236

ER -