Rapid analysis and classification of blue ballpoint pen inks using high-performance thin-layer chromatography and digital color analysis.

Despite the rise of digitalization, the scrutiny of paper documents with stamps and signatures remains crucial, constituting 55-60 % of document examination court cases. Developing accessible and robust ink analysis techniques is an ongoing challenge for forensic scientists. To tackle issues related to the identification and classification of dye components in blue ballpoint pens, we have proposed an advanced approach that integrates high-performance thin-layer chromatography (HPTLC), digital color analysis (DCA), and hierarchical cluster analysis (HCA) for a simplified yet effective procedure. The method encompasses multiple development of TLC plates. Initial separation employs methanol:ethanol (4:1, v/v), followed by a second separation using methanol:propan-2-ol:formic acid (1:1:0.1, v/v/v) to achieve cationic dye separation. This TLC development enhances separation selectivity, contributing to a more precise clustering of pen inks. Employing advanced DCA as a processing method for HPTLC, the optimal descriptor for ink classification is identified as the difference between red (R) and green (G) color components. The proposed clustering algorithm achieved 8 distinct ink clusters, showing improved discriminative capability and a reduced variable space compared to well-established or state-of-the-art methods. The approach's robustness was demonstrated across different readout devices, including smartphones and scanners. Reproducibility tests yielded consistent clustering results, confirming the method's reliability. Moreover, the method proved effective in clustering pen ink extracts from paper substrates, showcasing its potential for real sample analysis. This HPTLC-DCA approach presents a simple, cost-effective, and high-precision solution for clustering blue pen inks, significantly enhancing accessibility in high-throughput forensic applications.