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Improvements in printing technology have exacerbated the problem of document counterfeiting, prompting the need for analytical techniques that better characterize inks for forensic analysis and comparisons. In this study, 319 printing inks (toner, inkjet, offset, and Intaglio) were analyzed directly on the paper substrate using scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). As anticipated, the high sensitivity of LA-ICP-MS pairwise comparisons resulted in excellent discrimination (average of ~ 99.6%) between different ink samples from each of the four ink types and almost 100% correct associations between ink samples known to originate from the same source. SEM-EDS analysis also resulted in very good discrimination for different toner and intaglio inks (>97%) and 100% correct association for samples from the same source. SEM-EDS provided complementary information to LA-ICP-MS for certain ink types but showed limited utility for the discrimination of inkjet and offset inks.

A searchable printing ink database was designed and validated as a tool to improve the chemical information gathered from the analysis of ink evidence. The database contains 319 samples from printing sources that represent some of the global diversity in toner, inkjet, offset, and intaglio inks. Five analytical methods were used to generate data to populate the searchable database including FTIR, SEM-EDS, LA-ICP-MS, DART-MS, and Py-GC-MS. The search algorithm based on partial least-squares discriminant analysis generates a similarity "score" used for the association between similar samples. The performance of a particular analytical method to associate similar inks was found to be dependent on the ink type with LA-ICP-MS performing best, followed by SEM-EDS and DART-MS methods, while FTIR and Py-GC-MS were less useful in association but were still useful for classification purposes. Data fusion of data collected from two complementary methods (i.e., LA-ICP-MS and DART-MS) improves the classification and association of similar inks.