The rise in improved and widely accessible printing technology has resulted in an interest to develop rapid and minimally destructive chemical analytical techniques that can characterize printing inks for forensic document analysis. Chemical characterization of printing inks allows for both discrimination of inks originating from different sources and the association of inks originating from the same source. Direct analysis in real-time mass spectrometry (DART-MS) and attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) were used in tandem to analyze four different classes of printing inks: inkjets, toners, offset, and intaglio. A total of 319 samples or ~ 80 samples from each class were analyzed directly on a paper substrate using the two methods. DART-MS was found to characterize the semi-volatile polymeric vehicle components, while ATR-FTIR provided chemical information associated with the bulk components of these inks. Complimentary data results in improved discrimination when both techniques are used in succession resulting in >96% discrimination for all toners, 95% for all inkjets, >92% for all offset, and >54% for all intaglio inks.

No abstract

No abstract

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.

To my family, who have supported me since day one and pushed me to work towards my dreams. To my friends and loved ones who have stuck with me through the ups and downs, and for being understanding during my time in graduate school-I would not have been able to do this without you, and for helping me learn that: "We are made to persist. That's how we find out who we really are."-Tobias Wolff v ACKNOWLEDGMENTS First, I'd like to acknowledge and thank Dr. José R. Almirall for providing me with the opportunity to be a part of his research group, for his guidance, insight, and for pushing me to excel. I would also like to thank my dissertation committee Dr.