Document Type



Doctor of Philosophy (PhD)



First Advisor's Name

Jose Almirall

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

David Becker

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Yong Cai

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Anthony DeCaprio

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Werner Boeglin

Fifth Advisor's Committee Title

Committee Member


Forensic, Ink, Glass, Likelihood Ratio, SEM-EDS, LA-ICP-MS, LIBS, Database

Date of Defense



Improvements in printing technology have exacerbated the problem of document counterfeiting, prompting the need for analytical techniques that better characterize inks for forensic analysis. 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 resulted in excellent discrimination (> 99%) between ink samples originating from different sources. Moreover, LA-ICP-MS provided ≥ 90% correct association for ink samples originating from the same source. SEM-EDS resulted in good discrimination for toner and intaglio inks (> 97%) and excellent correct association (100%) for all four ink types. However, the technique showed limited utility for the discrimination of inkjet and offset inks.

A searchable ink database, the Forensic Ink Analysis and Comparison System (FIACS), was developed in order to provide a tool that allows the analyst to compare a questioned ink sample to a reference population. The FIACS database provided a correct classification rate of 94-100% for LA-ICP-MS and 67-100% for SEM-EDS.

An important consideration in forensic chemistry is the interpretation of the evidence. Typically, a match criterion is used to compare the known and questioned sample. However, this approach suffers from several disadvantages, which can be overcome with an alternative approach: the likelihood ratio (LR). Two LA-ICP-MS glass databases were used to evaluate the performance of the LR: a vehicle windshield database (420 samples) and a casework database (385 samples). Compared to the match criterion, the likelihood ratio led to improved false exclusion rates (< 1.5%) and similar false inclusion rates (< 1.0%). In addition, the LR limited the magnitude of the misleading evidence, providing only weak support for the incorrect proposition.

The likelihood ratio was also tested through an inter-laboratory study including 10 LA-ICP-MS participants. Good correct association rates (94-100%) were obtained for same-source samples for all three inter-laboratory exercises. Moreover, the LR showed a strong support for an association. Finally, all different-source samples were correctly excluded with the LR, resulting in no false inclusions.



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