Forensic classification of black inkjet prints using Fourier transform near-infrared spectroscopy and Linear Discriminant Analysis

Michal Oravec; Anel Beganović; Lukáš Gál; Michal Čeppan; Christian W Huck

doi:10.1016/j.forsciint.2019.03.041

Forensic classification of black inkjet prints using Fourier transform near-infrared spectroscopy and Linear Discriminant Analysis

Forensic Sci Int. 2019 Jun:299:128-134. doi: 10.1016/j.forsciint.2019.03.041. Epub 2019 Mar 30.

Authors

Michal Oravec¹, Anel Beganović², Lukáš Gál³, Michal Čeppan⁴, Christian W Huck⁵

Affiliations

¹ Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology, Radlinskeho 9, 812 37 Bratislava, Slovak Republic. Electronic address: michal.oravec@stuba.sk.
² Leopold-Franzens University, Institute of Analytical Chemistry and Radiochemistry, CCB - Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria. Electronic address: Anel.Beganovic@uibk.ac.at.
³ Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology, Radlinskeho 9, 812 37 Bratislava, Slovak Republic. Electronic address: lukas.gal@stuba.sk.
⁴ Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology, Radlinskeho 9, 812 37 Bratislava, Slovak Republic. Electronic address: michal.ceppan@stuba.sk.
⁵ Leopold-Franzens University, Institute of Analytical Chemistry and Radiochemistry, CCB - Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria. Electronic address: Christian.W.Huck@uibk.ac.at.

PMID: 30991211
DOI: 10.1016/j.forsciint.2019.03.041

Abstract

This work presents a study regarding the forensic discrimination of black inkjet-printed documents in question. Nondestructive Fourier transform near-infrared (FT-NIR) spectroscopy in combination with supervised classification method Discriminant Analysis (DA); Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were utilized to investigate 22 different prints of the three most sold office printer brands. The spectra were acquired using the FT-NIR spectrometer NIRFlex N-500 (Büchi Labortechnik AG, Flawil, Switzerland) with the Fiber Optic Solids measuring cell in the spectral region of 10,000-4000 cm^-1. Each sample was printed on the same type of office paper. The spectra were 45 times acquired on 3 separate printed squares of each sample. It results in 990 acquired spectra for the presented experiment. The FT-NIR spectra of the printed squares were split into calibration and test sets with which the Classification accuracy (CA) value of unknown samples was evaluated. In order to increase the significance, three different compilations of calibration and test sets were realized. The performance of three different Discriminant model methods; LDA (Euclidean and Mahalanobis algorithm) and QDA were compared to each other. Furthermore, the CA of each DA method was examined using 1-5 principal components (PCs) in the construction of the respective DA model. Two groups of models, according to the ink subset (Carbon black and Black colorant), were performed in raw and Standard Normal Variate (SNV) correction spectra alternations. The results showed that the Euclidean method yielded the highest accuracy in predicting independent test samples and thus clearly outperformed the QDA and Mahalanobis algorithm DA method. It was also determined that the ink type Carbon black had higher CA values than the ink type Black colorant. This work demonstrated the special ability of FT-NIR spectroscopy in combination with DA to examine inkjet-printed documents in a fast and non-destructive fashion.

Keywords: Chemometrics; Document; Forensic science; LDA; Spectroscopy.