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

• Published in: Forensic science international 2019-06, Vol.299, p.128-134
• Main Authors: Oravec, Michal, Beganović, Anel, Gál, Lukáš, Čeppan, Michal, Huck, Christian W.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alternations; Black carbon; Calibration; Carbon; Carbon black; Chemometrics; Chromatography; Classification; Discriminant analysis; Discrimination; Document; Fiber optics; Forensic science; Forensic sciences; Fourier transforms; I.R. radiation; Infrared analysis; Infrared spectra; Infrared spectroscopy; Inkjet printing; Investigations; Lasers; LDA; Mass spectrometry; Methods; Near infrared radiation; Optical fibers; Principal components analysis; R&D; Research & development; Scientific imaging; Spectroscopy; Test sets
• ISSN: 0379-0738; 1872-6283
• DOI: 10.1016/j.forsciint.2019.03.041

•Investigation of 990 FT-NIR spectra of 22 prints of 3 most sold office printer brands.•LDA vs QDA: to find the optimal approach to classify black inkjet prints in question.•Novel, fast and non-destructive chemometric tool requiring minimal pretreatment.•Easy discrimination of prints as promising tool for forensic laboratories. 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.