Forensic Classification of Paper with Infrared Spectroscopy and Principal Components Analysis

Abstract

Fourier transform infrared (FT-IR) spectra of six document papers were classified by the chemometric technique of soft independent modelling of class analogy (SIMCA) using principal component analysis (PCA). The data were split into two regions, mid infrared (MIR, 2500–4000 cm−1) and the near infrared region (NIR, 4000–9000 cm−1). The raw data failed to produce an appreciable separation; hence it was pre-processed using derivatives and the multiple wavenumber ratios technique. The aim of this research was to determine the spectral region with the best discriminating power and evaluate the impact of data pre-processing techniques on the classification of paper. It was found that MIR (both raw and ratios) had higher discriminating ability than the NIR. The first derivatives and two ratios produced the best classifications. These results indicate that IR spectroscopy coupled with chemometric analyses can be effectively employed for the forensic classification of paper.