Multivariate analysis techniques in the forensics investigation of the postblast residues by means of fourier transform-infrared spectroscopy

Abstract

Fourier transform-infrared (FT-IR) spectroscopy has gained considerable attention among the forensic scientists because it shows high sensitivity and selectivity and offers near real time detection of analyzed samples. However, the amount of obtained information due to complexity of the measured spectra forces the use of additional data processing. Application of the multivariate statistical techniques for the analysis of the FT-IR data seems to be necessary in order to enable feature extraction, proper evaluation, and identification of obtained spectra. In this article, an attempt to develop a feasible procedure for characterization of spectroscopic signatures of the explosive materials in the remnants after explosion has been made. All spectra were derived after analysis of samples from debris after especially prepared and performed blasts with the use of three various highly explosive materials: C-4, 2,4,6-trinitrotoluene (TNT), and pentaerythritol tetranitrate (PETN). Two well-known multivariate statistical methods, hierarchical cluster analysis (HCA) and principal component analysis (PCA), were tested in order to classify the samples into separate classes using a broad wavelength data range (4000-600 cm-1) on collected spectra sets. After many trials it seems that PCA is the best choice for the mentioned earlier tasks. It was found that only three principal components carry over 99.6% of variance within the sample set. The results show that FT-IR spectroscopy in combination with multivariate methods is well-suited for identification and differentiation purposes even in quite large data sets, and for that reason forensic laboratories could employ these methods for rapid screening analysis. © 2010 American Chemical Society.