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.

Effect of UV-C irradiation and heat treatment on the shelf life stability of a lemon–melon juice blend: multivariate statistical approach

“…PCA is a widely used multivariate analysis technique based on the transformation of original measurement variables into new variables called principal components (PC). Each principal component is a linear combination of the original measurement variables (Banas et al, 2010). A data matrix was constructed using the analyzed physicochemical properties in columns and the days during shelf life in rows.…”

“…Clustering analysis was computed in consideration of Ward's linkage as an amalgamation method and Euclidean distance as a similarity measurement. HCA resulted in the similarity of observations given as a dendrogram, which is defined as a tree-shaped map of the inter sample distances in the data set by Banas et al, 2010.…”

Multivariate statistical methods in the forensic investigation of the post-blast residues measured by Fourier transform infrared spectroscopy

“…Especially designed sample catchers (Banas et al 2009) were used in order to maximize the number of residues obtained after each explosion. All post-blast residues †…”

“…Especially designed sample catchers (Banas et al 2009) were used in order to maximize the number of residues obtained after each explosion. All post-blast residues were collected in separate plastic bags and transported to the laboratory at Singapore Synchrotron Light Source (SSLS).…”

Multivariate Analysis in Vibrational Spectroscopy of Highly Energetic Materials and Chemical Warfare Agents Simulants

“…Vibrational spectroscopy has demonstrated to be valuable for the detection of HEM, HME, CWA and Simulants (CWAS) and Toxic Industrial Compounds (TIC). In particular, infrared spectroscopy (IRS) and Raman spectroscopy (RS) in various modalities have played unique roles in threat compounds detection [6,[12][13][14][15][16][17][18][19]. IRS and RS can be employed for detection of Explosives and Chemical Warfare Agents, as well as other chemical and biological threats in airports, in military environments, in government buildings and other public safety places.…”