Multivariate Analysis Techniques in the Forensics Investigation of the Postblast Residues by Means of Fourier Transform-Infrared Spectroscopy

摘要

Fourier transform-infrared (FT-IR) spectroscopy has gainednconsiderable attention among the forensic scientists becausenit shows high sensitivity and selectivity and offersnnear real time detection of analyzed samples. However,nthe amount of obtained information due to complexity ofnthe measured spectra forces the use of additional datanprocessing. Application of the multivariate statisticalntechniques for the analysis of the FT-IR data seems to bennecessary in order to enable feature extraction, propernevaluation, and identification of obtained spectra. In thisnarticle, an attempt to develop a feasible procedure forncharacterization of spectroscopic signatures of the explosivenmaterials in the remnants after explosion has beennmade. All spectra were derived after analysis of samplesnfrom debris after especially prepared and performednblasts with the use of three various highly explosivenmaterials: C-4, 2,4,6-trinitrotoluene (TNT), and pentaerythritolntetranitrate (PETN). Two well-known multivariatenstatistical methods, hierarchical cluster analysisn(HCA) and principal component analysis (PCA), werentested in order to classify the samples into separatenclasses using a broad wavelength data range (4000-600ncm-1) on collected spectra sets. After many trials itnseems that PCA is the best choice for the mentionednearlier tasks. It was found that only three principalncomponents carry over 99.6% of variance within thensample set. The results show that FT-IR spectroscopynin combination with multivariate methods is well-suitednfor identification and differentiation purposes even innquite large data sets, and for that reason forensicnlaboratories could employ these methods for rapidnscreening analysis.