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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. 10.1021/ac100115r

As we live under a constant threat of global terrorism, the effective detection of highly energetic materials is one of the critical procedures needed at a variety of locations, including airports, border checkpoints, and entrances to high-security buildings. In this work, the application of optical-photothermal infrared (O-PTIR) spectromicroscopy for the detection of highly explosive materials within fingerprints is described. High-explosive (HE) materials (e.g., PETN, RDX, C-4, or TNT) were used to prepare contaminated fingerprints. These were subsequently deposited on various objects, including microscopic glass slides, a table, a mug, etc. Samples deposited on glass slides were directly sent for analyses; for other samples, adhesive tapes were used to lift off fingermarks. In cases of difficulty in locating fingerprints, additional powders were used to enhance their visibility. Experiments were performed with a mIRage IR microscope working in a noncontact, far-field reflection mode, offering submicron IR spectroscopy and imaging. Fast imaging (several characteristic absorbances were selected for every substance of interest) was used to locate "suspicious" particles among various residues present in fingerprints. Subsequently, spectra were collected for those particles. Reflection mode O-PTIR spectra taken from powdered and nonenhanced fingerprints were of comparable quality to transmission mode FTIR spectra collected for pure HEs. On the basis of the performed experiments, we consider O-PTIR spectromicroscopy to open a new avenue for the nondestructive, efficient, and reliable analysis of exogenous substances deposited within fingerprints. The real significance of O-PTIR is in its ability to deliver high-quality, spatially resolved FTIR transmission-like spectra below the diffraction limit of infrared wavelengths, doing so in an easy-to-use reflection (far-field) mode. Collected spectra are also searchable and interpretable in both commercial and institutional IR databases without mathematical modeling.

The objective of this work was to measure infrared spectra of high explosive materials (HE) in wide spectral range in order to acquire information for their complete characterization and find out the regions that are the most discriminatory for each material. Four HEs were measured by means of Fourier Transform Infrared (FTIR) spectroscopy in a very broad range (from near- via mid- to far-IR). Obtained spectra were subsequently evaluated using multivariate statistical methods for dimension reduction and results grouping. Clustering was assessed in terms of compactness and stability in order to distinguish which region or regions are most suitable for the identification based on spectral signature. Based on outcomes of visualization method (silhouette plot) used to compare results of implemented chemometric methods (HCA, PAM, and PCA) done on FTIR spectra collected for four high explosive materials (PETN, C-4, RDX, and TNT) within all regions, it seems that the mid-IR region is the most informative for the distinction among analyzed HE materials based on substance spectral signatures. However, it is worth noticing that also the near-IR region can be used for good differentiation.

Assessment of the performance and up-to-date diagnostics of scientific equipment is one of the key components in contemporary laboratories. Most reliable checks are performed by real test experiments while varying the experimental conditions (typically, in the case of infrared spectroscopic measurements, the size of the beam aperture, the duration of the experiment, the spectral range, the scanner velocity, etc.). On the other hand, the stability of the instrument response in time is another key element of the great value. Source stability (or easy predictable temporal changes, similar to those observed in the case of synchrotron radiation-based sources working in non top-up mode), detector stability (especially in the case of liquid nitrogen- or liquid helium-cooled detectors) should be monitored. In these cases, recorded datasets (spectra) include additional variables such as time stamp when a particular spectrum was recorded (in the case of time trial experiments). A favorable approach in evaluating these data is building hyperspectral object that consist of all spectra and all additional parameters at which these spectra were recorded. Taking into account that these datasets could be considerably large in size, there is a need for the tools for semiautomatic data evaluation and information extraction. A comprehensive R archive network--the open-source R Environment--with its flexibility and growing potential, fits these requirements nicely. In this paper, examples of practical implementation of methods available in R for real-life Fourier transform infrared (FTIR) spectroscopic data problems are presented. However, this approach could easily be adopted to many various laboratory scenarios with other spectroscopic techniques.