FTIR spectral imaging applications in trace evidence

Infrared spectral (or chemical) imaging is emerging as an exciting new tool in forensic analysis. Here we present some of our work in the application of infrared spectral imaging to different types of trace evidence: automotive paint chips, bicomponent fibers and the detection of illicit substances in fingerprints. INTRODUCTION Fourier Transform Infrared (FTIR) spectral (or chemical) imaging is emerging as an exciting new tool in forensic analysis. It involves the simultaneous collection of multiple mid-infrared spectra across a given sample. Since the mid-infrared region is capable of discriminating between different molecules, the data collected can be used to visualize chemical differences across the sample. The Digilab ‘Stingray’® infrared chemical imaging system used in this study has a 64 × 64 pixel mercury cadmium telluride (MCT) focal plane array (FPA) detector. Using this detector, 4096 infrared spectra can be obtained simultaneously in the form of an image, with one spectrum recorded for each pixel. This is in contrast with conventional infrared microscopy, where only one spectrum can be collected at a time. The image data collected can be thought of as a three-dimensional datacube, which contains vertical and horizontal spatial dimensions, and a spectral frequency dimension. There are many advantages the technique can bring to forensic analysis including the ability to obtain images which show contrast based on chemical differences, an obvious advantage for imaging fingerprints (1) and questioned document analysis e.g. intersecting lines. This technique has also been applied to forensic trace evidence such as multi-layered paint chips (2) and bicomponent fibers (3). The simultaneous visualization and identification of chemical differences in samples allows for new, more powerful ways to analyze and present trace evidence in a legal context. The large number of spectra which are rapidly obtained from the sample increases the confidence in the results obtained, and multivariate statistics can be used on the large datasets to draw more powerful conclusions from the evidence.