Signal processing for the detection of explosive residues on varying substrates using laser induced breakdown spectroscopy

Laser induced breakdown spectroscopy (LIBS) can provide rapid, minimally destructive, chemical analysis of substances with the benefit of little to no sample preparation. Therefore, LIBS is a viable technology for the detection of substances of interest in near real-time fielded remote sensing scenarios. Of particular interest to military and security operations is the detection of explosive residues on various surfaces. It has been demonstrated that LIBS is capable of detecting such residues, however, the surface or substrate on which the residue is present can alter the observed spectra. Standard chemometric techniques such as principal components analysis and partial least squares discriminant analysis have previously been applied to explosive residue detection, however, the classification techniques developed on such data perform best against residue/substrate pairs that were included in model training but do not perform well when the residue/substrate pairs are not in the training set. Specifically residues in the training set may not be correctly detected if they are presented on a previously unseen substrate. In this work, we explicitly model LIBS spectra resulting from the residue and substrate to attempt to separate the response from each of the two components. This separation process is performed jointly with classifier design to ensure that the classifier that is developed is able to detect residues of interest without being confused by variations in the substrates. We demonstrate that the proposed classification algorithm provides improved robustness to variations in substrate compared to standard chemometric techniques for residue detection. © 2011 SPIE.