We have demonstrated the detection and discrimination of explosive residues on a variety of surfaces using laser-induced breakdown spectroscopy and partial least squares discriminant analysis. The correlation between the hardness of a substrate and the ability to discriminate residues on that substrate is discussed. We have also shown that while using the full spectra in the chemometric model improves the classification of sample types in the model, the use of intensities and ratios specific to explosive residues improves the classification of sample types that are not part of the model. In the first case, differences in the laser-induced plasma (and resulting spectral features) attributable to the laser-material interaction contribute to the sample discrimination, while using intensities and ratios specific to explosive residues focuses on the chemical composition of the sample for discrimination. Combining the results of the two models for a given sample set decreases the overall false positive rate for all test samples.
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Gottfried et al. (2009) studied this question.
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