Laser-Induced Breakdown Spectroscopy for the Discrimination of Explosives Based on the ReliefF Algorithm and Support Vector Machines

Real-time explosive detectors must be developed to facilitate the rapid implementation of appropriate protective measures against terrorism. We report a simple yet efficient methodology to classify three explosives and three non-explosives by using laser-induced breakdown spectroscopy. However, the similarity existing among the spectral emissions collected from the explosives resulted in the difficulty of separating samples. We calculated the weights of lines by using the ReliefF algorithm and then selected six line regions that could be identified from the arrangement of weights to calculate the area of each line region. A multivariate statistical method involving support vector machines was followed for the construction of the classification model. Several models were constructed using full spectra, 13 lines, and 100 lines selected by the arrangement of weights and areas of the selected line regions. The highest correct classification rate of the model reached 100% by using the six line regions.