Standoff detection of explosives using laser induced breakdown spectroscopy combined with multivariate analysis

Abstract We report the development and optimization of a compact standoff laser-induced breakdown spectroscopy (ST-LIBS) system for the investigation of explosives combined with multivariate approaches. ST-LIBS in tandem with the artificial neural network (ANN) is exploited for the first time towards the identification of explosives to the best of our knowledge. We use a single plano-convex lens in conjunction with a compact CCD spectrometer for analyzing the optical response. The experimental setup was initially optimized by interrogating metal target at a standoff distance of ∼ 6.5 m and later performed on a set of five explosives and nineteen non-explosives. This study reveals that good signal strength, even in a single-shot mode with a minimum pulse energy of 100 mJ can be easily achieved with the spectrometers available on catalogs of standard companies. A 2D scatter plot approach and principal component analysis (PCA) have demonstrated an excellent separation among the explosives as well as among explosives and non-explosives. The identification accuracies of ∼ 98 and 94 % were achieved within explosives and among explosives and non-explosives respectively with ANN. These findings demonstrate that the developed standoff LIBS system has great potential in providing a flexible and portable remote analysis for industrial and security applications.