Single-shot compact spectrometer based standoff LIBS configuration for explosive detection using artificial neural networks

摘要

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 pianoconvex 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 similar to 6.5 m and later exploited for the study on a set of five explosives and nineteen nonexplosives. 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 compact 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 similar to 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.