Range-Adaptive Standoff Recognition of Explosive Fingerprints on Solid Surfaces using a Supervised Learning Method and Laser-Induced Breakdown Spectroscopy

Abstract

The distance between the sensor and the target is a particularly critical factor for an issue as crucial as explosive residues recognition when a laser-assisted spectroscopic technique operates in a standoff configuration. Particularly for laser ablation, variations in operational range influence the induced plasmas as well as the sensitivity of their ensuing optical emissions, thereby confining the attributes used in sorting methods. Though efficient classification models based on optical emissions gathered under specific conditions have been developed, their successful performance on any variable information is limited. Hence, to test new information by a designed model, data must be acquired under operational conditions totally matching those used during modeling. Otherwise, the new expected scenario needs to be previously modeled. To facing both this restriction and this time-consuming mission, a novel strategy is proposed in this work. On the basis of machine learning methods, the strategy stems from a decision boundary function designed for a defined set of experimental conditions. Next, particular semisupervised models to the envisaged conditions are obtained adaptively on the basis of changes in laser fluence and light emission with variation of the sensor-to-target distance. Hence, the strategy requires only a little prior information, therefore ruling out the tedious and time-consuming process of modeling all the expected distant scenes. Residues of ordinary materials (olive oil, fuel oil, motor oils, gasoline, car wax and hand cream) hardly cause confusion in alerting the presence of an explosive (DNT, TNT, RDX, or PETN) when tested within a range from 30 to 50 m with varying laser irradiance between 8.2 and 1.3 GW cm(-2). With error rates of around 5%, the experimental assessments confirm that this semisupervised model suitably addresses the recognition of organic residues on aluminum surfaces under different operational conditions.

A critical review of recent progress in analytical laser-induced breakdown spectroscopy

“…The discrimination capabilities and robustness of a range of multivariate chemometric methods were tested. Stand-off and mobile LIBS instruments, tandem Raman-LIBS instrumentation [83,205], and novel samplepresentation methods (e.g. optical catapulting [32]) were also developed for this specific application.…”

Nanomaterials for luminescence detection of nitroaromatic explosives

“…Further requirements of luminescence methods, source and detector can be incorporated into a hand-held device, which could be easily used for field detection of nitroaromatics [47]. Fast, or extremely fast (with a response time of a few seconds) and ultrasensitive detection of nitroaromatics (even trace-level detection by the naked eye) in challenging environments (e.g., fingerprints, leather, soil, printer, clothing or at the scene of terrorist attack) could be achieved.…”

Qualitative discrimination of coal aerosols by using the statistical evaluation of laser-induced breakdown spectroscopy data

“…Mukherjee and Cheng investigated how different mass loadings in the plasma induce matrix effects during the quantitative analysis of carbonaceous aerosols [12]. At the same time, no studies have addressed the on-line, qualitative discrimination of coal aerosols according to their source coal type so far, although the potential of LIBS for the classification/identification of samples has already been successfully demonstrated on various samples, for example in microbiological [25][26][27], medical [28,29], industrial [16,30,31] or forensic [32][33][34] applications.…”