Standoff Detection of Chemical and Biological Threats Using Laser-Induced Breakdown Spectroscopy

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for real-time chemical and biological warfare agent detection in the field. We have demonstrated the detection and discrimination of the biological warfare agent surrogates Bacillus subtilis (BG) (2% false negatives, 0% false positives) and ovalbumin (0% false negatives, 1% false positives) at 20 meters using standoff laser-induced breakdown spectroscopy (ST-LIBS) and linear correlation. Unknown interferent samples (not included in the model), samples on different substrates, and mixtures of BG and Arizona road dust have been classified with reasonable success using partial least squares discriminant analysis (PLS-DA). A few of the samples tested such as the soot (not included in the model) and the 25% BG:75% dust mixture resulted in a significant number of false positives or false negatives, respectively. Our preliminary results indicate that while LIBS is able to discriminate biomaterials with similar elemental compositions at standoff distances based on differences in key intensity ratios, further work is needed to reduce the number of false positives/negatives by refining the PLS-DA model to include a sufficient range of material classes and carefully selecting a detection threshold. In addition, we have demonstrated that LIBS can distinguish five different organophosphate nerve agent simulants at 20 meters, despite their similar stoichiometric formulas. Finally, a combined PLS-DA model for chemical, biological, and explosives detection using a single ST-LIBS sensor has been developed in order to demonstrate the potential of standoff LIBS for universal hazardous materials detection.

Towards the clinical application of laser-induced breakdown spectroscopy for rapid pathogen diagnosis: the effect of mixed cultures and sample dilution on bacterial identification

“…The use of computerized chemometric techniques like DFA, principal component analysis, or principal least squares-discriminant analysis has greatly increased the selectivity of the LIBS technique and reduced the time required to make identifications of unknown targets based on their LIBS spectra [29][30][31][32][33][34]. DFA is a statistical technique used for classifying a set of observations into mutually exclusive groups on the basis of a set of independent variables (predictors).…”

Laser-Induced Breakdown Spectroscopy: Capabilities and Applications

“…ARL has used LIBS for the detection of Halon alternative agents (19,20), tested a field-portable LIBS system for the detection of lead in soil and paint (10), studied the spectral emission of aluminum and aluminum oxide from bulk aluminum in different bath gases (21), performed kinetic modeling of LIBS plumes (22)(23)(24)(25), and demonstrated the detection and discrimination of geological materials (18,(26)(27)(28)(29)(30)(31), plastic landmines (32,33), explosives (34)(35)(36)(37)(38)(39)(40)(41)(42), and chemical and biological warfare agent surrogates (43)(44)(45)(46)(47). ARL has also published a number of reviews on LIBS (8,(48)(49)(50)(51)(52).…”

“…With recent advances in broadband spectrometers and chemometric analysis techniques, the identification of non-metals has become increasingly widespread with LIBS. LIBS has been used for the identification of polymers (43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57), thermoplasts (58)(59)(60), and other organic compounds (61,62), including explosives (34)(35)(36)(37)(38)(39)(40)(41)(42)(63)(64)(65). Figure 4 shows the LIBS spectra of various thermoplastic polymers from McMaster-Carr acquired with a commercial LIBS system (Applied Photonics, Ltd).…”

Laser Induced Breakdown Spectroscopy for Classification of High Energy Materials using Elemental Intensity Ratios

“…Exact assignment of these spectral lines is performed with comparison of the available standard atomic emission lines from NIST database 4 . The analysis of LIBS data (line intensities) combined with various statistical methods [5][6][7][8] provide plethora of opportunities for identification/ discrimination of high energy materials (HEMs). However, the detection mechanism, if carried out in ambient air, is ambiguous because of the contribution from atmospheric constituents such as Hydrogen, Nitrogen, and Oxygen 9 .…”