, Volume 400, Issue 10, pp 3289-3301

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates

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Abstract

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.

Figure

Laser - induced breakdown spectroscopy (LIBS) is a promising technique under development for the detection of hazardous materials. LIBS offers the potential for real-time chemical and biological threat detection in the field for military and civilian first responders.

Published in the special issue Laser-Induced Breakdown Spectroscopy with Guest Editors Jagdish P. Singh, Jose Almirall, Mohamad Sabsabi, and Andrzej Miziolek.