Online discrimination of chemical substances using standoff laser induced fluorescence signals

Abstract

Chemical contamination of objects and areas, caused by accident or on purpose, is a common scenario. The immediate countermeasures depend on the class of risk and consequently on the characteristics of the substances. Standoff laser based detection techniques provide this information without direct contact of humans to the hazardous materials. This presentation explains the data acquisition and classification procedure for laser induced fluorescence spectra of several chemical agents. The substances are excited from a distance of 3.5 m by laser pulses of two UV wavelengths (266 and 355 nm) with less than 0.1 mJ and a repetition rate of 100 Hz. Each pair of simultaneously emitted laser pulses is temporally separated by an optical delay line. Every measurement consists of a dataset of 100 spectra per wavelength containing the signal intensities in the spectral range of approximately 250 nm and 680 nm, recorded by a 32 channel photo multiplying tube array. Based on these datasets three classification algorithms are trained which can distinguish the samples by their single spectra with an accuracy of over 95 %. These predictive models, generated with decision trees, support vector machines and neural networks, can identify all agents (e.g. benzaldehyde, isoproturon and piperine) within the current set of substances.