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Release of hazardous substances may cause severe consequences to humans and infrastructure. A fast detection system classifying these substances can be used to initiate countermeasures quickly and reduce damage to general public significantly. Nowadays, the investigation of such materials is time and money consuming but essential for damage limitation. Current procedures take long and require methods that involve measurements at close range and/or sampling for subsequent laboratory analyses. Both, sampling time and distance, can be improved using standoff laser induced fluorescence (LIF) spectroscopy which enables detection in seconds reaching distances over 100 m. By now the specificity of the technique is not sufficient to identify samples but it can be helpful for risk assessment and to guide first responders to salient regions for subsequent in situ measurements. This contribution presents an interactive graphical user interface as well as the practical workflow from generating training data and classification models to forecasting new records concurrently after the measurement. The foregoing modeling process is based on datasets generated previously with well-defined samples. Each sample from a considerable set of different chemical, botanical and bacterial substances can be distinguished using the LIF signals excited with short laser pulses of two UV wavelengths within a few seconds. Simultaneously, the fluorescence lifetime is recorded to provide additional information for a further enhanced discriminability. Estimating the sample species for new measurements consumes just a few seconds - including data acquisition, preprocessing, model application and visualization to the operator. As an example, the workflow is presented together with performance results for a test classification of 20 different substances.

After accidental or intended release of chemical or biological hazardous materials, fast detection and classification is mandatory to be able to rapidly take steps against negative consequences on humans. Standoff laser based devices are suited to localize and classify a thread on short timescales without the need of direct human contact to these materials. In this scope we present the classification prospects of our standoff detection system that utilizes laser induced fluorescence signals excited with laser pulses of two different UV excitation wavelengths. For fast data acquisition we use a rather limited spectral resolution of 32 channels per wavelength which is sufficient to achieve a good performance. The described system is able to perform a complete classification procedure including data acquisition, data analysis and classification and to get an assignment of the measured sample after a few seconds. The identification and classification prospects are evaluated by classifying measured sample spectra using a model which is based on a previously acquired reference database. Within the set, different representatives for bacterial and chemical agents as well as background materials as diesel and pollen are investigated. The performance, namely the classification accuracy and false alarm rates, is evaluated for differently sized classes down to an identification of the investigated set of materials.