Sensitive detection and rapid identification of hazardous bioorganic material with high sensitivity and specificity are essential topics for defense and security. A single method can hardly cover these requirements. While point sensors allow a highly specific identification, they only provide localized information and are comparatively slow. Laser based standoff systems allow almost real-time detection and classification of potentially hazardous material in a wide area and can provide information on how the aerosol may spread. The coupling of both methods may be a promising solution to optimize the acquisition and identification of hazardous substances. The capability of the outdoor LIF system at DLR Lampoldshausen test facility as an online classification tool has already been demonstrated. Here, we present promising data for further differentiation among bacteria. Bacteria species can express unique fluorescence spectra after excitation at 280 nm and 355 nm. Upon deactivation, the spectral features change depending on the deactivation method.
The high and still increasing number of attacks by hazardous bioorganic materials makes enormous demands on their detection. A very high detection sensitivity and differentiability are essential, as well as a rapid identification with low false alarm rates. One single technology can hardly achieve this. Point sensors can collect and identify materials, but finding an appropriate position is time consuming and involves several risks. Laser based standoff detection, however, can immediately provide information on propagation and compound type of a released hazardous material. The coupling of both methods may illustrate a solution to optimize the acquisition and detection of hazardous substances.
At DLR Lampoldshausen, bioorganic substances are measured, based on laser induced fluorescence (LIF), and subsequently classified. In this work, a procedure is presented, which utilizes lots of information (time-dependent spectral data, local information) and predicts the presence of hazardous substances by statistical data analysis. For that purpose, studies are carried out on a free transmission range at a distance of 22m at two different excitation wavelengths alternating between 280nm and 355 nm. Time-dependent fluorescence spectra are recorded by a gated intensified CCD camera (iCCD). An automated signal processing allows fast and deterministic data collection and a direct subsequent classification of the detected substances. The variation of the substance parameters (physical state, concentration) is included within this method.
The challenges of detecting hazardous biological materials are manifold: Such material has to be discriminated from other substances in various natural surroundings. The detection sensitivity should be extremely high. As living material may reproduce itself, already one single bacterium may represent a high risk. Of course, identification should be quite fast with a low false alarm rate. Up to now, there is no single technique to solve this problem. Point sensors may collect material and identify it, but the problems of fast identification and especially of appropriate positioning of local collectors are sophisticated. On the other hand, laser based standoff detection may instantaneously provide the information of some accidental spillage of material by detecting the generated thin cloud. LIF technique may classify but hardly identify the substance. A solution can be the use of LIF technique in a first step to collect primary data and – if necessary- followed by utilizing these data for an optimized positioning of point sensors. We perform studies on an open air laser test range at distances between 20 and 135 m applying LIF technique to detect and classify aerosols. In order to employ LIF capability, we use a laser source emitting two wavelengths alternatively, 280 and 355 nm, respectively. Moreover, the time dependence of fluorescence spectra is recorded by a gated intensified CCD camera. Signal processing is performed by dedicated software for spectral pattern recognition. The direct comparison of all results leads to a basic classification of the various compounds.
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