(Received 22 January 1997; accepted 14 July 1997)
Published Online: March
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The purpose of this work was to investigate the utility of electronic aroma detection technologies for the detection and identification of ignitable liquid accelerants and their residues in suspected arson debris. Through the analysis of “known” accelerants and residues, a trained neural network was developed for classifying fire debris samples. Three “unknown” items taken from actual fire debris that had contained the fuels, gasoline, kerosene, and diesel fuel, were classified using this neural network. One item, taken from the area known to have contained diesel fuel, was correctly identified as diesel fuel residue every time. For the other two “unknown” items, variations in sample composition, possibly due to the effects of weathering or increased sample humidities, were shown to influence the sensor response. This manifested itself in inconsistent fingerprint patterns and incorrect classifications by the neural network. Sorbent sampling prior to aroma detection was demonstrated to reduce these problems and allowed improved neural network classification of the remaining items which were identified as kerosene and gasoline residues.
Staff scientist, Oak Ridge National Laboratory, managed by Lockheed Martin Energy Research Corp., U.S. Department of Energy under contract DE-AC05-96OR22464,
Stock #: JFS16134J