Journal Published Online: 01 March 1998
Volume 43, Issue 2

Analysis of Accelerants and Fire Debris Using Aroma Detection Technology



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.

Author Information

Barshick, S-A
Oak Ridge National Laboratory, managed by Lockheed Martin Energy Research Corp., U.S. Department of Energy under contract DE-AC05-96OR22464
Pages: 10
Price: $25.00
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Stock #: JFS16134J
ISSN: 0022-1198
DOI: 10.1520/JFS16134J