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 accelerant residues in suspected arson debris. Through the analysis of known accelerant residues, a trained neural network was developed for classifying suspected arson samples. Three unknown fire debris samples were classified using this neural network. The item corresponding to diesel fuel was correctly identified every time. For the other two items, wide variations in sample concentration and excessive water content, producing high sample humidities, were shown to influence the sensor response. Sorbent sampling prior to aroma detection was demonstrated to reduce these problems and to allow proper neural network classification of the remaining items corresponding to kerosene and gasoline