| Rapid and economical detection of human pathogens in animal and food production systems would enhance food safety efforts. The objective of this research was to develop a gas sensor based instrument, coupled with an artificial neural network (ANN), which is capable of differentiating the human pathogen E. coli O157:H7 from non-O157:H7 E. coli isolates. The production of gases from eight laboratory isolates and 20 field isolates of E. coli were monitored during growth in laboratory conditions, and a unique gas signature for each isolate was generated. An ANN was used to analyze the gas signatures, and classify the bacteria as O157:H7 or non-O157:H7 E. coli. Detectable differences were observed between the gas signatures of the E. coli O157:H7 and non-O157:H7 isolates and the ANN classified the isolates with a high degree of accuracy. Based on this work, gas sensor based technology has promise as a diagnostic tool for pathogen detection in pre-harvest and post-harvest food safety. |
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