Differentiation of Escherichia coli O157:H7 from non-O157:H7 E. coli serotypes using a gas sensor-based, computer-controlled detection system
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abstract
Rapid and economical detection of human pathogens in animal and food production systems would enhance food safety efforts. Gas sensors, coupled with an artificial neural network, have been used to detect and differentiate between different species of bacteria. The purpose of this project was to develop a sensor-based, computer-controlled detection system to differentiate Escherichia coli O157:H7 from non-O157:H7 strains. The detection system was used to monitor the gas emissions from four isolates of E. coli O157:H7 and four non-O157:H7 E. coli isolates. A standard concentration of each isolate was grown in 10 mL of nutrient broth at 37 C for 16 hours with gas measurement every 5 minutes, resulting in a gas signature. Detectable differences were observed between the gas patterns of the E. coli O157:H7 and the non-O157:H7 E. coli isolates. A backpropagation neural network (BPN) algorithm was used to interpret the gas patterns. Analyzing the response of the BPN, the sensitivity and specificity of the instrument were calculated. Based on the ability to detect differences in the gas patterns, this technology has a broad scope of potential applications with promise as a diagnostic tool for pathogen detection in pre-harvest and post-harvest food safety.
