Experimental use of a gas sensor-based instrument for differentiation of Escherichia coli O157:H7 from non-O157:H7 Escherichia coli field isolates

Abstract

The rapid and economical detection of human pathogens in animal and food production systems would enhance food safety efforts. An instrument based on gas sensors coupled with an artificial neural network (ANN) was developed for the detection of and differentiation between laboratory isolates of Escherichia coli O157:H7 and non-O157:H7 E. coli. The purpose of this study was to use field isolates of E. coli to further evaluate the sensor system. This gas sensor-based, computer-controlled detection system was used to monitor gas emissions from 12 isolates of E. coli O157:H7 and 8 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 h, and gas sampling was carried out every 5 min. Readings were continuously plotted to generate gas signatures. A back-propagation ANN algorithm was used to interpret the gas patterns. By analysis of the response of the ANN, the sensitivity and specificity of the instrument were calculated. Detectable differences between the gas signatures of the E. coli O157:H7 isolates and the non-O157:H7 isolates were observed. The instruments degree of sensitivity was high for E. coli O157:H7 isolates, but a lower degree of accuracy was observed for non-O157:H7 isolates because of increased strain variation. The sensitivity of the detection system was improved by the normalization of the data generated from the gas sensors. Because of its ability to detect differences in gas patterns, this instrument has a broad range of potential food safety applications.