Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2003
Publication Date: 8/1/2003
Citation: Younts, S., Alocilja, E., Osburn, W., Marquie, S., Gray, J.T., Grooms, D. Experimental use of a gas sensor based instrument for differentiation of e. coli o157:h7 from non-o157:h7 e. coli field isolates. Journal of Food Protection. 66(8):1455-1458. 2003.
Interpretive Summary: E. coli O157:H7 is an important foodborne disease in the United States. The organism causes numerous illnesses, some very severe, often in children. The disease is associated with eating ground beef and has been termed 'hamburger disease.' Appropriate methods to rapidly detect E. coli 0157:H7 in various environments are lacking to assist in the control of this organism. In this study we evaluated the ability of a new detection method to differentiate E. coli O157:H7 that were obtained from field outbreaks. The new method consists of a gas sensor, or artificial nose, and an artificial neural network, or electronic brain. The electronic system was initially trained to detect known E. coli O157:H7 and then tested for its ability to detect field strains of the bacteria. E. coli strains were obtained from human outbreaks of disease, as well as isolates from cattle and cattle feedlots. These isolates were identified as E. coli and they were then subjected to the electronic nose testing. The new system detected all the E. coli O157:H7 positive isolates but misidentified some of the non-E. coli O157:H7 isolates. The non-E. coli 0157:H7 isolates varied greatly in their ability to be deleted. However, with further adjustment of the artificial neural network the accuracy improved greatly. Based on the ability to detect differences in gas patterns, this technology has a broad scope of potential food safety applications and should be useful to industry and scientists alike.
Technical Abstract: Rapid and economical detection of human pathogens in animal and food production systems would enhance food safety efforts. An instrument was developed based on gas sensors, coupled with an artificial neural network (ANN), to detect and differentiate between laboratory isolates of E. 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. The gas sensor-based, computer controlled detection system was used to monitor the 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 10ml of nutrient broth at 37 degrees C for 16 hours with gas sampling conducted every five minutes. Readings were continuously plotted to generate gas signatures. A back propagation artificial neural network algorithm was used to interpret the gas patterns. Analyzing the response of the ANN, the sensitivity and specificity of the instrument was calculated. Detectable differences were observed between the gas signatures of the E. coli O157:H7 and the non-O157:H7 isolates. The instrument had a high degree of sensitivity for E. coli O157:H7 isolates, but lower accuracy for non-O157:H7 isolates because of increased strain variation. The sensitivity of the detection system was improved following normalization of the data generated from the gas sensors. Based on the ability to detect differences in gas patterns, this technology has a broad scope of potential food safety applications.