Rapid method for prediction of Escherichia coli numbers using an electronic sensor array and an artificial neural network.
An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37 degrees C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 10(5) CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography-mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.
Siripatrawan
Ubonratana
U
Department of Food Technology, Chulalongkorn University, Bangkok 10330, Thailand.
Linz
John E
JE
Harte
Bruce R
BR
D015374:Biosensing Techniques; D015169:Colony Count, Microbial; D004563:Electrochemistry; D004566:Electrodes; D004926:Escherichia coli / Q000254:growth & development* / Q000302:isolation & purification; D005506:Food Contamination / Q000032:analysis*; D005516:Food Microbiology; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D014835:Volatilization