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Detection of Escherichia coli in packaged alfalfa sprouts with an electronic
nose and an artificial neural network
01.aug.06
Journal of Food Protection Volume 69, Number 8, pp. 1844-1850(7)
Siripatrawan, Ubonrat; Linz, John E.; Harte, Bruce R.
Abstract:
A rapid method for the detection of Escherichia coli (ATCC 25922) in packaged
alfalfa sprouts was developed. Volatile compounds from the headspace of packaged
alfalfa sprouts, inoculated with E. coli and incubated at 10°C for 1, 2, and 3
days, were collected and analyzed. Uninoculated sprouts were used as control
samples. An electronic nose with 12 metal oxide electronic sensors was used to
monitor changes in the composition of the gas phase of the package headspace
with respect to volatile metabolites produced by E. coli. The electronic nose
was able to differentiate between samples with and without E. coli. To predict
the number of E. coli in packaged alfalfa sprouts, an artificial neural network
was used, which included an input layer, a 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. The network was shown to be
capable of correlating voltametric responses with the number of E. coli. A good
prediction was possible, as measured by a regression coefficient (R2 = 0.903)
between the actual and predicted data. In conjunction with the artificial neural
network, the electronic nose proved to have the ability to detect E. coli in
packaged alfalfa sprouts.
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