PMID: 11934019Apr 6, 2002Paper

Application of recurrent neural network to predict bacterial growth in dynamic conditions

International Journal of Food Microbiology
M Cheroutre-Vialette, A Lebert

Abstract

A combination of a factorial design and two central composite designs was used to assess quantitatively the effects of acid pH (5.6-7.0) or alkaline pH (7.0-9.5) and NaCl (0-8%) variations on the growth of Listeria monocytogenes in a meat broth, at 20 degrees C and lower temperature 10 degrees C. Two principal phenomena were observed when bacteria were submitted to abrupt change of pH and a(w) during growth, whatever the growth temperature: (i) large environmental variations induced a lag phase following the fluctuation, and (ii) the growth continued with a generation time value different from that observed before the change or that associated to the new environment. A dynamic model, based on recurrent neural network (RNN), was developed to describe the growth of L. monocytogenes as a function of temperature and fluctuating conditions of acid pH, alkaline pH and concentration of NaCl. The results showed that the neural network model can be used to represent the complex effects of environmental variable conditions on the microorganism behaviour.

References

May 1, 1995·International Journal of Food Microbiology·J F Van ImpeJ De Baerdemaeker
Jan 1, 1997·International Journal of Food Microbiology·Y M NajjarM N Hajmeer
Jan 1, 1997·International Journal of Food Microbiology·M N HajmeerY M Najjar
Aug 26, 1998·International Journal of Food Microbiology·M Cheroutre-VialetteA Lebert
Nov 26, 1999·International Journal of Food Microbiology·K McDonald, D W Sun
May 3, 2000·International Journal of Food Microbiology·M Cheroutre-Vialette, A Lebert

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Citations

Feb 21, 2003·International Journal of Food Microbiology·M N Hajmeer, I A Basheer
Mar 20, 2010·Critical Reviews in Food Science and Nutrition·Peter J Taormina
Jan 1, 2014·BMC Systems Biology·Daniel MadarUri Alon
Nov 13, 2007·Journal of Food Science·A ValeroG Zurera

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