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

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.