Article,
Microbial growth modelling with artificial neural networks
International Journal of Food Microbiology, 64 (3): 343--354 (2001)
Abstract
There is a growing interest in modelling microbial growth as an alternative
to time-consuming, traditional, microbiological enumeration techniques.
Several statistical models have been reported to describe the growth
of different microorganisms, but there are accuracy problems. An
alternate technique 'artificial neural networks' (ANN) for modelling
microbial growth is explained and evaluated. Published data were
used to build separate general regression neural network (GRNN) structures
for modelling growth of Aeromonas hydrophila, Shigella flexneri,
and Brochothrix thermosphacta. Both GRNN and published statistical
model predictions were compared against the experimental data using
six statistical indices. For training data sets, the GRNN predictions
were far superior than the statistical model predictions, whereas
the GRNN predictions were similar or slightly worse than statistical
model predictions for test data sets for all the three data sets.
GRNN predictions can be considered good, considering its performance
for unseen data. Graphical plots, mean relative percentage residual,
mean absolute relative residual, and root mean squared residual were
identified as suitable indices for comparing competing models. ANN
can now become a vehicle whereby predictive microbiology can be applied
in food product development and food safety risk assessment. Copyright
� 2001 Elsevier Science B.V.
Tags
- artificial
- general
- microbial
- microbiology
- modelling,
- models,
- network,
- networks,
- neural
- polynomial
- predictive
- regression
