Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks

The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for “intelligent” methods to model highly nonlinear systems is long established. The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology.