Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks

The aim of the present work is to investigate the capabilities of a wavelet neural network for describing the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in milk, and to compare its performance against classic neural network architectures and models utilised in food microbiology. A new wavelet network is being proposed that includes a "product operation" layer between wavelet functions and output layer, while the connection output-layer weights have been replaced by a local linear model. Milk was artificially inoculated with an initial population of the pathogen and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (25 degrees C). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas all learning-based networks were utilised in a standard identification approach. The prediction performance of the proposed local linear wavelet network was better at both validation pressures. The development of accurate models to describe the survival curves of microorganisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process. (C) 2011 Elsevier Ltd. All rights reserved.