A neural network approach for thermal/pressure food processing Original Research Article

High-pressure processing is an interesting technology for the food industry that offers some important advantages compared to thermal processing. But, the results obtained after a pressure treatment depend as much on the applied pressure as the temperature during the treatment. Modelling the thermal behaviour of foods during high-pressure treatments using physical-based models is a really hard task. The main difficulty is the almost complete lack of values for thermophysical properties of foods under pressure. In this work, an artificial neural network (ANN) was carried out to evaluate its capability in predicting process parameters involved in thermal/pressure food processing. The ANN was trained with a data file composed of: applied pressure, pressure increase rate, set point temperature, high-pressure vessel temperature, ambient temperature and time needed to re-equilibrate temperature in the sample after pressurisation. When ANN was trained, it was able to predict accurately this last variable. Then, it becomes a useful alternative to physical-based models for process control since thermophysical properties of products implied are not needed in modellisation.