Artificial neural networks: a promising tool to design and optimize high-pressure food processes

In this work, an artificial neural network (ANN) is used to predict two parameters of interest for high-pressure food processing: the maximum or minimum temperature reached in the sample after pressurization and the time needed for thermal re-equilibration in the high-pressure system. Both variables together represent in a reliable form the temperature evolution during the high-pressure process. The ANN was trained with a data file composed of: applied pressure, pressure increase rate, set point temperature, high-pressure vessel temperature and ambient temperature altogether with the parameters to predict. After a proper training, the ANN was able to make predictions accurately and therefore, it becomes a useful tool to design and optimize high-pressure processes in the food industry where the pressure/temperature evolution is an essential factor to control the microbiological and/or enzymatic activity of the products.