Journal
JOURNAL OF FOOD ENGINEERING
Volume 69, Issue 3, Pages 299-306Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2004.08.020
Keywords
artificial neural networks; high-pressure; modeling; food processing; heat transfer
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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. (c) 2004 Elsevier Ltd. All rights reserved.
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