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Título

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

AutorTorrecilla, J. S.; Otero, Laura ; Sánz Martines, Pedro Dimas
Palabras claveModeling
Food processing
Heat transfer
High-pressure
Artificial neural networks
Fecha de publicación2005
EditorElsevier
CitaciónJournal of Food Engineering 69: 299- 306 (2005)
ResumenIn 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. © 2004 Elsevier Ltd. All rights reserved.
URIhttp://hdl.handle.net/10261/121256
DOI10.1016/j.jfoodeng.2004.08.020
Identificadoresdoi: 10.1016/j.jfoodeng.2004.08.020
issn: 0260-8774
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