Title :
An artificial neural network for optimizing safety and quality in thermal food processing
Author :
Kseibat, D. ; Basir, O.A. ; Mittal, G.S.
Author_Institution :
Sch. of Eng., Guelph Univ., Ont., Canada
Abstract :
Presents a backpropagation artificial neural network for optimizing food safety and quality in thermal processing applications. Five inputs (can size, initial temperature, thermal diffusivity, sensitivity indicator of microorganism, and sensitivity indicator of quality) are used as inputs to the network. The network computes the optimal control parameters (sterilization temperature, process time) and quality degradation of the food. This study is based on a wide range of microorganisms involved in foods
Keywords :
backpropagation; food processing industry; heat transfer; multilayer perceptrons; optimal control; process control; quality control; safety; thermal diffusivity; backpropagation artificial neural network; can size; food quality; food safety; initial temperature; microorganisms; process time; quality degradation; sterilization temperature; thermal diffusivity; thermal food processing; Artificial neural networks; Food preservation; Intelligent networks; Mathematical model; Q factor; Resistance heating; Safety; Temperature sensors; Testing; Thermal degradation;
Conference_Titel :
Intelligent Control/Intelligent Systems and Semiotics, 1999. Proceedings of the 1999 IEEE International Symposium on
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7803-5665-9
DOI :
10.1109/ISIC.1999.796687
