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

fYear

1999

fDate

1999

Firstpage

393

Lastpage

398

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control/Intelligent Systems and Semiotics, 1999. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Cambridge, MA

ISSN

2158-9860

Print_ISBN

0-7803-5665-9

Type

conf

DOI

10.1109/ISIC.1999.796687

Filename

796687