Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms

Abstract

Variable retort temperature (VRT) thermal processing can be an effective technique used for improving the quality of canned foods and reduce the process time. In this study, artificial intelligent techniques were applied to develop prediction models and search for optimal variable retort temperature processing conditions for conduction heated foods. A computer simulation program was used to gather data needed for training and testing of artificial neural network (ANN) models. Two VRT functions, sine and exponential, were used for modeling and optimization. ANN models were implemented to develop prediction models for VRT output variables: average quality retention ( Q v), process time ( P t) and surface cook value ( F s). Genetic algorithms (GA) were coupled with trained neural network models to meet different optimization objectives: minimum P t and F s, under given constraints. The searching range of each independent variable was based on a sensitivity analysis of effects of function parameters on response variables. The best results for Q v, P t and F s under constant retort temperature (CRT) processing conditions were used as constraints. Test results indicated that coupled ANN–GA models could be effectively used for describing the relationships between the operating variables and VRT function parameters, and for identifying optimal processing conditions. VRT processes reduced the process time by more than 20% and surface cook value by about 7–10% as compared to the best CRT process.