Data-based mechanistic modeling approach for predicting thermal response of conductive food during heating processes

Abstract

The application of a data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was completely filled in 307x113 steel cans as the conductive food. Step increase in heating medium was applied while the product temperature was recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. A first order transfer function model proved to be sufficiently good in describing the heat transfer from heating medium to product with a high statistical significance (R2 > 0.99). In this model, a parameter related to the heat transfer coefficient was found and could be used to predicting of product temperature during heating processes.