ARTICLE INFO

ABSTRACT

Received date: 29/07/2015

Accepted date: 19/02/2016

The application of data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was 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 instrumental variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. As a result, the first order transfer function model is proved to be sufficiently enough for 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 predict the product temperature during heating processes.

KEYWORDS

Thermal processing, heat transfer coefficient, modeling