Improved prediction of thermal properties of refrigerated foods

Abstract

Thermal properties are essential parameters for performing heat transfer calculations. The thermal properties of foods can have a strong dependence on temperature during thermal processing, particularly during freezing, which has posed a challenge to those attempting to develop generic models that can be applied to a wide range of foods. Ideally, thermal property models should not incorporate any parameters whose value would need to be determined by a physical measurement (since this may defeat the purpose of the model). Instead it is preferable to perform a prediction based only on composition data, in terms of the major food components. This study presents an evaluation of thermal property models requiring only composition data as inputs. It is recommended that the Additive model incorporating a new correlation equation of the specific heat capacity of water should be used for effective specific heat capacity predictions. The thermal conductivity predictions from a little-known model developed by Dul'nev and Novikov provided more accurate predictions on average than more widely known models that have been recommended in previous studies. • A comparison of different composition-based thermal property models is presented. • The Additive model is recommended for effective specific heat capacity predictions. • The Dul'nev and Novikov model shows the best thermal conductivity predictions.