Texture prediction during deep frying: A mechanistic approach

A framework for fundamental physics-based prediction of texture was developed whereby changes in Young’s modulus in potato strips during frying could be predicted by combining modulus changes with temperature and moisture with predictions of the latter from fundamental physics-based process model. Moisture and temperature dependence of Young’s modulus was obtained from experiment. Process model for frying was based on multiphase porous media based transport equations. Effective value of Young’s modulus for a potato strip was obtained from local values of modulus predicted by the model through homogenization. The predictions were validated using measured Young’s modulus during frying. Such a model-based prediction providing insight into texture development during a frying process (both as function of time as well as spatially) within the potato strip will be difficult to achieve from direct experimentation alone. Precise effects of increased sample size and reduced oil temperature in slowing down the texture development are shown. Since the model is physics-based, the prediction framework can be extended to processes other than frying, allowing fundamental-based quality prediction in general.