Heat and Mass Transfer Modeling to Predict Temperature Distribution during Potato Frying after Pre-Treatment with Pulsed Electric Field

Abstract

Based on unsteady state heat conduction, a mathematical model has been developed to describe the simultaneous heat and moisture transfer during potato frying. For the first time, the equation was solved using both enthalpy and Variable Space Network (VSN) methods, based on a moving interface defined by the boiling temperature of water in a potato disc during frying. Two separate regions of the potato disc namely fried (crust) and unfried (core), were considered as heat transfer domains. A variable boiling temperature of the water in potato discs was required as an input parameter for the model as the water is evaporated during frying, resulting in an increase in the soluble solid concentration of the potato sample. Pulsed electric field (PEF) pretreatment prior to frying had no significant effect on the measured moisture content, thermal conductivity or frying time compared to potatoes that did not receive a PEF pretreatment. However, a PEF pretreatment at 1.1 kV/cm and 56 kJ/kg reduced the temperature variation in the experimentally measured potato center by up to 30%. The proposed heat and moisture transfer model based on unsteady state heat conduction successfully predicted the experimental measurements, especially when the equation was solved using the enthalpy method.

Application of Novel Thermal Technology in Foods Processing

“…In agreement with the review article by Xu et al [ 6 ], the work of Gholamibozanjani et al [ 7 ] conducted a timely investigation on the use of suitable heat and mass transfer models to predict temperature distribution during potato frying after pre-treatment with PEF. Meanwhile, the work of Abduh et al [ 8 ] reported the kinetics of colour development during the frying of four potato cultivars pre-treated with PEF, in which the kinetic result can later aid in the optimisation of frying conditions for deep-fried potato industries where PEF technology is implemented.…”

“…Meanwhile, the work of Abduh et al [ 8 ] reported the kinetics of colour development during the frying of four potato cultivars pre-treated with PEF, in which the kinetic result can later aid in the optimisation of frying conditions for deep-fried potato industries where PEF technology is implemented. Based on an unsteady-state heat conduction, a mathematical model was developed by Gholamibozanjani et al [ 7 ] to describe the simultaneous heat and moisture transfer during potato frying. For the first time, the equation was solved using both enthalpy and Variable Space Network methods, based on a moving interface defined by the boiling temperature of water in a potato disc during frying.…”

Multiobjective Optimization of a Frying Process Balancing Acrylamide Formation and Quality: Solution Analysis and Uncertainty Propagation

“…For a proper analysis of the optimal solutions, it must be taken into account that this modelling exercise considered that the cooking temperature is uniform throughout the potato and that the temperature of the oil is equal to the temperature of the potato. Obviously, this does not actually happen [ 58 , 59 ], so these theoretical times could be increased without affecting the limit of acrylamide produced. However, we would still conclude that most operating conditions within the ranges of temperatures and times usually considered in real processes exceed the recommended acrylamide amount.…”

Machine learning‐based modeling in food processing applications: State of the art

“…Although a food frying company has significant economic potential, design and optimization of large‐scale industrial frying processes are demanding, and hence a comprehensive understanding of frying processes is required. Physics‐based modeling has great potential for understanding frying kinetics, and therefore many physics‐based mathematical models of frying processes have been developed (Gholamibozanjani et al., 2021; Halder & Datta, 2007). As the development of a physics‐based model is time‐consuming and computationally intensive, ML‐based predictive modeling could be an exciting opportunity to optimize product quality and improve the energy efficiency of the fryer.…”