“…The tetrahedral elements were automatically produced by COMSOL, as presented in Figure 2 , and were manually adjusted until the change in the temperature at the selected points was not significant ( p < 0.05; data not shown). The maximum mesh size element ( ) of all domains was 0.017 m, while the sausage domain was 0.008 m. The mesh size elements were less than the maximum mesh size calculated using Equation (2) [ 24 ], which had a 0.01–0.04 m mesh size. Figure 1 The ( a ) experimental setup of temperature measurement in household microwave and ( b ) geometry of sausages.…”
The microwave oven has become a standard appliance to reheat or cook meals in households and convenience stores. However, the main problem of microwave heating is the non-uniform temperature distribution, which may affect food quality and health safety. A three-dimensional mathematical model was developed to simulate the temperature distribution of four ready-to-eat sausages in a plastic package in a stationary versus a rotating microwave oven, and the model was validated experimentally. COMSOL software was applied to predict sausage temperatures at different orientations for the stationary microwave model, whereas COMSOL and COMSOL in combination with MATLAB software were used for a rotating microwave model. A sausage orientation at 135° with the waveguide was similar to that using the rotating microwave model regarding uniform thermal and electric field distributions. Both rotating models provided good agreement between the predicted and actual values and had greater precision than the stationary model. In addition, the computational time using COMSOL in combination with MATLAB was reduced by 60% compared to COMSOL alone. Consequently, the models could assist food producers and associations in designing packaging materials to prevent leakage of the packaging compound, developing new products and applications to improve product heating uniformity, and reducing the cost and time of the research and development stage.
“…The tetrahedral elements were automatically produced by COMSOL, as presented in Figure 2 , and were manually adjusted until the change in the temperature at the selected points was not significant ( p < 0.05; data not shown). The maximum mesh size element ( ) of all domains was 0.017 m, while the sausage domain was 0.008 m. The mesh size elements were less than the maximum mesh size calculated using Equation (2) [ 24 ], which had a 0.01–0.04 m mesh size. Figure 1 The ( a ) experimental setup of temperature measurement in household microwave and ( b ) geometry of sausages.…”
The microwave oven has become a standard appliance to reheat or cook meals in households and convenience stores. However, the main problem of microwave heating is the non-uniform temperature distribution, which may affect food quality and health safety. A three-dimensional mathematical model was developed to simulate the temperature distribution of four ready-to-eat sausages in a plastic package in a stationary versus a rotating microwave oven, and the model was validated experimentally. COMSOL software was applied to predict sausage temperatures at different orientations for the stationary microwave model, whereas COMSOL and COMSOL in combination with MATLAB software were used for a rotating microwave model. A sausage orientation at 135° with the waveguide was similar to that using the rotating microwave model regarding uniform thermal and electric field distributions. Both rotating models provided good agreement between the predicted and actual values and had greater precision than the stationary model. In addition, the computational time using COMSOL in combination with MATLAB was reduced by 60% compared to COMSOL alone. Consequently, the models could assist food producers and associations in designing packaging materials to prevent leakage of the packaging compound, developing new products and applications to improve product heating uniformity, and reducing the cost and time of the research and development stage.
“…Among these methods, microwave thawing presents advantages such as a short thawing time, easy control, fast heat efficiency, and energy saving. These advantages have resulted in the extensive use of microwave thawing, both in industry and at home [ 112 , 119 , 120 , 121 ]. Nonetheless, its major problem is non-uniform heating via the uneven distribution of the microwave field inside the food product [ 114 , 122 ].…”
Section: Effect Of Thawing On Food Qualitymentioning
confidence: 99%
“…Although the freeze time and temperature distribution could be experimentally measured during the freezing process, this is costly, time-consuming, and lacks a complete theoretical description of the process [ 88 ], as measuring temperature spot by spot thermocouple is not feasible in a biological structure [ 47 , 155 ]. Furthermore, process design data and new designs in thawing systems are often based on trial and error [ 102 , 120 ]. Due to changes in the properties and structure of components in food products during the thawing process, trial and error methods are costly and time-consuming [ 115 ].…”
Section: Current Limitations and Future Trendsmentioning
Freezing is a well-established preservation method used to maintain the freshness of perishable food products during storage, transportation and retail distribution; however, food freezing is a complex process involving simultaneous heat and mass transfer and a progression of physical and chemical changes. This could affect the quality of the frozen product and increase the percentage of drip loss (loss in flavor and sensory properties) during thawing. Numerical modeling can be used to monitor and control quality changes during the freezing and thawing processes. This technique provides accurate predictions and visual information that could greatly improve quality control and be used to develop advanced cold storage and transport technologies. Finite element modeling (FEM) has become a widely applied numerical tool in industrial food applications, particularly in freezing and thawing processes. We review the recent studies on applying FEM in the food industry, emphasizing the freezing and thawing processes. Challenges and problems in these two main parts of the food industry are also discussed. To control ice crystallization and avoid cellular structure damage during freezing, including physicochemical and microbiological changes occurring during thawing, both traditional and novel technologies applied to freezing and thawing need to be optimized. Mere experimental designs cannot elucidate the optimum freezing, frozen storage, and thawing conditions. Moreover, these experimental procedures can be expensive and time-consuming. This review demonstrates that the FEM technique helps solve mass and heat transfer equations for any geometry and boundary conditions. This study offers promising insight into the use of FEM for the accurate prediction of key information pertaining to food processes.
“…The distribution of electromagnetic waves in food products changes according to dielectric properties (vary with food composition), thermal properties (conductivity, specific heat), physical properties (shape, size), microwave standing waves, the location inside the resonant cavity and oven design [6,[18][19][20]. Regarding the design, turntables [7,[21][22][23], modified sample compartment [18,24], stirrers [17,25], and complementary heating [26,27] are recurrent alternatives to improve microwave distribution. Also, product temperature can be further moderated by controlling magnetron duty cycle or using solid-state technology [1] and power density [28], including online feedback control [29].…”
Microwave technology has many current applications. It is very useful for food processing, including domestic cooking and warming-up and industrial heating and drying. It heats faster than conventional applications; however, in most cases result in non-uniform temperature distribution. Adequate cavity and equipment designs can reduce the impact of these heterogeneities and using multiples magnetrons is a possibility to mitigate hot and cold spots. Yet, the literature lacks methods to evaluate and compare multiple magnetrons designs. This study aimed to develop a procedure to evaluate the number and position of magnetrons connected to an application cavity using multiphysics modelling and simulation of the microwave distribution and heating of a food model. It was based on evaluating the electric field distribution into a selected working volume filled with air or a mixture of air-potato and the consequent effective power absorbed and temperature distribution into the air-potato medium. The assisted methodology with process simulation offers an insight into food temperature distribution, which would be very difficult to obtain experimentally or in any equipment design methodology. In this case study, it was found that up to 6 magnetrons are good enough when active in approximately 0.16 m3 cavity with a load that fills 10% of its volume.
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