The drying kinetics of peppermint leaves was studied to determine the best drying method for them. Two drying methods include hot-air and infrared techniques, were employed. Three different temperatures (30, 40, 50 °C) and air velocities (0.5, 1, 1.5 m/s) were selected for the hot-air drying process. Three levels of infrared intensity (1500, 3000, 4500 W/m2), emitter-sample distance (10, 15, 20 cm) and air speed (0.5, 1, 1.5 m/s) were used for the infrared drying technique. According to the results, drying had a falling rate over time. Drying kinetics of peppermint leaves was explained and compared using three mathematical models. To determine coefficients of these models, non-linear regression analysis was used. The models were evaluated in terms of reduced chi-square (χ2), root mean square error (RMSE) and coefficient of determination (R2) values of experimental and predicted moisture ratios. Statistical analyses indicated that the model with the best fitness in explaining the drying behavior of peppermint samples was the Logarithmic model for hot-air drying and Midilli model for infrared drying. Moisture transfer in peppermint leaves was also described using Fick’s diffusion model. The lowest effective moisture diffusivity (1.096 × 10−11 m2/s) occurred during hot-air drying at 30 °C using 0.5 m/s, whereas its highest value (5.928 × 10−11 m2/s) belonged to infrared drying using 4500 W/m2 infrared intensity, 0.5 m/s airflow velocity and 10 cm emitter-sample distance. The activation energy for infrared and hot-air drying were ranged from 0.206 to 0.439 W/g, and from 21.476 to 27.784 kJ/mol, respectively

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.