In the last decades, different non-thermal and thermal technologies have been developed for food processing. However, in many cases, it is not clear which experimental parameters must be reported to guarantee the experiments’ reproducibility and provide the food industry a straightforward way to scale-up these technologies. Since reproducibility is one of the most important science features, the current work aims to improve the reproducibility of studies on emerging technologies for food processing by providing guidelines on reporting treatment conditions of thermal and non-thermal technologies. Infrared heating, microwave heating, ohmic heating and radiofrequency heating are addressed as advanced thermal technologies and isostatic high pressure, ultra-high-pressure homogenization sterilization, high-pressure homogenization, microfluidization, irradiation, plasma technologies, power ultrasound, pressure change technology, pulsed electric fields, pulsed light and supercritical CO₂ are approached as non-thermal technologies. Finally, growing points and perspectives are highlighted.

Convective hot air drying technology is widely used for fruits and vegetables drying process due to its simplicity in usage, small investment, and convenience in operation. However, it is generally considered energy-intensive process with lower drying efficiency and quality because of unprecise control of drying process. In order to solve these problems, step-down relative humidity (RH) hot air drying technology was applied for papaya drying to improve drying efficiency and quality and reduce energy consumption. The effect of constant RH (20%, 30%, 40%, and 50%) and step-down RH of 50% with different holding times (10, 30, 60, and 90 min) and then decrease to 20% on heat and mass transfer process, drying quality, and energy consumption was examined. Results showed that decreasing RH contributed to improvement in the drying rate (DR). Compared to constant RH of 20%, the drying time under initial RH of 50% for 30 min followed by 20% RH shortened by 21.4%. Additionally, under this RH control strategy, the rehydration ratio (RR) and energy consumption achieved their maximum (7.71 ± 0.06) and minimum value (0.015 ± 0.001 kJ/g), respectively. During initial drying stage, high RH improved convective heat transfer coefficient (h_t) values and material temperature increased quickly. Furthermore, high RH contributed to maintaining pore structure formation, which benefited moisture diffusion. When RH was reduced, low RH enhanced convective mass transfer coefficient (h_m) significantly, resulting in rapid diffusion of internal moisture at high temperature, as well as rapid moisture evaporation from the surface. Therefore, step-down RH hot air drying was verified to improve drying of papaya slices, making it energy efficient process. The findings in current work indicate step-down relative humidity strategy is a promising technique to enhance hot air drying process, quality, and energy efficiency of papaya slices.

This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51–3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.