Drying is one of the most effective methods to preserve foods and guarantee their supply for more people. Mathematical modeling is an important tool that aids the design, development, optimization and control of food drying systems. Driven by the increasing needs to dry more foods with higher efficiency, sustainability, better food quality and safety, various novel drying technologies have been developed, and food drying modeling approaches have also evolved significantly. In this review, progresses and advancements in empirical, mechanistic, and machine learning (ML) modeling approaches in food drying processes toward Multiphysics, multiphase, multidimensional, and intelligent directions are overviewed. Several challenges, needs, and future trends are identified and discussed, which includes multiscale modeling, shape change and structure deformation, coupling of food reaction kinetics, intelligent control and digitalization, translation, and dissemination of modeling results. The materials presented here aim to emphasize the importance of food drying modeling for addressing the key food challenges and attract more food scientists and engineers to contribute to improve food drying models and a more sustainable food production system.

In the current work, the effects of steam and boiling water blanching on the drying characteristics, water distribution, microstructure, and contents of bioactive substances of Gastrodia elata (G. elata) were explored. Results showed that the degree of steaming and blanching was related to the core temperature of G. elata. The steaming and blanching pretreatment increased the drying time of the samples by more than 50%. The low-field nuclear magnetic resonance (LF-NMR) of treated samples showed that the relaxation time corresponded to water molecule states (bound, immobilized, and free) and G. elata became shorter, which indicated a reduction in free moisture and increased resistance of water diffusion in the solid structure during drying. Hydrolysis of polysaccharides and gelatinization of starch granules was observed in the microstructure of treated samples, which was consistent with changes in water status and drying rates. Steaming and blanching increased gastrodin and crude polysaccharide contents and decreased p-hydroxybenzyl alcohol content. These findings will contribute to a better understanding of the effect of steaming and blanching on the drying behavior and quality attributes of G. elata.

The objective of this study was to develop an efficient drying technology for poria cubes in order to improve product quality. Poria cubes were dried using different methods, including air impingement drying, infrared-assisted air impingement drying, vacuum drying, two-stage vacuum drying, and infrared-assisted air impingement drying. The results were compared with those from hot air drying. For the two-stage drying, the tested conditions were the first stage of vacuum drying with temperatures between 65–85 °C and a switching moisture ratio of 70–90%. The second stage infrared-assisted air impingement drying also had temperatures 65–85 °C. The drying kinetics (effective moisture diffusivity (D_eff), Biot number (Bi), and mass transfer coefficient (k) were studied via the product qualities (broken ratio, firmness, microstructure, and water-soluble polysaccharide content) and specific energy consumption (SEC) of the drying processes. The results showed that two-stage drying led to the lowest drying time and energy consumption, and also obtained the best qualities. Box–Behnken experimental design with response surface methodology (RSM) was used to optimize the two-stage operating conditions as 82 °C under vacuum drying until a moisture content of 81% and a temperature of 69 °C with infrared-assisted air impingement drying was achieved. These findings suggested that two-stage vacuum and infrared-assisted air impingement drying is a promising method for producing high quality and energy efficient dried poria cubes.