Key points are not available for this paper at this time.
In food drying processes, inadequate control of drying conditions can lead to under- or over-dried foods, often due to uncontrolled parameters including dryer type, airflow distribution, and optimal drying conditions. This review examines common food drying technologies, methods for optimizing the drying parameters, and methods for model selection for accurate data fitting. The drying methods covered include open sun drying, solar drying, cabinet drying, drum drying, spray drying, freeze drying, fluidized-bed drying, potted-bed drying, superheated steam drying, and microwave drying. Furthermore, tools with better optimization and prediction capabilities for various food types such as Computational Fluid Dynamics (CFD), ANSYS Multi-Objective Genetic Algorithm (MOGA), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Genetic Algorithm (GA)-tuned Artificial Neural Networks (ANN), Analysis of Variance (ANOVA), and Response Surface Methodology (RSM) are reviewed in detail. These tools offer valuable methods for determining optimal drying parameters across different drying technologies and food products. Additionally, theoretical, semi-empirical, and empirical thin-layer models are discussed as effective methods for accurately fitting drying process data. Achieving high-quality dried products requires maintaining uniform airflow distribution within the dryer and optimizing key parameters such as drying temperature, moisture content, drying rate, drying time, and airflow speed. One of the primary challenges in existing drying technologies is the non-uniform airflow distribution throughout the drying chamber, which directly affects both quality and efficiency of the drying process. The future of food drying technology has focused on advancing the processing methods, and integrating and employing hybrid drying methods to enhance drying efficiency.
Asrate et al. (Sun,) studied this question.