The encapsulation process has been utilized in the field of food technology to enhance the technofunctional properties of food products and the delivery of nutraceutical ingredients via food into the human body. The latter application is very similar to drug delivery systems. The inherent sophisticated nature of release mechanisms requires the utilization of mathematical equations and statistics to predict the release behavior during the time. The science of mathematical modeling of controlled release has gained a tremendous advancement in drug delivery in recent years. Many of these modeling methods could be transferred to food. In order to develop and design enhanced food controlled/targeted bioactive release systems, understanding of the underlying physiological and chemical processes, mechanisms, and principles of release and applying the knowledge gained in the pharmaceutical field to food products is a big challenge. Ideally, by using an appropriate mathematical model, the formulation parameters could be predicted to achieve a specific release behavior. So, designing new products could be optimized. Many papers are dealing with encapsulation approaches and evaluation of the impact of process and the utilized system on release characteristics of encapsulated food bioactives, but still, there is no deep insight into the mathematical release modeling of encapsulated food materials. In this study, information gained from the pharmaceutical field is collected and discussed to investigate the probable application in the food industry.

Spray drying (SD) is one of the most important thermal processes used to produce different powders and encapsulated materials. During this process, quality degradation might happen. The main objective of applying optimization methods in SD processes is maximizing the final nutritional quality of the product besides sensory attributes. Optimization regarding economic issues might be also performed. Applying optimization approaches in line with mathematical models to predict product changes during thermal processes such as SD can be a promising method to enhance the quality of final products. In this review, the application of the response surface methodology (RSM), as the most widely used approach, is introduced along with other optimization techniques such as factorial, Taguchi, and some artificial intelligence-based methods like artificial neural networks (ANN), genetic algorithms (GA), Fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS). Also, probabilistic methods such as Monte Carlo are briefly introduced. Some recent case studies regarding the implementation of these methods in SD processes are also exemplified and discussed.