The development and application of novel food processing technologies have gained significant attention in recent years due to their potential to support sustainability and net-zero development. Technologies such as Ohmic heating, high pressure, and ultrasound have been shown to improve the nutritional quality and acceptability of food products while reducing energy and water consumption. However, the proper design, implementation, and selection of process parameters remain an area requiring further development and knowledge. In addition, the transportation of granular materials, commonly encountered during food processing in equipment such as milling or husking machines, remains largely understudied. A deeper understanding of the behavior of these materials is crucial for optimal design and operation in food processing, making it a promising area for future research.

Modeling is a powerful tool in the design and optimization of real-world systems, representing the system through mathematical representation. The models generated from this exercise provide valuable information and physical insight into the process. These models can take the form of mathematical models, physical models, or hybrid models. Simulations utilizing these models allow for the computational evaluation and optimization of process conditions, making it possible to predict the effects of changes to key parameters such as temperature, electric field, pressure, rheology, environment, and boundary conditions. This capability reduces the cost of laboratory or pilot tests and provides realistic outcomes that would otherwise be unattainable. In essence, modeling and simulation offer a versatile and cost-effective way to understand and optimize complex real-world systems.

In an effort to address limitations posed by conventional technologies, new and innovative approaches are being developed to fill existing technological gaps. These novel technologies aim to achieve improved results in areas such as higher retention of bioactive compounds, better product quality, increased consumer acceptability, enhanced process control, increased energy efficiency, and reduced processing time. However, the variability of starting materials and process conditions can make it challenging to consistently produce high-quality products, presenting a significant obstacle in the implementation of these new technologies.

This research topic aims to encompass the advancement of mathematical and physical models and simulation techniques for emerging technologies, with the goal of creating a comprehensive platform for sharing and exchanging knowledge related to overcoming challenges in the scientific understanding, design, implementation, and selection of process parameters for these technologies. Specific questions to be answered include: How can modeling and simulation be optimized to better predict the outcomes of novel food processing technologies? What are the most effective process parameters for different types of food products? How can the behavior of granular materials be accurately modeled to improve processing efficiency? Hypotheses to be tested include the potential for modeling and simulation to significantly reduce the cost and time associated with experimental trials and the ability of these tools to enhance the consistency and quality of food products.

To gather further insights in the modeling and simulation of novel food processing technologies, we welcome articles addressing, but not limited to, the following themes:

- Mechanical Energy: Pressure (hydrostatic), Ultrasonication, Membrane-based processing, micro and nanofluidic.

- Electrical and Electromagnetic Energy: Irradiation and Light-based processes, Microwave and Radio Frequency heating, Electric Field Processing (MEF, PEF, Ohmic), and other advanced methods.

- Chemical Agents: Advanced oxidation (including ozone), Supercritical processes.

- Advanced Drying Technologies: Refractance window, Microwave drying.

- Granular Materials Processing: DEM, CFD-DEM, SPH, Lattice, 2D and 3D continuum modeling, husking, milling, rheology, and constitutive law.

- Machine Learning and Deep Learning: Data space analytics, surrogate models, statistical characteristics, and contact models.

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Frontiers in Food Science and Technology

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