Computer-aided food engineering.

Area of Science:

• Food Science and Technology
• Computational Engineering
• Digital Agriculture

Background:

• Computer-aided food engineering (CAFE) offers benefits like reduced resource use and improved innovation in food product, process, and equipment development.
• Significant challenges hinder CAFE adoption, including the inherent complexity and variability of food composition and structure, and the dynamic transformations during processing.
• Limited availability of comprehensive mechanistic frameworks for describing food transformations restricts the full potential of CAFE.

Purpose of the Study:

• To introduce novel frameworks for modeling food processes and predicting physicochemical properties to accelerate CAFE.
• To address the limitations in current mechanistic frameworks for food processing.
• To facilitate the integration of CAFE into the evolving digital food systems landscape.

Main Methods:

• Development of new theoretical frameworks for simulating food process dynamics.
• Implementation of predictive models for key food physicochemical properties.
• Review of current practices and future needs for CAFE implementation.

Main Results:

• The proposed frameworks enable more accurate modeling of food processes.
• Physicochemical property prediction is enhanced, leading to better product and process design.
• The study highlights pathways to overcome existing barriers in CAFE.

Conclusions:

• The developed frameworks are poised to significantly accelerate the application of CAFE.
• Open access initiatives, including code sharing, and specialized capacity-building courses are vital for widespread CAFE adoption.
• These advancements will support the ongoing transformation towards digital food systems, enhancing efficiency, safety, and quality.