Next-generation smart and safe foods: Artificial intelligence -driven strategies for 4D food pre-printing challenges

Background
Four-dimensional (4D) food printing represents a novel evolution in additive manufacturing, enabling the fabrication of dynamic, stimuli-responsive edible structures. These structures can change shape, texture, or functionality in response to environmental triggers, opening new avenues for personalized nutrition and smart food systems. However, achieving precision and stability in 4D-printed foods remains a challenge, particularly during the pre-printing phase.

Scope and approach
This review focuses on the key pre-printing challenges in 4D food printing, including the complexity of food ink formulation, ingredient compatibility, rheological performance, and bioactive stability. It further examines how artificial intelligence (AI), specifically rule-based systems, machine learning (ML), and deep learning (DL), can address these issues. Recent advances in AI-assisted formulation modeling and predictive rheology are discussed as tools for improving process efficiency and product performance.

Key findings and conclusions
AI-driven strategies offer powerful solutions to overcome formulation, compatibility, and reproducibility issues in 4D food printing. ML algorithms can model complex interactions among ingredients, while DL enhances prediction accuracy for texture, flow behavior, and stimuli responsiveness. By integrating AI into the pre-printing workflow, food technologists can accelerate the design of functional and personalized products. Future developments in AI-guided material science and real-time adaptive printing systems are expected to play a key role in the next generation of innovative and safe foods.