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

IF 15.4 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology Pub Date : 2025-09-19 DOI:10.1016/j.tifs.2025.105317

Mohammad Ekrami , Behdad Shokrollahi Yancheshmeh , Negar Roshani-Dehlaghi , Nima Mobahi , Zahra Emam-Djomeh , Mohammadamin Mohammadifar

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引用次数: 0

Abstract

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.

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下一代智能和安全食品：人工智能驱动的4D食品预印挑战策略

四维（4D）食品打印代表了增材制造的新发展，使制造动态，刺激响应的可食用结构成为可能。这些结构可以根据环境因素改变形状、质地或功能，为个性化营养和智能食品系统开辟了新的途径。然而，在3d打印食品中实现精度和稳定性仍然是一个挑战，特别是在预打印阶段。本文综述了4D食品打印中主要的印前挑战，包括食品油墨配方的复杂性、成分相容性、流变学性能和生物活性稳定性。它进一步研究了人工智能（AI），特别是基于规则的系统，机器学习（ML）和深度学习（DL）如何解决这些问题。人工智能辅助配方建模和预测流变学的最新进展作为提高工艺效率和产品性能的工具进行了讨论。关键发现和结论：ai驱动策略为克服4D食品打印中的配方、兼容性和可重复性问题提供了强大的解决方案。机器学习算法可以模拟成分之间复杂的相互作用，而深度学习可以提高质地、流动行为和刺激反应的预测精度。通过将人工智能集成到预印工作流程中，食品技术人员可以加速功能性和个性化产品的设计。人工智能引导的材料科学和实时自适应打印系统的未来发展预计将在下一代创新和安全食品中发挥关键作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Trends in Food Science & Technology 工程技术-食品科技

CiteScore

32.50

自引率

2.60%

发文量

322

审稿时长

37 days

期刊介绍： Trends in Food Science & Technology is a prestigious international journal that specializes in peer-reviewed articles covering the latest advancements in technology, food science, and human nutrition. It serves as a bridge between specialized primary journals and general trade magazines, providing readable and scientifically rigorous reviews and commentaries on current research developments and their potential applications in the food industry. Unlike traditional journals, Trends in Food Science & Technology does not publish original research papers. Instead, it focuses on critical and comprehensive reviews to offer valuable insights for professionals in the field. By bringing together cutting-edge research and industry applications, this journal plays a vital role in disseminating knowledge and facilitating advancements in the food science and technology sector.