数字农科院2.0

Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity

文献类型： 外文期刊

作者： Yue Sun;Ziyu Wang;Jian Ye;Yinta Li;Lili Wang;Ruge Cao

作者机构：

关键词： artificial intelligence (AI);food matrix;nuclear magnetic resonance (NMR);predictive modeling;starch-based nanocomposites (SNCs)

期刊名称： Foods

ISSN： 2304-8158

年卷期： 2025 年 14 卷 5 期

页码：

收录情况： SCIE（2025版）

摘要： Starch-based nanocomposites (SNCs) are at the forefront of innovations in food science, offering unparalleled opportunities for enhancing the stability, bioactivity, and overall functionality of food systems. This review delves into the potential of SNCs to address contemporary challenges in food formulation, focusing on the synergistic effects of their components. By integrating cutting-edge technologies, such as artificial intelligence (AI) and nuclear magnetic resonance (NMR), we explore new avenues for enhancing the precision, predictability, and functionality of SNCs. AI is applied to optimize the design of SNCs, leveraging predictive modeling to fine-tune material properties and streamline production processes. The role of NMR is also critically examined, with particular emphasis on its capacity to provide high-resolution structural insights, monitor stability over time, and elucidate molecular interactions within food matrices. Through detailed examples, the review highlights the impact of NMR in unraveling the complex behaviors of bioactive compounds encapsulated in SNCs. Additionally, we discuss the integration of functional assays and AI-driven analytics in assessing the bioactivity and sensory properties of these systems, providing a robust framework for the rational design of advanced food products. The synergy between AI, NMR, and SNCs opens new pathways for developing tailored, high-performance food formulations that address both health and consumer preferences.

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