ReviewFoods (Basel, Switzerland)2025
Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Starch-Coated Superparamagnetic FeNanomaterials (Basel, Switzerland) · 2026Article
- Biocomposite Films Reinforced With Nano Navy Bean Starch: Application and Characterization.Food science & nutrition · 2026Article
- Molecular Interactions of Resistant Dextrin with Wheat Starch and Gluten: Structural Dynamics and Macromolecular Network Formation.Foods (Basel, Switzerland) · 2026Article
- Insights into factors governing encapsulation and release performance of starch-based nanocarriers for nutraceuticals delivery.Frontiers in nutrition · 2026Review
- Review
- Exploring the Frontiers of Food Science: A Comprehensive Review of Advanced Magnetic Resonance Applications in Food Analysis, Quality Analysis, and Safety Assessment.Food science & nutrition · 2025Review
- Chitosan-Based Nanoparticles Targeted Delivery System: In Treatment Approach for Dyslipidemia.International journal of nanomedicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.