A Comprehensive Review of Synthesis and Surface Modifications of Starch Nanomaterials for Fortification of Vitamin D<sub>3</sub>
Bibliographic record
Abstract
Starch-based nanomaterials (SNPs) have garnered significant attention for their potential applications in food fortification and supplementation owing to their unique surface properties. Numerous studies have reported diverse preparation methods to produce SNPs with varying morphologies, particle sizes, stabilities, chemical reactivities, flow properties, opacities, and mechanical strengths. However, these properties, particularly in relation to micronutrient loading and release performance, remain underexplored in the current literature. Notably, the incorporation of certain micronutrients, such as vitamin D 3, into SNPs often compromises particle stability and impairs release kinetics in aqueous conditions. These limitations highlight the need for optimized surface modification strategies and scalable and cost-effective encapsulation protocols. This review critically illustrates the factors influencing starch nanoparticle fabrication, characterization, surface modification, micronutrient loading, and release kinetics. We provide a comprehensive overview of the key formulation considerations, from starch selection and physicochemical properties to emerging strategies for targeted delivery applications. Additionally, the review underscores the importance of precise surface functionalization and processing techniques, including ultrasonication, lyophilization, and the degree of substitution, in enhancing micronutrient solubility and bioavailability. To our knowledge, this work offers one of the most complete syntheses of VD 3 nanoencapsulation using SNPs, serving as a valuable resource for scholars in food science and nanotechnology. Furthermore, we present a detailed bibliometric analysis of the past decade, mapping research trends, identifying influential studies, and highlighting future research directions. This analysis offers insights into the impact, quality, and practical relevance of starch nanoparticle research, promoting innovation and advancement in food fortification technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".