Peptide–Mineral Complexes: Understanding Their Chemical Interactions, Bioavailability, and Potential Application in Mitigating Micronutrient Deficiency
Bibliographic record
Abstract
Iron, zinc, and calcium are essential micronutrients that play vital biological roles to maintain human health. Thus, their deficiencies are a public health concern worldwide. Mitigation of these deficiencies involves micronutrient fortification of staple foods, a strategy that can alter the physical and sensory properties of foods. Peptide-mineral complexes have been identified as promising alternatives for mineral-fortified functional foods or mineral supplements. This review outlines some of the methods used in the determination of the mineral chelating activities of food protein-derived peptides and the approaches for the preparation, purification and identification of mineral-binding peptides. The structure-activity relationship of mineral-binding peptides and the potential use of peptide-mineral complexes as functional food ingredients to mitigate micronutrient deficiency are discussed in relation to their chemical interactions, solubility, gastrointestinal digestion, absorption, and bioavailability. Finally, insights on the current challenges and future research directions in this area are provided.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".