Characterizing the pH-Dependent Release Kinetics of Food-Grade Spray Drying Encapsulated Iron Microcapsules for Food Fortification
Bibliographic record
Abstract
Iron deficiency is the primary cause of many widespread nutritional diseases including anemia, pregnancy complications, and infant mortality. Release kinetics of iron premixes to be mixed with food items like salt, rice, and tea is a key research objective of many globally active iron fortification efforts. Iron release kinetics of microcapsules of two reverse-enteric coating materials (chitosan and Eudragit EPO) encapsulating various amounts of ferrous sulfate (10–40% of total other solids) were done at three pH values (1, 4, 7) for 2 hours. Chitosan and Eudragit microcapsules contained 2.8–5.3% ( w / w ) and 1.7–9.6% ( w / w ) iron, respectively, depicting higher iron loading capacity of Eudragit microcapsules. More than 90% iron was released from most samples within 30 min under stomach conditions (pH 1) and less than 15% iron was released in 2 h under ambient conditions (pH 7), showing suitability of both chitosan and Eudragit EPO as reverse-enteric coatings for iron encapsulation. In terms of reverse-enteric behavior (RE), Eudragit EPO (RE = 2.4) was found to be slightly better than chitosan, suggesting the use of fillers in future research. Higuchi model and Hixson-Crowell model were found to best fit the data, suggesting a transport phenomenon governed by both (a) the diffusion process through the coating material and (b) the dissolution phenomenon resulting in decrease in size of the capsules. Results from this study shall provide guidance for technology development aspects of various food fortification initiatives and an understanding of the iron release from these fortificants during the food preparation and digestion stages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".