Characterization of granular micronutrient premixes for food fortification
Bibliographic record
Abstract
Abstract Granular micronutrient premixes have been used to fortify salt and proven to alleviate micronutrient deficiencies. Expansion of the micronutrient premix technology to other food vehicles requires characterization of the granules to establish a basic understanding of the material. The objective of this study is to determine the physical and chemical properties of micronutrient premixes, and examine these parameters in relation to the attrition of the granules. The physical properties examined were particle shape, density, and compressive strength; and chemical compositions of the granule surfaces and cross sections were mapped using x‐ray photoelectron spectroscopy, scanning electron microscopy, and time‐of‐flight secondary ion mass spectroscopy. Currently, premix granules coated with hydroxypropyl methylcellulose and fully hydrogenated soybean oil to a minimum coating thickness of 25 μm is sufficient for resisting attrition under the processing conditions of double‐fortified salt. Micronutrient premix granules can be made more resistant to attrition under harsher processing conditions through reduction in particle size, increase in sphericity, and increase in the coating thickness. Practical Applications Two billion people worldwide suffer from the deficiency at least one micronutrient, which burdens an individual's health and quality of life. Fortification of staple foods is a proven cost‐effective approach to mitigate micronutrient deficiencies. Micronutrient premix granules used to fortify salt have had great success in reducing iron‐deficient anemia. Premix granules must remain intact until they reach the consumer for the micronutrients to be absorbed by the body. Currently, existing formulations of premix can resist breakage under the processing and handling conditions of salt production. However, premix granules have not been tested under elevated temperatures and compression, so their integrity under such conditions remains to be shown. This study reports on the properties of micronutrient premix granules to establish a baseline understanding for the expansion of their use. Our results show that existing premix granules can be made smaller, rounder, and with a thicker coating to withstand harsher processing conditions.
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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) | 0.001 | 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".