Designing iron‐ethyl cellulose microparticles to prevent unwanted color changes during iron fortification of milk tea
Bibliographic record
Abstract
Abstract Iron deficiency affects an estimated 1.62 billion individuals worldwide, while Asia and Africa bearing the highest burden. The widespread consumption, unique sensory properties and cultural significance of tea make it an appealing avenue for iron fortification. Obtaining microparticles for food fortification with acceptable organoleptic properties is key for consumer acceptability. Microcapsules were prepared with Aquacoat and various iron salts. The experiments were designed to understand the effect of formulation variables, i.e. type of iron salt, ratio of iron‐to‐coating, temperature and flow rate of the process. The iron compound significantly impacted yield, particle formation, and size distribution (5‐15 μm). Post treatment by curing at specified relative humidity and temperature improved the colour in milk tea with ΔE reduced from 7 to 2 in NBS units. Microparticles from FeCl 3 exhibited superior morphology and colour‐masking efficacy, inhibiting iron‐polyphenol interaction in tea and show promise as iron fortificants for milky black tea. Practical applications Microencapsulation is a highly effective technique for encapsulating active iron cores within inert coating materials, ensuring the desired chemical and physical properties. Using spray drying, we can produce small and uniformly sized particles ranging from 1 to 20 μm. This can easily be utilized for fortification of beverage like hot tea/coffee or similar products. The success of the current process is evaluated based on several key factors, including process yield, encapsulation efficiency, and sensory properties of fortified tea.
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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".