Designing iron‐ethyl cellulose microparticles to prevent unwanted color changes during iron fortification of milk tea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it