Alginate-based Microencapsulation of Iron through 2 3-fluid nozzle Spray Drying Techniques for Tea Fortification
Bibliographic record
Abstract
Iron-deficiency anaemia is a global health issue, affecting over 1.2 billion people and causing significant health and economic impacts. Traditional solutions like supplementation and dietary changes are often unfeasible for low- and middle-income groups. This study explores a cost-effective solution: fortifying milk tea with iron through alginate-based microencapsulation using two- and three-fluid nozzle spray drying techniques. As the second most popular beverage globally, tea is an ideal vehicle for fortification. Various strategies were explored, including optimising spray drying parameters (inlet temperatures of 120°C, 140°C, and 160°C; core and shell feed flow rates of 1.47 mL/min and 1.4 mL/min, or 2.4 mL/min and 2.8 mL/min, respectively), varying sodium alginate concentrations (1%, 2%, 3%), and incorporating additional wall materials like fungal chitosan and gum arabic. A double-coating method with fungal chitosan was the most effective approach. The optimised formulation used 3% (w/v) sodium alginate and 2.39% (w/v) ferrous sulphate heptahydrate in the core feed, along with 0.69% (w/v) calcium carbonate in the shell feed during the first spray drying with a three fluid nozzle with an inlet temperature of 120℃, flow rate of 1.47 mL/min for core feed and 1.4 mL/min for shell feed. The premix was further coated with 0.5% (w/v) fungal chitosan in a second spray-drying step with a two-fluid nozzle with the same parameters. This method achieved an encapsulation efficiency of 70% ± 21.6 while preserving the tea’s taste and appearance (ΔE = 2.1 ± 1.1). Our approach demonstrates significant potential for combating iron-deficiency anaemia effectively.
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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".