Iron-Polyphenol Interaction Reduces Iron Bioavailability in Fortified Tea: Competing Complexation to Ensure Iron Bioavailability
Bibliographic record
Abstract
Tea seems to be like a logical substrate for iron fortification; however, its fortification with iron presents technical challenges as tea polyphenols form a blue complex with iron that makes both of them unavailable for absorption. The objective of this work was to develop an effective technology, to prevent the interaction of iron and polyphenols by using EDTA as a competing complexing agent. Fortified tea was prepared from premix, prepared by spraying iron and sodium EDTA into tea leaves. Iron concentration in tea was adjusted to 5 mg/cup. Iron content was measured by AAS and the iron-polyphenol complex by spectrophotometry at 560 nm. Sensory evaluation was carried out in order to determine if fortification affects the properties of tea. A molar ratio of 1 : 2 Fe : EDTA was able to avoid complex formation and provide 4 mg of iron per cup of brewed tea. The fortified tea had a similar colour and flavour as ordinary tea, without the development of off-flavours. However, fortified tea with a ratio lower than 1 : 2 had a darker colour and off-flavours. By the addition of EDTA in a molar ratio ≥1 : 2, it is possible to produce an iron fortified tea without the formation of off-flavours.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".