Vitamin D‐Casein fortification of cheese and its bioavailability
Bibliographic record
Abstract
Widespread vitamin D insufficiency worldwide and increased vitamin D intake recommendations from the Institute of medicine (IOM) emphasize a need to expand vitamin D fortification practices. IOM's 2011 report almost tripled the dietary guidelines for vitamin D: from 200 IU up to 600 IU daily. Vitamin D fortification practices vary and are largely voluntary in Canada and the US. Vitamin D‐fortified cheddar cheese using emulsified vitamin D 3 was found to have comparable bioavailability of vitamin D with liquid supplement. Our objectives are to establish a new protocol for vitamin D fortification via incorporation of vitamin D in casein; and to evaluate the effect of baking on bioavailability. Vitamin D 3 retention in lab‐scale mozzarella cheese was 54% and the loss of vitamin D3 into the whey was 1.6%. The remaining vitamin D 3 was lost during mozzarella cheese processing. Large‐scale mozzarella cheese fortified with vitamin D 3 will be used to cook pizza that will be consumed by study participants. Approximately 120 subjects will be randomized to weekly servings of pizza baked with fortified mozzarella cheese either at 200 IU/serving or 28,000 IU/serving. Vitamin D levels will be compared between groups. Vitamin D fortification of cheese is a good strategy to increase intakes and may benefit public health as well as dairy industry practices. Grant Funding Source : Dairy Farmers of Canada
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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 | 0.001 | 0.001 |
| 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".