Vitamin D<sub>3</sub> Fortification, Quantification, and Long-Term Stability in Cheddar and Low-Fat Cheeses
Bibliographic record
Abstract
Considering the widespread insufficiency of vitamin D, the fortification of additional foods with vitamin D is warranted. The objective of this research was to assess the feasibility of vitamin D3 fortification in natural hard cheeses. We examined the recovery, distribution, long-term retention, and heat stability of the vitamin in industrially made fortified Cheddar and low-fat cheeses. The results indicated that the vitamin D3 did not degrade during processing, over 1 year of ripening (3-8 degrees C), or after thermal treatment at 232 degrees C for 5 min. Vitamin D3 recovery in the fortified Cheddar and low-fat cheeses were, respectively, 91 and 55% of the vitamin D3 added to the milk used to make each cheese. The remaining vitamin D3 was entrained in the whey. The vitamin D3 was uniformly distributed throughout the blocks of cheese. The fortification process did not alter the yield, chemical composition, or flavor of the Cheddar cheese. We conclude that industrially manufactured Cheddar and low-fat cheeses are suitable for vitamin D3 fortification.
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.
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 | 0.001 |
| 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".