Bioaccessibility and Cellular Uptake of Lutein, Zeaxanthin and Ferulic Acid from Muffins and Breads Made from Hairless Canary Seed, Wheat and Corn Blends
Bibliographic record
Abstract
Using a simulated gastrointestinal digestion model combined with a Caco-2 cell model, this study aims to assess the bioaccessibility and cellular uptake of dietary lutein, zeaxanthin, and ferulic acid from muffins and bread prepared from blends of hairless canary seed (HCS), wheat, and corn. Residual digestive enzymes damaged the Caco-2 monolayer and necessitated the requirements for the additional clean-up of the digesta. Several digesta cleanup treatments were examined, and the C18 column, along with AEBSF inhibitor, was selected as the most effective treatment. However, the cleanup treatment reduced lutein, zeaxanthin, and ferulic acid concentrations. The bioaccessibility of lutein from muffins was high at 92-94% (without clean-up) and 81-86% (with cleanup); however, the cellular uptake was low (7-9%). The bioaccessibility and cellular uptake (4-11%) of zeaxanthin were lower than lutein. Ferulic acid from muffins exhibited a wide range of bioaccessibility for non-cleanup (105-229%) and clean-up (53-133%) digesta samples; however, cellular uptake was very low (0.5-1.8%). Bread made from wheat/HCS had higher lutein bioaccessibility (47-80%) than the control bread (42%), with an apical cellular uptake ranging from 4.3 to 9.2%. Similar to muffins, the bioaccessibility of zeaxanthin from bread was lower than lutein, while ferulic acid had a fairly high bioaccessibility at 98-103% (without clean-up) and 81-102% (with cleanup); however, zeaxanthin cellular uptake was low (0.2%). These results suggest that muffins and bread could boost the daily consumption of lutein, zeaxanthin, and ferulic acid, allowing for a small portion to be absorbed in the small intestine.
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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".