Effect of cooking duration on carotenoid content, digestion and potential absorption efficiencies among refined semolina and whole wheat pasta products
Bibliographic record
Abstract
The bioaccessibility of carotenoids varies among different foods due to factors such as food matrix composition and type or extent of processing. Hence it is important to understand the extent to which these factors influence carotenoid bioaccessibility after the consumption and digestion of food. This study evaluated the carotenoid content, micellization efficiency, digestive stability, antioxidant activity and bioaccessibility of carotenoids as impacted by wheat cultivar and cooking duration among whole wheat flour (WWF) and refined semolina (RS) pasta. WWF and RS pasta were processed from three durum wheat cultivars (AAC Spitfire, CDC Precision, and Transcend) and cooked to al dente (Al), fully cooked (FCT) or overcooked (OC). The study showed that the wheat cultivar and cooking duration were significant functions of bioaccessible lutein in RS samples while only the cultivar influenced the bioaccessibility of zeaxanthin and lutein in WWF samples. In both WWF and RS, the effect of the cultivar on the bioaccessibility of lutein and zeaxanthin was similar and was as follows: Transcend > CDC Precision > AAC Spitfire. Cooking to Al significantly caused an increment in bioaccessible lutein in RS samples regardless of the wheat cultivar. This influence of cooking duration (Al > FCT > OC) was inversely related to the lutein concentrations in undigested pasta (OC = FCT > Al). DPPH scavenging activity among WWF samples was about 2-fold greater or more than that of RS samples regardless of the cultivar or cooking duration before and after digestion. Our data suggest that the effect of wheat cultivar and cooking duration modulates the bioaccessibility and antioxidant activity of RS and WWF pasta products.
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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) | 0.001 | 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".