Bioavailability of Palm Oil Carotenoids Consumed from Different Foods
Bibliographic record
Abstract
Crude palm oil (CPO), an ingredient with a high content in provitamins A, is usually consumed associated with foods that could affect carotenoids metabolism. This work aimed to evaluate the influence of foodstuffs prepared with the incorporation of CPO on carotenoids absorption. Thus, we studied carotenoids bioavailability in Cameroonian local meals based on CPO and three different foodstuffs: maize (Zea mais), cassava (Manihot esculente) or black eyed peas (Vigna unguiculata). Modified ‘yellow sauce’ prepared mainly with CPO was used as control meal. Eleven healthy volunteers with age range between 20-30 year and similar body mass index were submitted to the study. A total of four interventions (i.e., meal ingestion) were performed every six days. After meal ingestion blood samples were collected at 0, 2, 4 and 6 h, and carotenoids content were analyzed by HPLC. Results showed that control meal had the highest carotenoids absorption (0.479±0.063 µg.h/ml.), followed by maize (0.329±0.115 µg.h/ml) and cassava (0.141±0.075 µg.h/ml) cakes. Food based on black eyed peas incorporated with CPO showed the smallest increase in blood carotenoids (0.053±0.062 µg.h/ml). Notably, CPO consumption leads to a short term significant increase of blood carotenoids (p<0.05) that declines over time. Our results suggest that the association of CPO with different foodstuffs significantly affects carotenoids bioavailability. This effect seems to be more important for leguminous, followed by tubers and cereals.
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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".