Investigations of in vitro bioaccessibility from interesterified stearic and oleic acid-rich blends
Bibliographic record
Abstract
Interesterification was previously found to impact stearic acid absorption in a randomized cross-over study, when human volunteers consumed a 70 : 30 wt% high-oleic sunflower and canola stearin blend (NIE) compared to the same blend which had undergone either chemical (CIE) or enzymatic (EIE) interesterification. In this research, in vitro lipid digestion, bioaccessibility, and changes in undigested lipid composition and melting behavior of these same test fats were investigated using the dynamic, multi-compartmental TIM-1 digestion model and compared with the previous human study. Overall, TIM-1 bioaccessibility was higher with interesterification (p < 0.05). Oleic acid bioaccessibility was higher than stearic acid bioaccessibility for NIE, and vice versa for the interesterified blends (p < 0.05). Stearic acid was more concentrated in the undigested triacylglycerols (TAG) from NIE, corresponding to a relatively higher melting temperature of the undigested lipids. The results confirm the impact of TAG composition, fatty acid position and/or physical properties on lipid digestion. TIM-1 bioaccessibility was linearly correlated (R(2) = 0.8640) with postprandial serum TAG concentration in the human study. Therefore, the in vitro digestion model offered predictive insights related to the impacts of lipid interesterificaton on absorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".