Emulsification of algal oil with soy lecithin improved DHA bioaccessibility but did not change overall in vitro digestibility
Bibliographic record
Abstract
Oil emulsification facilitates digestive lipolysis and subsequent lipid bioaccessibility. This study aimed to understand the effects of emulsification on DHA-rich algal oil digestion and bioaccessibility. An oil/water emulsion (50 : 44 : 6 oil-water-soy lecithin) was subjected to an in vitro digestion model with gastric pH 1.6 or 4.0 and particle size distributions, duodenal stage lipolysis and DHA bioaccessibility were determined. The emulsion was destabilized at gastric pH 1.6, with subsequent slow duodenal lipolysis. With gastric pH 4.0, the emulsion structure remained intact, initial lipolysis proceeded rapidly and DHA bioaccessibility was higher than for bulk oil, a mixture of oil, water and soy lecithin, and the gastric pH 1.6 destabilized emulsion (p < 0.05). However, the extent of lipolysis was not affected by emulsification or gastric pH. Therefore, the presence of an intact emulsion at the start of duodenal digestion, while not impacting the extent of lipolysis, did impact the initial lipolysis and DHA bioaccessibility.
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 | 0.000 |
| 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".