Effect of emulsifiers on linseed oil emulsion structure, lipolysis and oxidation during <i>in vitro</i> digestion
Bibliographic record
Abstract
Health benefits have been associated with the consumption of omega-3 polyunsaturated fatty acids (PUFA). Linseed oil is rich in long chain omega-3 PUFA, but can generate toxic compounds due to its high susceptibility to oxidation. The nature of the emulsifier can affect both lipolysis and oxidation during digestion since these phenomena occur at the oil-water interface. The objective of this study was to compare the effect of low-molecular weight surfactants (cetyltrimethylammonium bromide (CTAB), Citrem), protein (sodium caseinate, fish gelatin) and polysaccharides (gum arabic, modified starch) on the structure of linseed oil emulsions, lipolysis and formation of reactive oxidation species during in vitro digestion. The emulsion stabilized with Citrem underwent extensive coalescence in the gastric phase, which strongly decreased the extent of lipid digestion and reduced the formation of oxidation markers relative to other emulsions. Emulsions stabilized by proteins and modified starch showed aggregation with partial coalescence in the gastric phase, but protein-stabilized emulsions showed better resistance to oxidation. This study shows that emulsifier properties affect the susceptibility of the emulsion to aggregation and coalescence in the gastrointestinal environment, and strongly influence the extent of lipid digestion and the formation of reactive oxidation products. These findings point out the importance of the choice of the emulsifier to control the lipid digestibility and the protection of sensible lipids thus promoting optimal nutritional properties in omega-3-enriched foods.
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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".