Vegetable protein isolate-stabilized emulsions for enhanced delivery of conjugated linoleic acid in Caco-2 cells
Bibliographic record
Abstract
Developing edible delivery systems which offer higher protection and release of bioactive constituents is a current challenge in the food industry. The ability of oil-in-water emulsions (20% oil) stabilized by soy or pea protein isolates (4%) to deliver conjugated linoleic acid (CLA, 6%), was studied. The emulsions were prepared by conventional homogenization (550 bar) using one or five homogenization passes. The physicochemical properties of the emulsions were determined, as well as loading capacity and oxidative stability. The emulsions were subjected to in vitro digestion, and tested on absorptive Caco-2 cells. The presence of CLA isomers was followed throughout the process. When comparing similar treatments, soy protein isolate emulsions showed smaller particle size distributions than emulsions prepared with pea protein isolates. Emulsions containing soy proteins showed preferential adsorption of the α′ subunit of conglycinin and the A 1, A 2, A 4 subunits of glycinin on the oil droplets. All emulsions protected the encapsulated CLA better than the non-emulsified control in which CLA was oxidized during storage, as well as after in vitro digestion and delivery in Caco-2 cells. Similar percentages of bioaccessibility and bioavailability of CLA were found for all the emulsion treatments. The results obtained here open new prospects for using oil-in-water emulsions as structured emulsion-based delivery systems to be used in functional foods containing CLA with health enhancing properties.
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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) | 0.000 | 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".