Stability and Bioavailability of Curcumin in Mixed Sodium Caseinate and Pea Protein Isolate Nanoemulsions
Bibliographic record
Abstract
Abstract The goals of this research were to develop oil‐in‐water nanoemulsions (NE) encapsulating curcumin by partially replacing sodium caseinate (SC) with pea protein isolate (1:1) and to investigate the lipid digestibility and bioaccessibility of curcumin using in vitro digestion. Structural changes in oil droplets during digestion were also examined using particle‐size measurement and confocal laser‐scanning microscopy. Both SC and mixed protein (MP)‐stabilized NE were stable for the experimental time frame of eight weeks without significant changes in the droplet size. About 50% active curcumin encapsulated in the NE remained stable over eight weeks where the stability was higher for SC compared to the MP‐stabilized NE (MPE). An increase in the droplet sizes and changes in their distribution during in vitro digestion were found to be a combined effect of the presence of digestive enzymes and also the rapid changes in the ionic strength and pH of the system. The lipid digestibility of the MPE was significantly lower than the SC‐stabilized NE, which was attributed to a stronger viscoelastic oil‐droplet interface in the presence of pea proteins. However, the former was found to be as efficient as the latter in successfully releasing about 50% curcumin in the simulated intestine phase. Therefore, pea proteins can be used to partially replace SC as an emulsion stabilizer for the protection and delivery of oil‐soluble bioactive compounds.
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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".