Triacylglycerol Crystallinity and Emulsion Colloidal Acid Stability Influence In Vitro Digestion Lipolysis and Bioaccessibility of Long-Chain Omega-3 Fatty Acid-Rich Nanoemulsions
Bibliographic record
Abstract
This study investigated the relationships between emulsion droplet triacylglycerol (TAG) crystallinity and colloidal acid stability on in vitro digestion microstructure, lipolysis, and docosahexaenoic acid (DHA) bioaccessibility. Oil-in-water (o/w) nanoemulsions (20 wt%) composed of 50/50 DHA-rich algal oil with either palm stearin (PS) or olein (PO), and either acid-stable Tween 80 (2.0 wt%; AS) or acid-unstable soy lecithin (2.2 wt%; AU) were fast or slow cooled to 37 °C after microfluidization. Similar particle size distributions and D3,2 (~131–142 nm) and D4,3 (~208–239 nm) values were achieved. All emulsions were highly electronegative (~−45–70 mV) and differences (p < 0.05) were due to emulsifier type, as expected, and cooling rate. Next, emulsions were subjected to INFOGEST in vitro digestion for analysis of intestinal lipolysis by free fatty acid titration and DHA bioaccessibility. As expected, AU emulsions flocculated, forming larger aggregates during the gastric phase. Slower lipolysis was observed for the AU emulsions (p < 0.05), attributed to gastric phase aggregation, and lower 2 h lipolysis was observed for the PS emulsions (~74–77%) based on the presence of crystallinity. DHA bioaccessibility was high (~57–88%), especially for the AS emulsions (p < 0.05). Therefore, emulsion colloidal acid stability and TAG physical state significantly impacted emulsion gastric microstructure, digestion, and bioaccessibility.
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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".