The Physical State of Emulsified Edible Oil Modulates Its in Vitro Digestion
Bibliographic record
Abstract
Emulsified lipid digestion was tailored by manipulating the physical state of dispersed oil droplets in whey protein stabilized oil-in-water (O/W) emulsions, where the oil phase consisted of one of five ratios of soybean oil (SO) and fully hydrogenated soybean oil (FHSO). The evolution in particle size distribution, structural changes during oral, gastric, and intestinal digestion, and free fatty acid release during intestinal digestion were all investigated. Irrespective of the physical state and structure of the dispersed oil/fat, all emulsions were stable against droplet size increases during oral digestion. During gastric digestion, the 50:50 SO:FHSO emulsion was more stable against physical breakdown than any other emulsion. All emulsions underwent flocculation and coalescence or partial coalescence upon intestinal digestion, with the SO emulsion being hydrolyzed the most rapidly. The melting point of all emulsions containing FHSO was above 37 °C, with the presence of solid fat within the dispersed oil droplets greatly limiting lipolysis. Fat crystal polymorph and nanoplatelet size did not play an important role in the rate and extent of lipid digestion. Free fatty acid release modeled by the Weibull distribution function showed that the rate of lipid digestion (κ) decreased with increasing solid fat content, and followed an exponential relationship ( R 2 = 0.95). Overall, lipid digestion was heavily altered by the physical state of the dispersed oil phase within O/W emulsions.
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) | 0.000 | 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".