Microstructure and rheology of cellulose bead-filled whey protein isolate oleogels
Bibliographic record
Abstract
This study investigated the oleogelation of cellulose bead dispersions in a sunflower oil oleogel made with solvent-transferred whey protein isolate. The microstructure and rheology of the mixed gels depended on the ratio of hydrated cellulose beads to proteins (9:1, 8:2, 7:3, and 1:1). Two gel stabilization mechanisms were identified. In gels dominated by cellulose beads, capillary bridging and the avoidance of the continuous oil phase dominated network formation. With increasing protein content, the cellulose beads became covered by a layer of aggregated protein, leading to a protein-dominated network. All mixed gels displayed similar elastic behaviour. Oil release was greater in the gels at the highest cellulose bead to protein ratio, which aligned with the presence of void spaces in the network visible under the microscope. These findings serve as the foundation to explore other particle-filled oleogels for use in food-related applications. • Combined protein oleogel and cellulose bead dispersion gel vegetable oil. • Cellulose beads incorporated within protein oleogel form aggregated network. • No change in firmness with cellulose beads dispersed in protein oleogel. • Water necessary to aggregate protein particles and cellulose beads. • Optimization of cellulose bead to protein mass ratio limits oil release.
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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".