Fractionation of ethylcellulose oleogels during setting
Bibliographic record
Abstract
The use of ethylcellulose (EC) polymers as a means to structure edible oils for fat replacement is beginning to show great promise and the use of these 'oleogels' has recently been shown to be feasible in food products. These gels are very versatile, as the mechanical properties can be tailored by altering either the fatty acid profile of the oil component, or the viscosity or concentration of the polymer component. Here we report the observation that certain formulation of EC oleogels tend to separate into two distinct phases; a soft interior core surrounded by a firm exterior sheath. It was found that the extent of this effect depends on EC viscosity, and can also be induced through the addition of certain surfactants, such as sorbitan monostearate and sorbitan monooleate, though not by glycerol monooleate. Although the two visually distinct regions were shown to be chemically indistinct, the cooling rate during gel setting was found to play a large role; rapid setting of the gels reduces the fractionation effect, while slow cooling produced a completely homogeneous structure. In addition, by reheating only the soft region of the gel, a firm and soft fractionated gel could again be produced. Finally, it was observed that oleogels prepared with castor oil or mineral oil have the ability to remove or induce the gel separation, respectively. Taken together, these results indicate chemical interactions may incite the separation into two distinct phases, but the process also seems to be driven by the cooling conditions during gel setting. These findings lend insight into the EC-oleogel gelation process and should provide a stepping stone for future research into the manufacturing of these products.
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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 | 0.001 |
| 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".