Rheological properties of ethylcellulose oleogels of oil glycerolysis products as functional adipose tissue mimetics
Bibliographic record
Abstract
Comparative analysis of the rheological behavior of fat analogues and adipose tissue is useful as a means of better understanding the behavior of these ingredients in reformulation and cooking processes. Fat mimicking oleogels were developed based on various enzymatically glycerolized oils and ethylcellulose (EC) of differing molecular weights at a 5% concentration. The dynamic rheological behavior of the oleogels and adipose tissue of different origins (pork, beef, lamb) was studied via amplitude and frequency sweeps. Glycerolized oils and their oleogels behaved as solid-like materials, as elastic moduli were greater than viscous moduli throughout the linear viscoelastic region. Addition of EC produced a more deformable and cohesive product. Solid fat content was a dominant contributor to viscoelastic properties of GP-EC oleogels. Oil systems containing higher levels of saturated fatty acids such as palm olein and shea olein exhibited a more rigid structure, with stiffness comparable to that of pork and beef adipose tissue. Whereas tigernut, peanut, and rice bran oil oleogels were significantly softer and more deformable. Temperature sweep experiments were conducted to visualize the general softening behavior during cooking. The melting of fat dramatically softened both adipose tissue and oleogels. However, the microstructure of adipose tissue was intricately linked to its protein matrix at elevated temperatures, leading to substantial increase of elasticity from 50 °C to 80 °C. Oleogels exhibited thixotropic behavior by recovering their elasticity and functionality over time once the force is removed. This characteristic minimizes the shear related issues during and after the gel-setting process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".