An entropy‐centric equilibrium cooperative theory for the melting behavior of nonideal triaclylglycerol mixtures
Bibliographic record
Abstract
Abstract A new nonideal, equilibrium thermodynamics model was developed for the prediction of the solid fat content (SFC) of edible fats, which are mixtures of triaclylglycerols (TAGs). The SFC is the most dominant material structural parameter which influences macroscopic mechanical properties and functionality in foods, cosmetics and pharmaceutical incipients. By taking into consideration the entropy of mixing and the activity coefficient of a TAG in an effective solid medium, we calculate a freezing point depression. The new melting point is then nested into an equilibrium expression for the melting of that TAG in such effective solid medium. The model assumes that complex mixtures of TAGs consist of one solid phase in a specific polymorphic form and one liquid phase, obeying mass balances and the overall TAG composition determined experimentally. The SFC is then just the summation of the amount of solid TAG components in the mixture. Novel insights are gained from estimates of a cooperativity index for the melting of the different TAGs in the effective solid medium, while estimated solid‐state activity coefficients speak to interactions of particular TAGs with the effective medium. The model was successfully fitted to eight different SFC‐temperature profiles of complex fats and parameter estimates obtained and interpreted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".