Chemical and enzymatic interesterification of tristearin/ triolein‐rich blends: Chemical composition, solid fat content and thermal properties
Bibliographic record
Abstract
Abstract Blends of high‐oleic sunflower oil and fully hydrogenated canola oil were subjected to enzymatic and chemical interesterification using Candida antarctica lipase (5%) and sodium methoxide (0.3%), respectively. The effect of each interesterification process was determined by comparing the triacylglycerol (TAG) composition, solid fat content (SFC) profiles and thermal properties of the blends before and after interesterification. Interesterification resulted in a decrease in the concentration of triunsaturated and trisaturated TAG and an increase in the proportion of mono‐ and disaturated TAG. These alterations in TAG composition and the presence of a greater variety of TAG species upon interesterification was correlated with a broader melting transition by differential scanning calorimetry and, ultimately, a lower melting point for the interesterified blends. Much broader ranges in plasticity were observed for the interesterified blends (chemically and enzymatically) compared to the physical blends. Even though ideal solubility of stearin in oil was observed, the value predicted by the Hildebrand model was higher than the actual amount. Crystallization kinetic parameters (Avrami index and rate constant) were similar for the non‐interesterified, enzymatically interesterified and chemically interesterified blends when compared as a function of SFC. Results from this work will aid in the formulation of more healthy fat and oil products and address a critical industrial demand in terms of formulation options for spreads, margarines and shortenings.
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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) | 0.001 | 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".