Lipase-catalyzed glycerolysis extended to the conversion of a variety of edible oils into structural fats
Bibliographic record
Abstract
Lipase-catalyzed glycerolysis was recently shown to be a viable technique to structure cottonseed and peanut oils into structural fats by converting native triacylglycerols into partial glycerides without changing overall fatty acid composition. Here, this approach was extended to a variety oils of differing fatty acid compositions. Reactions were performed at 65 °C for 48 h at a triacylglycerol:glycerol molar ratio of 1:1, using the non-regiospecific Candida antarctica lipase B. In all oil systems, a 20 °C increase in crystallization onset temperature was observed following glycerolysis. Solid fat content increases resulting from glycerolysis were greatest for oils containing >10% saturated fat along with a high oleic acid content. The solid fat content of tigernut oil at 5 °C increased from 8% to 34% following glycerolysis. Tigernut glycerolysis product was used to make margarine with plasticity similar to commercial margarine and butter. This research demonstrates that glycerolysis is a general strategy to convert liquid oils into structural fats used in food applications, and thus replace palm oil and hydrogenated fats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".