Bibliographic record
Abstract
Lipase-catalyzed glycerolysis converts native triglycerides into partial glycerides and has been shown to be an effective technique for structuring plant-based oils into solid fats without altering the fatty acid composition. This approach has been successfully used to structure a variety of oils with differing fatty acid compositions. For all oil systems studied, a 20 °C increase in crystallization onset temperature was observed. The physical properties of the glycerolysis-structured systems changed depending on the fatty acid composition of the oil and produced a range of solid fat content-temperature profiles suitable for different trans-free applications. Solid fat content increases were greatest for high oleic oils containing >10% saturated fat. The solid fat content of tigernut oil at 5 °C increased from 8% to 34%, while olive oil, which previously had no solid material contained 24.1% solids following glycerolysis. Glycerolysis-structured tigernut oil has been used to make margarine with plasticity and firmness similar to commercial margarine and butter. In addition, the melting properties of this product are preferable to those of some palm-based margarines available commercially. The ability to further tailor the properties of structural fats using the glycerolysis reaction conditions makes this an appealing process for producing trans-free lipid systems.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".