Progress in Supercritical Fluid Technology for Fats and Oils Processing
Bibliographic record
Abstract
Abstract Supercritical CO 2 (SC‐CO 2 ) processing of fats and oils has been widely investigated since SC‐CO 2 offers an environmentally friendly alternative with added advantages such as moderate operating conditions and solvent‐free extracts and residues. From a processing perspective, a unique advantage of SC‐CO 2 processing lies in its versatility, which results from the ability to modify solvent properties by changing operating conditions (temperature and pressure) or by the addition of cosolvents. A good understanding of the fundamentals of solubility behavior of lipid components in SC‐CO 2 as affected by operating conditions and solute properties is required to realize its full potential in fats and oils processing. The operational flexibility offered by supercritical fluid technology enables the processor to fine‐tune solvent properties and to develop novel processes by the integration of unit operations of extraction, fractionation, reaction, and particle formation to meet the process objectives. These unit operations have been effectively utilized for the extraction and refining of oils, concentration of bioactive components from oils or oil by‐products, modification of the physical properties of fats and oils, production of oleochemicals and design of delivery systems for numerous applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".