Extraction of macauba kernel oil using supercritical carbon dioxide and compressed propane
Bibliographic record
Abstract
Abstract Macauba kernel oil (MKO) was extracted using supercritical CO2 and compressed propane as the solvent. The influence of temperature (313–353 K for CO2 and 303–333 K for propane) and pressure (18–22 MPa for CO2 and 8–12 MPa for propane) on oil yield was investigated, and the results were compared with conventional extraction. A characterization of oil and defatted meal was performed. Temperature and pressure, in the evaluated range, influenced oil yield with the use of CO2. For propane, the oil yield was not influenced by the experimental conditions. In the evaluated experimental range, the use of propane and CO2 provided 100 % and ∼93 % oil yield compared to conventional extraction, respectively. MKO showed a predominance of saturated fatty acids, among which lauric acid was the major constituent. CO2 presented greater efficiency in the removal of phytosteroids and tocopherols from the macauba kernel, however, the oil extracted with propane presented longer oxidative stability. Defatted meals presented modified physico‐chemical composition and potentiated technological characteristics due to oil removal.
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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 | 0.001 | 0.001 |
| 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".