Extraction and phase behaviour of <i>Moringa oleifera</i> seed oil using compressed propane
Bibliographic record
Abstract
Abstract This study reports the oil extraction efficiency using Moringa oleifera seed and pressurized propane, new experimental data for phase transition of the binary system {n‐propane + Moringa oleifera oil}, and the profile of fatty acid compositions of the extracted Moringa oil. During the present study, the knowledge of the phase behaviour proved to be of great importance to optimize supercritical extraction using pressurized fluid. This project also compares the classical Soxhlet method including mathematical modelling of kinetic curves of extraction. The extraction experiments were performed in the temperature and pressure ranges of 303–333 K and 2.5–12 MPa, respectively, at a constant flow rate of 1.0 cm3/min of n‐propane. All the conditions applied during the n‐propane extraction process provided similar or higher yields (32.8 to 42.1 %) compared to extractions using n‐hexane (42.6 %) and supercritical carbon dioxide (SC‐CO2) (37.8 %). In addition, phase transition study for the {Propane (1) + Moringa oleífera oil (2)} system was performed using a variable volume cell and the static synthetic method in a temperature range of 303–343 K, pressures up to 3.23 MPa, and n‐propane mass fraction between 0.2 and 0.8. Vapour‐liquid (VLE), liquid‐liquid (LLE), and vapour‐liquid‐liquid (VLLE) phase transitions were observed at relatively low pressures. The fatty acid profiles of the extracted Moringa oils were evaluated using gas chromatography. They all have very close composition regardless of the solvent used and oleic acid as the major component (≈76 %). The Sovová mathematical model indicated a good fit for all the conditions investigated.
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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) | 0.000 | 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".