Exploring Selectivity of Supercritical-CO2 for Vitamin E Extraction from Canola Seeds
Bibliographic record
Abstract
Abstract The objective of the current study was to investigate the selectivity of supercritical-CO 2 for extraction and concentration of Vitamin E components from canola seeds. The selectively extracted Vitamin E in supercritical-CO 2 solvent was related to pressure, temperature, and density through the developed thermodynamic modeling approach. The results suggested that increased pressure and density would enhance the selectivity of supercritical-CO 2 solvent, consequently obtaining highly concentrated Vitamin E. The thermodynamic modeling equations have correlated the selectivity of supercritical-CO 2 solvent for extracting Vitamin E in terms of processing conditions including pressure, temperature, and density of the supercritical-CO 2 solvent fluid. The activity coefficient in thermodynamic modeling was involved with those key parameters that are important in determining selectivity, concentration, and extraction results. The supercritical-CO 2 solvent can be made highly selective by precisely controlling the operating pressure and temperature. This allowed the supercritical-CO 2 solvent to achieve the desired density in the supercritical phase, thereby enhancing the selectivity for targeted components. The thermodynamic mathematical modeling offered valuable insights for enhancing extraction processes in industrial settings. A high regression coefficient via linear structural modeling analysis indicated that the response equation fitted with the experimental data ( R 2 = 0.8737). The experimental results for the separation parameters provide optimal selectivity of supercritical-CO 2 solvent for extracting and concentrating Vitamin E compounds for establishing commercial production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".