Enhanced extraction of flaxseed oil, tocopherols, and fatty acids using supercritical carbon dioxide with ethanol: Process optimization and modelling
Bibliographic record
Abstract
Abstract This study explores the extraction of flaxseed oil using supercritical CO2 (SC‐CO2) with ethanol as a co‐solvent, optimizing key process parameters to enhance oil yield and total tocopherol content (TTC). The influence of pressure (20–30 MPa), temperature (40–60°C), and CO2 flow rate (2–6 mL/min) was analyzed using response surface methodology (RSM). The optimal conditions (20 MPa, 51°C, 2 mL/min) yielded 36.86% oil and 112.71 ppm tocopherols, demonstrating the effectiveness of SC‐CO2 extraction. The addition of ethanol improved tocopherol and monounsaturated fatty acid (MUFA) recovery, while extraction without ethanol favoured polyunsaturated fatty acid (PUFA) retention. Gas chromatographic analysis confirmed that SC‐CO2 extraction produced a superior fatty acid profile compared to Soxhlet extraction, preserving higher levels of α‐linolenic acid (ALA) and oleic acid. Solubility studies indicated that moderate pressure and temperature conditions enhance oil recovery. SC‐CO2 with ethanol proved to be an efficient and environmentally friendly alternative to conventional extraction methods, producing high‐quality flaxseed oil rich in bioactive compounds. These findings support the scalability of SC‐CO2 extraction for nutraceutical, pharmaceutical, and food applications. Future research should explore antioxidant stability and large‐scale processing feasibility to enhance industrial adoption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".