Supercritical CO<sub>2</sub> Extraction of Flax Lignans
Bibliographic record
Abstract
Abstract Lignans, such as flaxseed secoisolariciresinol diglucoside (SDG), have been implicated in the prevention of hormonally related cancers and other prevalent diseases. Lignans are typically extracted using organic solvents, which must then be removed from the extract. Supercritical carbon dioxide (SC‐CO2) is a non‐toxic, inexpensive solvent, which, when combined with polar modifiers, can be used to extract polar phenolic compounds, such as SDG. The effects of processing conditions and pre‐treatment on the extraction of SDG using SC‐CO2 were investigated. Extraction from defatted flaxseed was performed with SC‐CO2 modified with ethanol at levels of 0, 10 and 20 mol% at different temperature (40, 50 and 60 °C) and pressure (35, 40 and 45 MPa) conditions. Extracts were analyzed using RP‐HPLC. The condition (7.8 mol% ethanol, 45 MPa and 60 °C) which produced the maximum CO2 loading of SDG (0.49 μg/g CO2) was used for the pre‐treatment study, in which flaxseed defatted with petroleum ether and SC‐CO2, full‐fat hulls, defatted hulls and pre‐hydrolyzed flaxseed were used. Temperature, pressure and solvent modifier level had no significant effect (p > 0.05) on the SDG loading of CO2. However, pre‐hydrolyzed seed resulted in a significantly (p ≤ 0.05) higher CO2 loading of SDG (3.8 μg/g CO2) compared to the other treatments studied. The yields obtained represented only a small fraction of the original lignan content (15 mg/g petroleum ether defatted flaxseed).
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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 | 0.000 |
| 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) | 0.001 | 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".