Enzyme‐assisted aqueous extraction of oil and protein from canola (<b><i>Brassica napus </i></b>L.) seeds
Bibliographic record
Abstract
Abstract The emphasis of this study was to investigate the effect of enzymes on aqueous extraction of canola (Brassica napus L.) seed oil and protein. Four enzymes, Protex 7L, Multifect Pectinase FE, Multifect CX 13L, and Natuzyme, were tested for their effectiveness in releasing oil and protein during aqueous extraction. The enzyme‐extracted oil content of canola seeds (22.2–26.0%) was found to be significantly (p <0.05) higher than that of the control (without enzyme) (16.48%). An appreciable amount of protein (3.5–5.9%) originally present in the seed was extracted into the aqueous and creamy phases during aqueous extraction of oil. The physicochemical properties of oils extracted from canola seed by conventional solvent extraction, and aqueous extraction, with or without enzyme addition were compared. Significant (p <0.05) differences were observed in free fatty acid content, specific extinctions at 232 and 270 nm, peroxide value, color (1‐inch cell) and concentration of tocopherols (α, γ, and δ). However, no significant variation (p <0.05) was observed in iodine value, refractive index (40 °C), density (24 °C), saponification value, unsaponifiable matter and fatty acid composition. A better oil quality was obtained with aqueous extraction (with and without enzyme) than with solvent extraction. While the enzymes enhanced the oil extraction, the oil yield was still significantly (p <0.05) lower than that obtained by solvent (hexane) extraction.
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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) | 0.000 | 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".