A Highly Stable Soybean Oil‐Rich Miscella Obtained by Ethanolic Extraction as a Promising Biodiesel Feedstock
Bibliographic record
Abstract
Abstract Soybean oil is industrially obtained upon hexane extraction. In biodiesel production, soybean oil is submitted to phospholipid removal in order to improve its quality before transesterification. An extraction process was employed to produce ethanolic oil‐rich miscella, which can be directly transesterified to produce biodiesel without prior refining. We assessed the oxidative stability of the miscella and three other soybean oils, namely degummed, alkali‐refined, and refined–bleached–deodorized (RBD) oil. In vitro antioxidant assays as well as the identification and quantification of tocopherols and isoflavones were also performed. Although hexane‐extracted oils showed higher tocopherol contents than miscella, this latter sample and its direct biodiesel demonstrated superior stability in accelerated tests. Miscella also outperformed hexane‐extracted oils in all in vitro assays. This behavior can be explained by the presence of phenolic compounds with higher affinity to ethanol than hexane, which was confirmed by the identification of isoflavones glycitein, genistein, and acetyldaidzin, found only in miscella. This study showed that the ethanolic extraction of soybean oil generated a highly stable lipid feedstock for biodiesel manufacture.
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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 | 0.001 | 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".