Ultralow phosphorus achievement in vegetable oil degumming: Synergistic effects of citric acid chelation and trace NaOH flocculation
Bibliographic record
Abstract
Degumming is critical for downstream refining efficiency and edible oil quality, but conventional water degumming is incomplete, while enzymatic degumming is slow. This study developed a green and energy-efficient hybrid degumming process, combining instantaneous citric acid chelation with trace sodium hydroxide (NaOH) flocculation. Plackett-Burman screening and Box-Behnken optimization determined the optimal conditions: 339 mg/kg citric acid, 167 mg/kg NaOH, 1.76 % water at 70 °C, which reduced residual phosphorus to 0.22 mg/kg (lower than acid degumming and enzymatic degumming). Confocal laser scanning microscopy (CLSM) showed that NaOH-induced charge neutralization promoted rapid phospholipid flocculation. This process reduced citric acid consumption to 10 % of that in acid degumming, used only 0.017 % NaOH, and shortened degumming time by 30 min compared to acid degumming and 2–4 h compared to enzymatic processes. It aligns with sustainable development goals by cutting chemicals, energy, and emissions. • A novel NaOH aided degumming strategy was developed for oil refining. • NaOH induces rapid phospholipid aggregation and flocculation during degumming. • Optimized process yields degummed oil with only 0.22 mg/kg residual phosphorus. • Compared with water, acid, or enzymatic degumming, this process cuts time and cost sharply.
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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 | 0.000 | 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".