Immobilization of Phospholipase A<sub>1</sub> and its Application in Soybean Oil Degumming
Bibliographic record
Abstract
Abstract Phospholipase A1 (PLA1), or Lecitase® Ultra, was immobilized on three different supports, calcium alginate (CA), calcium alginate‐chitosan (CAC), and calcium alginate‐gelatin (CAG), and crosslinked with glutaraldehyde. The results indicated that PLA1–CA retained 56.2% of the enzyme's initial activity, whereas PLA1–CAC and PLA1–CAG retained 65.5 and 60.2%, respectively. Compared with free PLA1, the optimal pH of immobilized PLA1 shifted to the basic side by 0.5–1.0 pH units and the pH/activity profile range was considerably broadened. Similarly, the temperature‐optima of PLA1–CAC and PLA1–CAG increased from 50 to 60 °C, and their thermal stability increased with relative activities of more than 90% that covered a wider temperature range spanning 50–65 °C. In a batch oil degumming process, the final residual phosphorus content was reduced to less than 10 mg/kg with free PLA1, PLA1–CAC and PLA1–CA in less than 5, 6 and 8 h respectively while PLA1–CAG was only able to reduce it to 15 mg/kg in 10 h. When the PLA1–CAC was applied in a plant degumming trial, the final residual phosphorus content was reduced to 9.7 mg/kg with 99.1% recovery of soybean oil. The recoveries of immobilized PLA1–CAC and activity of PLA1 were 80.2 and 78.2% respectively. Therefore, it was concluded that PLA1–CAC was the best immobilized enzyme complex for the continuous hydrolysis of phospholipids in crude vegetable oils.
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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".