New Insights on Degumming and Bleaching Process Parameters on The Formation of 3-Monochloropropane-1,2-Diol Esters and Glycidyl Esters in Refined, Bleached, Deodorized Palm Oil
Bibliographic record
Abstract
This paper examines the interactions of degumming and bleaching processes as well as their influences on the formation of 3-monochloropropane-1,2-diol esters (3-MCPDE) and glycidyl esters in refined, bleached and deodorized palm oil by using D-optimal design. Water degumming effectively reduced the 3-MCPDE content up to 50%. Acid activated bleaching earth had a greater effect on 3-MCPDE reduction compared to natural bleaching earth and acid activated bleaching earth with neutral pH, indicating that performance and adsorption capacities of bleaching earth are the predominant factors in the removal of esters, rather than its acidity profile. The combination of high dosage phosphoric acid during degumming with the use of acid activated bleaching earth eliminated almost all glycidyl esters during refining. Besides, the effects of crude palm oil quality was assessed and it was found that the quality of crude palm oil determines the level of formation of 3-MCPDE and glycidyl esters in palm oil during the high temperature deodorization step of physical refining process. Poor quality crude palm oil has strong impact towards 3-MCPDE and glycidyl esters formation due to the intrinsic components present within. The findings are useful to palm oil refining industry in choosing raw materials as an input during the refining process.
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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) | 0.001 | 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".