Esterification effect on the recovery of vitamin <scp>E</scp> from palm oil refining residues by molecular distillation
Bibliographic record
Abstract
Abstract Waste edible oils are an alternative source of high‐value natural compounds. Vitamin E can be recovered from palm oil refining residues by molecular distillation. However, the presence of other lipophilic molecules compromises the selective separation of vitamin E. Esterification of the free fatty acids can enhance the selective separation of vitamin E by molecular distillation, but the conditions for carrying out the reaction need to be investigated to simultaneously ensure the conversion of free fatty acids and the reduction of vitamin E losses. Thus, this study investigated the effect of the esterification of the industrial waste on the recovery of vitamin E by molecular distillation. Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and analysis for fats and oils preconized by the American Oil Chemists' Society (AOCS) were used in the characterization of the industrial waste. Determination of the best condition to carry out the reaction was obtained by a central composite rotational design (CCRD) using the response surface methodology (RSM) and the desirability profile. The results showed that the best condition for the esterification was at 64°C, 213 min, 2 wt.% sulphuric acid, and a 10/1 methanol/free fatty acids molar ratio. This reaction condition achieved 97.9% conversion of free fatty acids and less than 3% of vitamin E loss. The esterification promoted concentration of vitamin E in the residue stream (145.4%) and reduction in the distillate stream (87.8%). Therefore, the obtained results presented a suitable route to obtaining vitamin E concentrate and adding value to an industrial residue.
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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".