Enzymatic esterification/transesterification of rice bran acid oil for subsequent γ-oryzanol recovery
Bibliographic record
Abstract
This study recovered γ-oryzanol from rice bran acid oil (RBAO), following an initial enzymatic esterification/transesterification to selectively convert its glyceride impurities into fatty acid ethyl esters (FAEEs) or biodiesel. γ-oryzanol was then deprotonated and separated from the biodiesel into the resulting aqueous phase via acid-base extraction. Herein, we determine the effects of varying reaction conditions, i.e., ethanol:RBAO molar ratio, temperature, reaction time, enzyme loading, and agitation speed, on the degrees of glyceride removal, γ-oryzanol loss, free fatty acid (FFA) remaining, and biodiesel content. Up to 100% glyceride removal was achieved with a relatively high biodiesel yield (84%) and γ-oryzanol loss as low as 26% under our most suitable reaction conditions (5:1 ethanol:RBAO molar ratio, 40 °C, 24 h reaction time, 10%wt enzyme loading, 200 rpm agitation). Furthermore, of the remaining oryzanol, up to 94% was recovered by the acid-base extraction with 2-4 M ethanolic NaOH solution. Our results suggest that a combination of enzymatic esterification/transesterification with subsequent acid-base extraction offers an efficient alternative approach to the simultaneous production of biodiesel and γ-oryzanol recovery from low-cost RBAO. Based on our analysis of techno-economic and environmental sustainability, integration of the present method into a rice bran oil refinery would make the process profitable, with the minimum use of toxic chemicals and energy.
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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) | 0.001 | 0.001 |
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".