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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".