Roles of Process Parameters on the Ricinoleic Acid Production from Castor Oil by Aspergillus flavus BU22S
Bibliographic record
Abstract
Ricinoleic acid is a biobased green chemical industrially produced from castor oil. Microbial conversion is a cleaner and greener approach to ricinoleic acid production from castor oil. These processes should be further optimized for a better yield of the product. Aspergillus flavus BU22S was used to convert castor oil into ricinoleic acid. The strain was isolated and identified by molecular biological techniques. It was found to be effective in the biotransformation of castor oil. The ricinoleic acid production and dry cell weight of the fungus were studied as functions of time. In this study, to increase the yield of ricinoleic acid and decrease the oil loss, which microorganisms utilizes in biomass production, response surface methodology (RSM) has been used for process optimization. The central composite design was used to optimize the predictor variables such as oil concentration (% w/v), glucose concentration (% w/v), and calcium chloride concentration (% w/v) to increase the overall yield of ricinoleic acid. A quadratic model was found to be the best fit to predict the responses of the experimental results. The model suggested that the concentrations of oil, glucose, and calcium chloride should be lower in order to increase the ricinoleic acid yield and minimize the oil loss. The bench scale studies of optimized conditions from RSM were also conducted. The yield of ricinoleic acid in batch and fed-batch culture studies was also compared. The yield of the ricinoleic acid in batch culture was 21.67 g/kg of total oil. The yield of ricinoleic acid in fed-batch culture in the absence of an external air supply was 46.77 g/kg of total oil. In this case, the oil loss was also reduced to only 12%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".