Optimization of medium composition for lipase production by<i>Candida rugosa</i>NCIM 3462 using response surface methodology
Bibliographic record
Abstract
A sequential optimization approach using statistical design of experiments was employed to enhance the lipase production by Candida rugosa in submerged batch fermentation. Twelve medium components were evaluated initially using the Plackett-Burman 2-level factorial design. The significant variables affecting lipase production were found to be glucose, olive oil, peptone, (NH4)2SO4, and FeCl3.6H2O. Various vegetable oils were tested in the second step, and among them, groundnut oil was found to be the best inducer for lipase production by C. rugosa. The third step was to identify the optimal values of the significant medium components with groundnut oil as the inducer using response surface methodology. The regression equation obtained from the experimental data designed using a central composite design was solved, and analyzing the response surface contour plots, the optimal concentrations of the significant variables were determined. A maximum lipase activity of 5.95 U.mL-1, which is 1.64 times the maximum activity obtained in the Plackett-Burman experimental trials, was observed. The optimum combination of medium constituents contained 19.604 g.L-1 glucose, 13.065 mL.L-1 groundnut oil, 7.473 g.L-1 peptone, 0.962 g.L-1 (NH4)2SO4, 0.0019 g.L-1 FeCl3.6H2O, and other insignificant components at the fixed level. A predictive model of the combined effects of the independent variables using response surface methodology and an artificial neural network was proposed. The unstructured kinetic models, logistic model, and Luedeking-Piret model were used to describe cell mass and lipase production. The parameters of the models were evaluated and the lipase production by C. rugosa was found to be growth associated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".