Optimization of biosurfactant production by Pseudomonas aeruginosa strain Pa using rubber tree seed oil as sole carbon source
Bibliographic record
Abstract
Abstract Biosurfactants (BS) are highly emulsifying, biodegradable, non- or low-toxic, stable and multifunctional molecules. However, high production costs and low yields limit their large-scale production and use. Consequently, using low-cost substrates (waste) and optimizing production conditions are necessary to reduce production costs and increase the yield of biosurfactants. This study aimed to optimize the conditions for the production of BS by Pseudomonas aeruginosa Pa using rubber tree seed oil RO ( Hevea brasiliensis ), a cheap and available substrate, as the sole carbon source. Factors significantly influencing biosurfactant production were screened using a Plackett–Burman design (PBD) and response was based on the emulsification index. The selected factors were optimized using the response surface methodology (RSM) through a Box-Behnken design (BBD). The biosurfactant produced under the optimized conditions was extracted by the coupled method of acid precipitation and organic solvent extraction using different solvents. PBD results showed that the initial pH of the production medium, NaCl concentration and rubber tree seed oil concentration significantly influenced BS production. Optimal levels of these factors were obtained for a pH of 8.7, a NaCl concentration of 0.072% and a rubber tree seed oil concentration of 6.91%. Under optimized culture conditions, the emulsification index of the biosurfactant produced reached 92.15 ± 0.89%. Rubber tree seed oil showed a BS production capacity superior to commercial carbon sources (conventional sources). Diethyl ether was chosen as a suitable solvent for extracting biosurfactant from the cell-free supernatant. This study showed that the use of rubber tree seed oil, an agro-industrial waste product, is efficient and guarantees the economic feasibility and sustainability of biosurfactant production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".