Characterization of rhamnolipid production by Burkholderia glumae
Bibliographic record
Abstract
AIMS: To investigate if Burkholderia glumae can produce rhamnolipids, define a culture medium for good production yields, analyse their composition and determine their tensioactive properties. METHODS AND RESULTS: Burkholderia glumae AU6208 produces a large spectrum of mono- and di-rhamnolipid congeners with side chains varying between C(12)-C(12) and C(16)-C(16), the most abundant being Rha-Rha-C(14)-C(14).The effects on rhamnolipid production of the cultivation temperature, nitrogen and carbon source were investigated. With urea as the nitrogen source and canola oil as the carbon source, a production of 1000.7 mg l(-1) was reached after 6 days. These rhamnolipids display a critical micelle concentration of 25-27 mg l(-1) and decrease the interfacial tension against hexadecane from 40 to 1.8 mN m(-1). They also have excellent emulsifying properties against long chain alkanes. CONCLUSIONS: Burkholderia glumae AU6208 can produce considerable amounts of rhamnolipids. They are produced as diversified mixtures of congeners. Their side chains are longer than those normally produced by those of Pseudomonas aeruginosa. They also present excellent tensioactive properties. SIGNIFICANCE AND IMPACT OF THE STUDY: In contrast with the classical rhamnolipid producer Ps. aeruginosa, B. glumae is not a pathogen to humans. This work shows that the industrial production of rhamnolipids with this species could be easier than with Ps. aeruginosa.
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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) | 0.000 | 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) | 0.000 | 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".