Development of a method for the application of solid-phase microextraction to monitor biodegradation of volatile hydrocarbons during bacterial growth on crude oil
Bibliographic record
Abstract
A quantitative solid-phase microextraction, gas chromatography, flame ionization detector (SPME-GC-FID) method for low-molecular-weight hydrocarbons from crude oil was developed and applied to live biodegradation samples. Repeated sampling was achieved through headspace extractions at 30°C for 45 min from flasks sealed with Teflon Mininert. Quantification without detailed knowledge of oil–water–air partition coefficients required the preparation of standard curves. An inverse relationship between retention time and mass accumulated on the SPME fibre was noted. Hydrocarbons from C5 to C16 were dated and those up to C11 were quantified. Total volatiles were quantified using six calibration curves. Biodegradation of volatile hydrocarbons during growth on crude oil was faster and more complete with a mixed culture than pure isolates derived therefrom. The mixed culture degraded 55% of the compounds by weight in 4 days versus 30–35% by pure cultures of Pseudomonas aeruginosa, Rhodococcus globerulus or a co-culture of the two. The initial degradation rate was threefold higher for the mixed culture, reaching 45% degradation after 48 h. For the mixed culture, the degradation rate of individual alkanes was proportional to the initial concentration, decreasing from hexane to undecane. P. fluorescens was unable to degrade any of the low-molecular-weight hydrocarbons and methylcyclohexane was recalcitrant in all cases. Overall, the method was found to be reliable and cost-effective. Journal of Industrial Microbiology & Biotechnology (2000) 25, 155–162.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".