Identification of plant-based spilled oils using direct analysis in real-time–time-of-flight mass spectrometry with hydrophobic paper sampling
Bibliographic record
Abstract
Spilled plant-based oils behave very differently in comparison to petroleum oils and require different clean-up measures. They do not evaporate, disperse, dissolve, or emulsify to a significant degree but can polymerize and form an impermeable cap on sediment, smothering benthic media and resulting in an immediate impact on the wildlife community. The current study explored the application of rapid up-to-date direct analysis in real time (DART) with high-resolution mass spectrometry for plant-based oil typing. The study introduced a new concept of using hydrophobic paper to collect and analyze oil samples, thus minimizing sample preparation and expenses. Application of this technique showed its ability to speedily distinguish plant-based from petroleum-based oils. A microcosm experiment exposing plant-based oil samples to weathering processes for comparison with petroleum-based oils demonstrated the ability of the method to classify weathered oil samples and identify their source oil. It was observed that canola and peanut oil were the most resistant to weathering processes. The developed DART-TOFMS method was shown to be accurate for short-term weathered oil spills up to between 12 and 26 days of exposure. The developed method performed identification in less than a day compared to the established multi-day method for oil spill forensics requiring careful sample collection in glass containers, time-consuming laboratory clean-up, lengthy gas chromatography sequences, and careful integration including integration of retention time markers.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".