Rapid spilled oil analysis using direct analysis in real time time-of-flight mass spectrometry
Bibliographic record
Abstract
Abstract Background The biomarker diagnostic ratio analysis outlined by the European Committee for Standardization is considered the current gold standard in oil forensic analysis. However, it has a major limitation as an emergency response procedure in the case of a large scale oil spill due to the high number of samples collected, long GC/MS instrument run time, and the time-consuming data processing required. This current study utilized direct analysis in real time time-of-flight mass spectrometry to develop a rapid spilled oil screening method. An exploratory search of biomarkers and synthetic additives was conducted on reference oil samples of various types. To build a robust yet swift procedure for oil typing, specific heat maps were built with extensive reference sample modelling. These heat maps were then used to select relevant ions from which principal component analysis and discriminant analysis of principal component models were constructed to result in defensible oil classifications. Results The initial exploratory search of biomarkers and additives in the various reference oil samples resulted in promising preliminary matches. The heat map and multivariate statistical analysis oil typing method was applied to three unknown samples, all of which were classified accurately. Conclusion The merit of direct analysis in real time time-of-flight mass spectrometry on oil forensic was confirmed with the detected biomarkers compound class starting members and lubricating additives along with the successful application of heat maps and multivariate statistical analysis, providing a swift yet reliable screening tool for oil spill environmental monitoring and impact surveying.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.783 | 0.006 |
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; both teacher heads agree on what is shown here.
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".