Petroleum contamination characterization and quantification using fluorescence emission-excitation matrices (EEMs) and parallel factor analysis (PARAFAC)
Bibliographic record
Abstract
This paper introduces a novel approach to characterize and semi-quantify common petroleum contaminants (natural gas condensate, gasoline, diesel, flare pit residue, and heavy crude oil) and their underlying aromatic hydrocarbon components in solutions based on their fluorescence spectral signatures. The method uses fluorescence excitation-emission matrices (EEMs) combined with multivariate statistical procedures: parallel factor analysis (PARAFAC) and soft independent method of class analogy (SIMCA) to identify the petroleum products. Quantitatively, fluorescence intensities of EEMs of analyzed petroleum products at different concentrations are used to establish standard calibration curves that can be employed to estimate unknown concentrations of similar petroleum products in solutions. As well, underlying aromatic hydrocarbon component concentrations are estimated by performing customized PARAFAC analysis. This approach provides fingerprints for different petroleum products along with estimates of their concentrations in non-fluorescing solvents. Concentrations of predicted PARAFAC components were validated by laboratory chemical analytical results of the same petroleum products.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| 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 | 0.000 | 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".