Molecular Patterns of Crude Oils with Varying Naphthenic Acidity Revealed by ESI(−)-FT-ICR MS and Unsupervised Learning
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Acidic crude oils are becoming more prominent in global markets due to technological advances that enable the exploitation of unconventional reserves. Understanding their chemical nature is essential for refining strategies, especially as acidity poses processing challenges. Unsupervised pattern recognition offers a way to uncover meaningful relationships between molecular composition and macroscopic acidity, reducing the manual interpretation effort. This study proposes an unsupervised learning framework to reveal such patterns efficiently. Crude oil samples from the Sergipe-Alagoas Basin (northeast Brazil) were analyzed using negative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(−)-FT-ICR MS), focusing on polar compounds. Bulk acidity was assessed through total acid number (TAN), naphthenic acidity index (NAI), and percentage of naphthenic acids (%NA). K-means clustering applied to these parameters identified four acidity-level groups. Clusters I and II contained low-TAN oils (<1 mg KOH g –1 ), differing in %NA, while Clusters III and IV grouped high-TAN oils (>1 mg KOH g –1 ), also separated by %NA. Multidimensional scaling (MDS) was applied to the autoscaled abundance of globally shared molecular formulas from the O 1 –O 4 oxygen classes. While individual classes had limited discriminatory power, combined O 1 –O 3 formulas enabled clear differentiation among acidity levels. This framework demonstrates that the data-driven analysis of high-resolution mass spectrometry data can be automated to enhance the molecular understanding of acidic oils, providing valuable insights for refining decisions and linking molecular fingerprints to macroscopic acidity behavior.
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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 | 0.001 | 0.001 |
| 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".