Automated Tandem Mass Spectrometry by Orthogonal Acceleration TOF Data Acquisition and Simultaneous Magnet Scanning for the Characterization of Petroleum Mixtures
Bibliographic record
Abstract
The automated acquisition of the product ion spectra of all precursor ions in a selected mass range by using a magnetic sector/orthogonal acceleration time-of-flight (oa-TOF) tandem mass spectrometer for the characterization of complex petroleum mixtures is reported. Product ion spectra are obtained by rapid oa-TOF data acquisition and simultaneous scanning of the magnet. An analog signal generator is used for the scanning of the magnet. Slow magnet scanning rates permit the accurate profiling of precursor ion peaks and the acquisition of product ion spectra for all isobaric ion species. The ability of the instrument to perform both high- and low-energy collisional activation experiments provides access to a large number of dissociation pathways useful for the characterization of precursor ions. Examples are given that illustrate the capability of the method for the characterization of representative petroleum mixtures. The structural information obtained by the automated MS/MS experiment is used in combination with high-resolution accurate mass measurement results to characterize unknown components in a polar extract of a refinery product. The exhaustive mapping of all precursor ions in representative naphtha and middle-distillate fractions is presented. Sets of isobaric ion species are separated and their structures are identified by interpretation from first principles or by comparison with standard 70-eV EI libraries of spectra. The utility of the method increases with the complexity of the samples.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".