Comparison of Atmospheric Pressure Ionization Gas Chromatography-Triple Quadrupole Mass Spectrometry to Traditional High-Resolution Mass Spectrometry for the Identification and Quantification of Halogenated Dioxins and Furans
Bibliographic record
Abstract
The goal of this study was to qualify gas chromatography coupled to atmospheric pressure ionization tandem mass spectrometry (APGC-MS/MS) as a reliable and valid technique for analysis of halogenated dioxins and furans that could be used in place of more traditional gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS) analysis. A direct comparison of the two instrumental techniques was performed. APGC-MS/MS system sensitivity was demonstrated to be on the single femtogram level. The APGC-MS/MS analysis also demonstrated method detection limits (MDLs) in both sediment and fish that were 2-18 times lower than those determined for the GC-HRMS. Inlet conditions were established to prevent issues with sample carry-over, due largely to the enhanced sensitivity of this technique. Additionally, this work utilized direct injection for sample introduction through the split/splittless inlet. Finally, quantification of both sediment and fish certified reference materials were directly compared between the APGC-MS/MS and GC-HRMS. The APGC-MS/MS performed similarly to, if not better than, the GC-HRMS instrument in the analysis of these samples. This data is intended to substantiate APGC-MS/MS as a comparable technique to GC-HRMS for the analysis of dioxins and furans.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".