An international round-robin study for the analysis of particulate semi-volatile organics by thermal desorption gas chromatography mass spectrometry
Bibliographic record
Abstract
Thermal desorption gas chromatography mass spectrometry (TD-GC/MS) is becoming more commonly used for the quantification and identification of organic compounds in particulate matter (PM), including ambient and source PM such as diesel particulate matter (DPM). It has been proven as an alternative to the traditional solvent extraction (SE) method and liquid injection gas chromatograph mass spectrometry (LI-GC/MS). However, little information is available on how different types of TD-GC/MS systems compare to each other for analysis of real-world PM samples or to direct LI-GC/MS for analysis of PM components in a test solution. To address this, CanmetENERGY Characterization Laboratory initiated a round robin with the participation of 10 laboratories worldwide. Three sample types were analysed: (i) a test solution with a suite of pure compounds commonly found in PM, analysed by TD-GC/MS and LI-GC/MS; (ii) a DPM sample, analysed by TD-GC/MS and SE; and (iii) an ambient PM sample, analysed by TD-GC/MS. The first part of the study showed good overall performance and comparability between the different TD-GC/MS systems and LI-GC/MS method for the analysis of PM components in a test solution, with some variability of results due to system types and parameters used, concentration of calibration standards, and whether or not an internal standards was used. The analysis of the DPM sample showed greater variability between laboratories and methods as many PM components were present near the detection limit and matrix effects particularly affected the TD-GC/MS analysis of heavier n-alkanes. In the last part of the study, for the analysis of an ambient PM sample by TD-GC/MS, the analysis of variance showed good comparison between labs for polycyclic aromatic hydrocarbons (94% non-significant), but slightly lower for n-alkanes (68%) and biomarkers (57%).
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".