The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques
Bibliographic record
Abstract
Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".