Prioritizing Chemical Features in Non-targeted Analysis through Spatial Trend Analysis: Application to the Identification of Organic Chemicals Subject to Mountain Cold-Trapping
Bibliographic record
Abstract
One of the challenges arising during non-targeted analysis (NTA) is that the number of detected chemical features is generally too large for detailed processing and interpretation. Here, we illustrate how the analysis of spatial trends in peak intensities can be an effective tool to prioritize chemical features in NTA. Specifically, features detected by gas chromatography and high-resolution mass spectrometry in soil and air samples, collected along an altitudinal transect on an urban mountain in Canada, were successfully grouped into different categories based on spatial trends with site altitude. The motivation was to identify features whose abundance increases in soil with increasing elevation, as the ability for amplification at higher elevations could characterize contaminants of concern to mountain ecosystems. Potential matching candidates were first selected by comparing empirically detected accurate masses and isotope distributions of chemical features with those in chemical databases. These potential candidates were then ranked by comparing MSMS spectra with fragments predicted in silico. Several highly ranked matches, as well as structurally related compounds, which were largely halogenated methoxylated benzenes and organochlorine pesticides, were then subjected to targeted analysis with analytical standards. Several of these compounds, including pentachloroanisole, tricamba, and 3,4,5-trichloroveratrole, were identified as having spatial patterns consistent with mountain cold-trapping, as evidenced by organic carbon-normalized soil concentrations that show a significant increase with elevation. Our study clearly demonstrated that spatial trend analysis holds considerable promise as a tool to guide chemical identification and prioritization during NTA.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".