Development of a general protocol for non-targeted analysis of per and poly-fluoroalkyl substances in drinking water part I: quality assurance/quality control for reproducible identification
Bibliographic record
Abstract
Per - and poly-fluoroalkyl substances (PFAS) make up a large group (or class) consisting of thousands of synthetic chemicals.Chronic human exposure to trace amounts of some PFAS has been linked to adverse health effects (e.g., a higher incidence of breast cancer, renal disease, and thyroid disease). The current monitoring program in Canada targets only the most researched PFAS and the number of PFAS characterized in exposure assessments is still relatively low compared to the total number registered for commercial use, in addition to their transformation products in the environment. “Non-targeted analysis” (NTA) has emerged as a tool for identification and prioritization of chemical substances assessed for human exposure. Unlike “targeted analysis”, there are no clearly established processes for NTA method development and validation, and despite efforts having recently been made to harmonize NTA workflows, there are still inconsistencies that remain. While the sample preparation steps determine the types of chemicals that get extracted (e.g., via choice of elution solvent), standardized data acquisition and data analysis steps are required for reliable chemical identification and quantification without the use of reference standards. The goal of this study was the development of a general NTA protocol including appropriate quality assurance/quality control (QA/QC) elements to provide reproducible NTA results for the identification of PFAS in source and drinking water. Existing software tools (FluoroMatch, TraceFinder, and Compound Discoverer) are employed along with a developed retention time prediction model to improve confidence in chemical substance identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".