Unraveling Matrix Effects: A Study on Drugs of Abuse in Wastewater Samples from Southern Ontario, Canada
Bibliographic record
Abstract
Wastewater composition presents significant analytical challenges in accurately quantifying drugs of abuse (DOAs) due to matrix effects (MEs), a common issue in liquid chromatography–tandem mass spectrometry (LC–MS/MS). This study presents an optimized workflow using solid-phase extraction (SPE) and LC–MS/MS to mitigate matrix effects while maintaining adequate detection limits for the target analytes. The optimized method uses Bond Elut Nexus weak cation exchange cartridges, achieving recoveries between 60 and 100%, and demonstrates superior matrix effect reduction compared to Bond Elut Plexa PCX and Oasis hydrophilic–lipophilic balance (HLB) cartridges. Matrix effect mitigation was further improved by diluting the extract with a selected concentration factor (CF) of 50. The method was validated, exhibiting a linearity of R 2 ≥ 0.9912 and limits of detection ranging from 0.01 to 0.2 ng L –1 . The method was applied to raw wastewater samples from seven municipalities in southern Ontario, Canada, to explore the influence of analyte hydrophobicity, chromatography separation, and population density on matrix effects. The results indicate no clear trends among population density, analyte hydrophobicity, and matrix effects. Additionally, the retention-time-matched correction using the nearest internal standard is ineffective for addressing matrix effects. This work contributes valuable insights to advancing standardized analytical methods applicable within wastewater-based surveillance (WBS) programs to estimate drug consumption worldwide.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".