Characterizing and Compensating for Matrix Effects Using Atmospheric Pressure Chemical Ionization Liquid Chromatography−Tandem Mass Spectrometry: Analysis of Neutral Pharmaceuticals in Municipal Wastewater
Bibliographic record
Abstract
Matrix effects are a great challenge for the quantitative analysis of environmental samples by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Signal suppression or enhancement can compromise the accuracy of analytical results. While matrix effects have been relatively well studied for applications of LC-MS/MS instrumentation with electrospray ionization, there have been relatively few studies to evaluate matrix effects when using atmospheric pressure chemical ionization (APCI) as the ion source. In this study, we determined the effects of sample matrix on the analysis of six neutral pharmaceuticals (i.e., caffeine, cotinine, carbamazepine and its major metabolite, carbamazepine-10,11-dihydrodiol, trimethoprim, and fluoxetine) in samples of municipal wastewater using LC-APCI-MS/MS and evaluated whether isotope-labeled internal standards can be used to compensate for matrix effects. The matrix effects were measured using postextraction spikes and postcolumn direct infusion, respectively. The results showed that the matrix in the extracts prepared from municipal wastewater enhanced the signals for four of the six analytes when using an APCI source. Without correction for signal enhancement, apparent recoveries of the analytes from wastewater samples were overestimated to levels as high as 178% of the spiked amount. Isotope-labeled compounds corrected for these overestimates that occurred as a result of interferences from the sample matrix.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".