Solid-Phase Microextraction Coupled to LC-ESI-MS/MS: Evaluation and Correction for Matrix-Induced Ionization Suppression/Enhancement for Pharmaceutical Analysis in Biological and Environmental Samples
Bibliographic record
Abstract
Solid-phase microextraction (SPME) coupled to liquid chromatography with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) has been widely used to analyze biological fluids, tissues, and environmental matrixes for a variety of organic compounds including pharmaceuticals. However, effects of the sample matrix coextracted by SPME on tandem mass spectrometry analysis have not been systematically investigated. In this study, we characterized the complexity of matrix effects (ME) by analyzing SPME extracts of fish muscle and brain tissue, blood, and bile, as well as tap water, surface water, and the influent and effluent from a wastewater treatment plant. Significant enhancement or suppression of ionization (>15%) was observed with all biological and environmental samples. Intrasample ME variability was assessed through comparison of multiple samples from the same sample matrix, while intersample variability between different experimental subjects or varying sample treatment, storage, and sampling conditions were evaluated. To correct for ME, an isotopic internal standard (IIS) method was developed, with the strengths and limitations of the approach discussed. This study provides a framework for applying SPME within complex sample systems where the influences of ME are inevitable, thus ensuring more accurate quantitation of analytes during biological and environmental analysis.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".