Matrix effects on mass spectrometric determinations of four pharmaceuticals and personal care products in water, sediments, and biota
Bibliographic record
Abstract
Simple analytical methods were developed for the extraction and determination of four pharmaceuticals and personal care products (PPCPs) from water, sediments, and biota. PPCPs were determined using tandem LC–MS in electrospray ionization mode, and interactions with matrix co-eluents were investigated. Extractions of water samples were performed using solid-phase extraction (SPE), sediments were extracted by pressurized liquid extraction (PLE), and biota was extracted by liquid extraction. The selected analytical methods yielded recoveries ≥ 61% in all matrixes. Matrix interactions were investigated throughout the linear range of quantification of each compound, revealing that dissolved salts had relatively minor effects on ionization (between 14% suppression to 12% enhancement), but that sediment and biota extracts caused significant matrix effects (ranging from 56% suppression to 25% enhancement). The direction and magnitude of matrix interactions reflected the physico-chemical properties of each analyte, particularly their pK a . Among the compounds analyzed in electrospray positive mode, carbamazepine was insensitive to matrix interactions, because it is a strong proton acceptor (pK a = 14.0). In contrast, atorvastatin (pK a = 4.5), a weaker proton acceptor, was particularly sensitive to matrix effects. For those compounds analyzed in negative-ion mode, sample alkalinity was found to be important. With a pK a of 10.4, 17α-ethinylestradiol generally exhibited matrix enhancement with increased sample alkalinity. However, the presence of acidic co-eluents contributed to matrix suppression. Lastly, TCS was particularly sensitive to matrix suppression, as its circumneutral pK a (7.9) caused even slight changes in sample pH to considerably impact ionization. We conclude that while different matrixes have clear impacts on ionization of these PPCPs, matrix effects can be quantified and overcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".