Inter-laboratory validation of automated SPME-GC/MS for determination of pesticides in surface and ground water samples: sensitive and green alternative to liquid–liquid extraction
Bibliographic record
Abstract
An automated solid-phase microextraction gas chromatography/mass spectometry (SPME-GC/MS) method was developed for the determination of semi-volatile pesticides from several classes with a wide range of polarities in an environmental matrix, and validated according to the rigorous standards of a large commercial laboratory reporting data requiring regulatory acceptance with the purpose of being used as a standard test protocol. The target analytes showed a detection limit of 0.05–1 μg L−1, good calibration linearity (R2 > 0.99) with a wide linear range of 0.05–20 μg L−1, and accuracy in the range of 80–110 at three levels of calibration with relative standard deviation below 7% by commercial polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fiber. An extensive study between SPME and liquid–liquid extraction as a reference US EPA method was performed from several analytical aspects including sensitivity, accuracy, repeatability, and greenness. The SPME method was validated through split blind analyses of 16 fortified surface and ground water samples within 4 months at Maxxam Analytics, the reference laboratory, and the University of Waterloo. Both methods were shown to be very accurate, with the highest frequency of results falling in the 70–130% accuracy range. The SPME method was shown to be more sensitive than the LLE, while requiring a lower volume of sample.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".