Ultra-High Performance Liquid Chromatography/Electrospray Ionization-Tandem Mass Spectrometry Determination of 151 Pesticides in Soybeans and Pulses
Bibliographic record
Abstract
This paper presents the application of ultra-high performance LC (UHPLC) and MS for the determination of 151 pesticides in soybeans and pulses. A core-shell particle (2.6 micro m particle size) column and a fully porous sub-2 microm (1.7 microm particle size) column showed comparable performance in chromatographic resolution and separation, increasing selectivity, and reducing analysis time. UHPLC was coupled with either a triple quadrupole mass analyzer (MS/MS) or a quadrupole Orbitrap (namely Orbital trap) mass spectrometer (Q-Orbitrap MS), which possesses fast data acquisition capability. Both configurations yielded analytical run times of < or =14 min. Soybean and pulse samples were analyzed and quantitated for pesticide residues using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) procedure, UHPLC/electrospray ionization (ESI)-MS/MS, and matrix-matched standard calibration curves (in an analytical range of 5-500 microg/kg) with isotopically-labeled standards or a chemical analog as internal standards. The method performance parameters that included overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a nested design experiment. Approximately 89% of the pesticides studied had recoveries between 81 and 110%; 95%, had intermediate precision < or =20%; and 93% showed measurement uncertainty < or =40%. From a pilot study of 100 samples, eight tested positive by UHPLCIESI-MS/MS for carbendazim, methomyl, or imidacloprid. These pesticides were further confirmed using UHPLC/ESI-Q-Orbitrap MS based on accurate mass measurement with mass error < or =5 ppm.
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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) | 0.001 | 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 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".