Determination of multi pesticide residues in leaf and needle samples using a modified QuEChERS approach and gas chromatography-tandem mass spectrometry
Bibliographic record
Abstract
In order to gain a better insight into pesticide and pollutant exposure in forests, a rapid and sensitive gas chromatography-tandem mass spectrometry (GC-MS/MS) method for the determination of 208 pesticide residues in leaves and needles has been established. The modified QuEChERS (quick, easy, cheap, effective, rugged and safe) approach uses 2 g of homogenized sample, acetonitrile and water as extraction agents, combined with citrate buffer for the following salting out step. The limits of quantification (LOQs) were determined to 0.0025-0.05 mg kg-1, respectively. Calibration curves showed a linear range between the respective LOQ and 1.0 mg kg-1 with coefficients of determination (R2) ≥ 0.99 for all analyzed pesticides. The recovery rates ranged from 69.7% to 92.0% with a relative standard deviation below 20%. The analysis of beech leaves, spruce and pine needles (each n = 3) provided a proof of concept for the developed methodology and revealed the presence of six pesticide residues (boscalid, epoxiconazole, fenpropimorph, lindane, terbuthylazine, terbuthylazine-desethyl). The results underline the strong need for systematic surveillance of the uncontrollable exposure of pesticides to nature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".