Direct extraction with acetonitrile of hemp seed oil for the analysis of pesticides by using comprehensive two-dimensional gas chromatography-triple quadrupole mass spectrometry
Bibliographic record
Abstract
The method herein described involves a rapid and limited-volume (0.5 mL of acetonitrile) solvent-extraction sample preparation process, for pesticide determination in hemp seed oil. The extraction method was characterized by the absence of both clean-up or pre-concentration steps. The extracts were directly analyzed through cryogenic-modulation comprehensive two-dimensional gas chromatography coupled to triple quadrupole mass spectrometry. The novelty characterizing the present research [compared to a previous one (Arena et al., 2023)] is related to the extension of the number of pesticides (97), and to the investigation of a more challenging matrix, contained in a vegetable oil of increasing interest among consumers. Linearity, limits of detection and quantification, accuracy, precision, recovery, and matrix effect were measured. Particular emphasis was devoted to the matrix effect, with the co-extracted matrix amount defined. Three international regulations (Canada, California, Europe) were considered, and the obtained limits of quantification were found to be too high in five (Canada) and twelve (Europe) cases, for a total number of 15 pesticides. The analysis of ten commercial samples showed the presence of seven pesticide residues in four of them, at concentration levels ranging from 0.02 to 0.98 mg kg−1, with most over the regulation residue limits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".