Supplementary Material for: Screening for More than 1,000 Pesticides and Environmental Contaminants in Cannabis by GC/Q-TOF
Bibliographic record
Abstract
A method has been developed to screen cannabis extracts for more than 1,000 pesticides and environmental pollutants using a gas chromatograph coupled to a high-resolution accurate mass quadrupole time-of-flight mass spectrometer (GC/Q-TOF). An extraction procedure was developed using acetonitrile with solid phase extraction cleanup. Before analysis, extracts were diluted 125:1 with solvent. Two data mining approaches were used together with a retention-time-locked Personal Compound Database and Library (PCDL) containing high-resolution accurate mass spectra for pesticides and other environmental pollutants. (1) A Find-by-Fragments (FbF) software tool extracts several characteristic exact mass ions within a small retention time window where the compound elutes. For each compound in the PCDL, the software evaluates the peak shape and retention time of each ion as well as the monoisotopic exact mass, ion ratios, and other factors to decide if the compound is present or not. (2) A separate approach used Unknowns Analysis (UA) software with a peak-finding algorithm called SureMass to deconvolute peaks in the chromatogram. The accurate mass spectra were searched against the PCDL using spectral matching and retention time as filters. A subset PCDL was generated containing only pesticides that are most likely to be found on foods in the US. With about 250 compounds in the smaller PCDL, there were fewer hits for non-pesticides, and data review was much faster. Organically grown cannabis was used for method development. Twenty-one confiscated cannabis samples were analyzed and ten were found to have no detectable pesticides. The remaining 11 samples had at least one pesticide and one sample had seven detectable residues. Quantitative analysis was run on the confiscated samples for a subset of the pesticides found by screening. Two cannabis samples had residues of carbaryl and malathion that were estimated to be about 10 times greater than the highest US Environmental Protection Agency tolerance set for food and about 4,000 times greater than the Canadian maximum residue limits for dried cannabis flower.
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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) | 0.062 | 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".