A LC-MS/MS method with electrospray ionization and atmospheric pressure chemical ionization source for analysis of pesticides in hemp
Bibliographic record
Abstract
BACKGROUND: Pesticide testing for hemp has traditionally focused on techniques like QuEChERS with dSPE and SPE which demand time-consuming sample preparation, typically resulting in poor recovery rates for some pesticides, and requires the use of both LC-MS/MS and GC-MS/MS based instruments to cover the analysis for all regulated pesticides. In this study, we describe a streamlined approach for working with LC-MS/MS featuring a dual electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources using solvent extraction for faster and easier sample preparation and with 80-120% recovery for the analysis of all of 66 pesticides (regulated by California state in cannabis) with low detection limits in hemp. METHODS: A simple solvent extraction with acetonitrile was used to extract pesticides from hemp. A LC-MS/MS system with dual ESI and APCI source was used to determine sensitivity for the analysis of 66 pesticides in hemp matrix, 62 pesticides were analyzed using an 18-min LC-MS/MS method with an ESI source and the other 4 pesticides were measured using a 6-min LC-MS/MS method with an APCI source. RESULTS: The limit of quantitation (LOQ) of all 66 pesticides in hemp was in the range of 0.0025-0.1 μg/g which was well below the California state action limits of these analytes in cannabis products. A simple, fast, and cost-effective solvent extraction method was used for sample preparation to get good recovery in the range of 80-120% with RSD less than 20%. The unique ionization mechanism of chlorinated pesticides such as pentachloronitrobenzene using the LC-MS/MS system with an APCI source was elucidated. The proficiency test report generated with the LC-MS/MS method showed acceptable results for all of 66 pesticides in hemp with all of th z scores less than 2 with no false positives and negatives. The stability data collected over 5 days showed RSD less than 20% for 66 pesticides in hemp, and this demonstrated the robustness of the LC-MS/MS system used in this work. CONCLUSIONS: A LC-MS/MS method with dual ESI and APCI sources was developed for the analysis of 66 pesticides in hemp. The recovery of all pesticides from a hemp matrix was in the acceptable range of 80-120% with RSD less than 20%.
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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) | 0.002 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".