Analysis of Herbicide and/or Pesticide Residues in Dietary Botanical Supplements
Bibliographic record
Abstract
Abstract Analysis of pesticides in nutraceuticals, particularly dietary botanical supplements, is challenging owing to the low water content of powder or tablet samples and potentially higher concentration of co‐extracts produced during the drying and manufacturing processes. These co‐extracts can cause signal suppression or enhancement in mass spectrometric (MS) detection. Poor chromatographic stability and peak shapes can also be observed from co‐eluting matrix components. Sample preparation is critical to the detection of pesticides in extracts, and the major sample preparation methods used for dietary botanical supplements included modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) methods, dilute‐and‐shoot, and pressurized liquid extraction (PLE) with in‐cell (on‐line) cleanup. Additional cleanup of extracts was completed by dispersive solid‐phase extraction (dSPE) or solid‐phase extraction (SPE). Recoveries of individual pesticides from a variety of chemical classes of varying polarities were evaluated using the major sample preparation approaches utilized for analysis of pesticides since 2010. Major chemical classes of pesticides included those predominately analyzed by GC‐MS/MS (pyrethroid insecticides) or LC‐MS/MS (carbamates, sulfonyl ureas, phenyl ureas, and neonicotinoid insecticides) and those that have greater flexibility to be analyzed by GC‐MS/MS or LC‐MS/MS (azole and strobilurin fungicides). Other selected herbicides including cyclohexene oxime herbicides, aryloxyphenoxy propionic herbicides, or fungicides that were included in multiresidue analysis methods were also examined. Common dSPE sorbents included primary secondary amine (PSA), octyldecyl silane (C18), graphitized carbon black (GCB), and zirconia and C18 bonded to silica (Z‐Sep + ), while carbon‐based sorbents were used for SPE along with Florisil ® . C18 and PSA were also used for SPE often in combination with a carbon‐based sorbent. Highlighted are the issues with different chemical classes or sample matrix types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".