Quantification of pyrrolizidine alkaloids in North American plants and honey by LC-MS: single laboratory validation
Bibliographic record
Abstract
Pyrrolizidine alkaloids (PAs) are a class of naturally occurring compounds produced by many flowering plants around the World. Their presence as contaminants in food systems has become a significant concern in recent years. For example, PAs are often found as contaminants in honey through pollen transfer. A validated method was developed for the quantification of four pyrrolizidine alkaloids and one pyrrolizidine alkaloid N-oxide in plants and honey grown and produced in British Columbia. The method was optimised for extraction efficiency from the plant materials and then subjected to a single-laboratory validation to assess repeatability, accuracy, selectivity, LOD, LOQ and method linearity. The PA content in plants ranged from1.0 to 307.8 µg/g with repeatability precision between 3.8 and 20.8% RSD. HorRat values were within acceptable limits and ranged from 0.62 to 1.63 for plant material and 0.56-1.82 for honey samples. Method accuracy was determined through spike studies with recoveries ranging from 84.6 to 108.2% from the raw material negative control and from 82.1-106.0 % for the pyrrolizidine alkaloids in corn syrup. Based on the findings in this single-laboratory validation, this method is suitable for the quantitation of lycopsamine, senecionine, senecionine N-oxide, heliosupine and echimidine in common comfrey (Symphytum officinale), tansy ragwort (Senecio jacobaea), blueweed (Echium vulgare) and hound's tongue (Cynoglossum officinale) and for PA quantitation in honey and found that PA contaminants were present at low levels in BC honey.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".