Targeted and non-targeted screening of flame retardants in rural and urban honey
Bibliographic record
Abstract
Flame retardants (FRs) are often added to commercial products to achieve flammability resistance, but they are not chemically bonded to the materials, so, they can be easily released into the environment during the production and disposal processes. When honeybees travel to collect nectar during the pollination process, they are prone to be contaminated by chemicals in the air. Therefore, honey contamination has been proposed as an indicator of the pollution status in a particular region. To date, the occurrence of flame retardants in urban honey has yet to be explored. In this study, a direct injection method was used, coupled with LC-QTOF-MS, to analyze honey samples. This method was applied to urban ( n = 100) and rural ( n = 100) honey samples from the Quebec province (Canada), and the levels of flame retardants in urban and rural honey samples were not significantly different. In the targeted approach, two of the target FRs, tris(2-butoxyethyl) phosphate (TBOEP) and triphenyl phosphate (TPHP), were detected and confirmed at an average trace concentration (<1 ng mL −1 ). Additionally, a non-targeted screening workflow with an in-house-built library was developed and validated to screen for flame retardants in honey. Tris (2-chloropropyl) phosphate (TCIPP) was identified in honey using the non-targeted screening workflow and confirmed using a pure analytical standard, but there are other compounds detected in the non-targeted analysis that have yet to be validated. This study was the first to report FR compounds based on a direct injection method, coupled with a non-targeted screening workflow, at a trace level in a honey matrix. It also showed that a non-targeted workflow was effective to detect and identify unknown compounds present in the honey sample; hence, this provided a novel angle for the occurrence of FRs in air, with honey as a bio-indicator.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".