Determination of Polycyclic Aromatic Hydrocarbons (PAHs) Transfer from Dried Medicinal Plants in Infusions for Therapeutic Purposes
Bibliographic record
Abstract
The purpose of this paper is to determine the level of polycyclic aromatic hydrocarbons (PAHs) from medicinal plants and infusions prepared using them, as well as assess the transfer of these contaminants from plants to infusions. The separation of compounds was achieved using microwave extraction for dried plants and liquid–liquid extraction in the case of infusions. The extracts were cleaned using solid-phase extraction, and the compounds were analysed using gas chromatography coupled with mass spectrometry (GC-MS). Exposure to PAHs through tea infusion consumption was evaluated by calculating the estimated daily intake (EDI, ng/kg.bw/day) and Margin of Exposure (MOE). The average total content of PAHs varied from 277.22 ± 12.78 to 2466.46 ± 203.45 µg/kg in dry plants, and the compounds benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP) were present in all samples. In the herbal tea infusions, the average total PAH content varied between 612.55 ± 46.12 ng/L and 2292.2 ± 140.24 ng/L. The observation was statistically checked using a two-sample paired test. The analysis revealed that PAHs could be split into those for which the content in the medicinal plants is significantly larger than in the infusions and those for which the difference is not significant. The average transfer rates of ∑16PAHs from plants to infusions varied from 7.25 to 32.86%. The MOE values confirmed that consumer exposure to PAHs via tea infusions is very low and safe for health.
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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 | 0.000 | 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 | 0.001 |
| 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".