Determination of Organochlorine Pesticides' Content in Dried Medicinal Plants and Their Infusions—Risk to Human Health
Bibliographic record
Abstract
Abstract The study investigates the organochlorinated pesticides (OPCs) content in eight medicinal plant species (Achillea millefolium, Artemisia absinthum , Hypericum perforatum , Tilia sp., Matricaria chamomilla , Taraxacum officinale, Calendula officinalis, Crataegus monogyna), purchased from pharmacies, as well as in infusions prepared from these plants. A total number of 24 samples were analyzed by gas chromatography—electron capture detector (GC-ECD). The transfer rate of OPCs from dried herbs to infusions, as well as the health risk of OPCs residues in infusions (estimated daily intake (EDI) and Hazard Index (HI) were calculated. All medicinal plants (MPs) contained pesticides in different concentrations. For the compounds Heptachlor, p,p' DDT, p,p' DDD, Aldrin, Dieldrin, concentrations well above the maximum residue limits (MRLs) were obtained. The transfer rate of organochlorine pesticides from dried plant to infusion ranged from 3.95% for Heptachlor (Artemisia absinthium) to 57.71% for Dieldrin (Achillea millefolium). The correlation between the OCPs content in the MPs and their infusion are statistically analyzed using the t-test for independence in high dimension, based on the correlation distance. Significant result was found for all OCP detected in the two media, with the content in the MPs significantly correlated with the content in the infusion. The EDI of the detected pesticides was lower than the Acceptable Daily Intake (ADI), and the Hazard Index values were less than 1 (HI < 1) in all infusions. Also, the cumulative hazard index (CHI), showed values less than 1 (CHI < 1) for all types of tea. These results indicate that residues of OCPs in the analysed infusions pose minimal risk to human health. However, taking into account the large transfer variations obtained, the results of the study recommend the development of control protocols for herbal infusions intended for human therapy that include the measurement of OCPs concentrations.
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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 | 0.000 |
| 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".