Multifactorial Evaluation of Honey from Pakistan: Essential Minerals, Antioxidant Potential, and Toxic Metal Contamination with Relevance to Human Health Risk
Bibliographic record
Abstract
Honey is prized for its nutritional and healing properties, but its quality can be affected by contamination with toxic elements. This study evaluates the nutritional value and health risks of fifteen honey samples from different agro-climatic regions of Pakistan. Physicochemical properties such as color, pH, electrical conductivity, moisture, ash, and solids content were within acceptable ranges. ICP-OES analysis was used to assess six essential minerals and ten toxic metals. Except for slightly elevated boron levels (up to 0.18 mg/kg), all elements were within safe limits, with potassium reaching up to 1018 mg/kg. Human health risk assessments—including Average Daily Dose of Ingestion, Total Hazard Quotient, and Carcinogenic Risk—indicated no carcinogenic threats for adults or children, despite some elevated metal levels. Antioxidant activity, measured through total phenolic content (TPC) and DPPH radical scavenging assays, showed that darker honeys had stronger antioxidant properties. While the overall quality of honey samples was satisfactory, significant variations (p ≤ 0.05) were observed across different regions. These differences are attributed to diverse agro-climatic conditions and production sources. The findings highlight the need for continued monitoring to ensure honey safety and nutritional quality.
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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 | 0.001 | 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".