Trace Metal Determination in Herbal Plants by Acid Digestion From Jeddah Market in Saudi Arabia
Bibliographic record
Abstract
The world is facing a serious issue with plants contaminated by trace metals. Therefore, a consideration is required due to its danger that impacts both humans and animals. Herbs are extensively used worldwide for their seasoning and therapeutic properties. This study aimed to estimate the level of trace metals (Fe, Zn, Pb, Cr, Cu, Ni) in selected customary herbs consumed in Saudi Arabia. The 5 samples of herbs were purchased from a local market in Jeddah City (Mint (Mentha), Basil (Ocimum), Arugula (Eruca sativa), Coriander (Coriandrum sativum) and Parsley (Petroselinum crispum)). Acid digestion was applied to the plant leaves and trace metals concentrations were determined using Inductively Coupled Plasma - Optical Emission Spectroscopy (ICP-OES). Metals were observed to be available in varied concentrations in the herb plant samples. The highest metal values, especially in Arugula (ES) 218.3±1.9 mg/kg and 24.4±0.09 mg/kg for Zn and Ni respectively, Cr was under detection limit, Coriander (CS) 148.5±1.8 mg/kg and 17.3±0.07 mg/kg for Fe and Pb respectively, Mint (ME) 28.6±0.26 mg/kg for Cu, while Basil (OC) was recorded below the (WHO) permissible limits 18.9± 0.06 mg/kg and 1.1± 0.003 mg/kg for Zn and Cr respectively, besides all metals were higher than the (WHO) allowed limit in Parsley (PC). The study found that most of the examined herbs contained hazardous levels of trace metals that exceeded the World Health Organization (WHO) permissible limits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.004 | 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 teacher head, 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".