Comparative Analysis of the Heavy Metals Content in Selected Colored Cosmetic Products at Saudi Market
Bibliographic record
Abstract
Heavy metal impurities in cosmetics are common due to their natural abundance. However, they should be kept to a minimum wherever technically feasible. Although human external contact with a substance rarely results in a significant systemic exposure, local exposure to cosmetics may pose a risk of heavy metal contamination. In this study, we sought to investigate the heavy metal concentration present in various cosmetic products from different brands and qualities that are available in the Saudi Market, also to analyze and compare the determined values relative to the reported permissible levels according to international standards. In this study, we have selected several facial cosmetics from the Saudi market and classified their quality into three main classifications based on their price. This was followed by an analysis and reporting of heavy metal content using an inductively coupled plasma-mass spectrometer. We found that three metals were below the permissible limits (Pb, As, and Cd) for cosmetics according to the Saudi Food and Drug Administration and Canadian Standards, besides (Cr) which was also below the limit of the United States Food and Drug Administration. The level of (Ni) exceeded the recommended range in the three-class classifications. On contrary, Pb, Cr, As, and Cd have all exceeded the acceptable levels based on European standards. Further assessment and careful selection of heavy metals content in cosmetics are urgently needed, as there are fluctuations in values between different international standards which might pose a potential harmful effect to human health from the daily use of cosmetics containing heavy metals impurities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".