Quantitative Analysis of Potentially Toxic Elements (Pb, Cd, Co, Cr, Fe, Mn, and Ni) in Lipstick Brands Available in Mekelle Markets, Tigray, Ethiopia
Bibliographic record
Abstract
Lipsticks, widely used cosmetic products, can be a significant source of potentially toxic elements exposure through unintentional ingestion. This poses potential long-term health risks due to the bioaccumulative nature of metals like Pb, Cd, and Cr. This study aimed to quantify the concentrations of seven potentially toxic elements (Pb, Cd, Co, Cr, Fe, Mn, and Ni) in three widely available and commonly sold lipstick brands from local markets in Mekelle, Ethiopia. Samples were prepared using a wet acid digestion method with a concentrated 5:1:1 mixture of HNO3, HCl, and H2O2 and analyzed through Flame Atomic Absorption Spectrophotometer (FAAS). Cd was not detected in any sample. The concentration ranges across all samples in ppm were: Pb (0.0400–0.1000), Co (3.740–5.570), Cr (10.62–18.05), Fe (75.00–1838), Mn (0.1500–0.1600), and Ni (12.83–51.41). The levels of Pb were well below the 10 ppm guidance limit set by international bodies like Health Canada, U.S. Food and Drug Administration (FDA), their consistent presence is a concern. However, concentrations of Cr and Ni, known sensitizers, were notable. As an intentionally added pigment, Fe showed the highest and most variable concentrations. Although the detected levels of toxic metals were within international permissible limits, their consistent presence is a public health concern. Given the direct ingestion route and frequency of application, daily use may contribute to the cumulative body burden of these metals over time. This study, though limited by a small sample size, highlights the urgent need for healthy regulatory oversight and routine quality control of cosmetics in Ethiopia. Further research is essential to assess a wider range of products and evaluate the associated health risks for consumers.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".