Evaluation of Harmful Substances and Health Risk Assessment of Mercury and Arsenic in Cosmetic Brands in Nigeria
Bibliographic record
Abstract
Forty two different cosmetic samples consisting of 16 facial cosmetics, 6 soaps, 1 shower gel, 12 emulsions, 2 underarm cosmetics, 3 nail cosmetics and 2 perfumes were purchased from department stores and cosmetic shops within Onitsha Main Market and Eke-Awka in Anambra, Nigeria. Seven of these cosmetic (16.67%) were locally manufactured in Nigeria while thirty five (83.33%) were imported into Nigeria. The cosmetics were ashed before digestion and filtration. The filtrates were assayed for mercury and arsenic with AAS SearchTech AA320N. Hydroquinone presence was identified by chromatographic test while steroids, nitrite and N-nitrosamines were identified by colour test and together were assayed by UV-spectrophotometer (Spectrulab 21). The health risk assessment methods developed by the United States Environmental Protection Agency (US EPA) were employed to explore the potential human health risk of Mercury and Arsenic in cosmeticsamples. Results showed that two (2) of the cosmetic samples contained mercury ( 0.003 + 0.000mg/kg and 0.07 + 0.00mg/kg) while three cosmetic samples contained arsenic (0.002 + 0.000, 0.002+0.000 and 0.005 +0.000 mg/kg). Hydroquinone concentration ranged from 1.14 + 0.00 – 1.83 + 0.03 mg/kg (1.14E-02 – 1.83E-02 %).Steroid was found in only two samples with concentration of 16.70 + 0.74 mg/kg and 17.63 + 0.74 while N-nitrosamines and nitrite occurred in nine and eleven samples in the range of 4.66 + 0.09 – 43.52 + 0.47 and 0.87 + 0.02 – 13.42 + 2.90 respectively. The total cancer and non-cancer risk results indicated that although the chances of cancer risk and non-cancer risk resulting from the use of these cosmetic products were unlikely, build up of these heavy metals overtime on continuous usage could be detrimental.
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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".