Assessment of heavy metals contamination incommercial cosmetic products / Nur Farhanah Zainuddin
Bibliographic record
Abstract
Atomic Absorption Spectroscopy (AAS) technique was applied to determine and analyse the concentration of heavy metal such as Zn, Pb and Ni in three different skin whitening creams and one herbal cream that available from retail shop in Jengka, Pahang. The samples were digested using acid and then analyzed using AAS. All metals were detected in all samples but with different concentrations. Zinc was range between 6.4 mg/kg and 17.7 mg/kg. Nickel and lead concentration range were lower compared to Zn which were 3.1 mg/kg to 4.2 mg/kg and 6.2 mg/kg to 13.0 mg/kg respectively. The detection of Zn in the whitening creams is under permissible limit as set by FAO I WHO and Health Canada 2007 standard which is 50 mg/kg. Hazard index (HI) values for HI, WI, W2 and W3 were 2.13 x 10-6,4.39 x 10-6 ,3.33 X 10-6 and 3.65 x 10-6 respectively. All values of HI were less than 1 and it is believed that there was no significant risk of non-carcinogenic effects. However, some of the samples contained lead exceeds the permissible limit set by FAO/WHO and Health Canada which is 10 mg/kg. Overall analysis proved that the concentration of heavy metals in herbal cream, HI was lower compared to other whitening creams, WI, W2 and W3. Since the HI values were lower than 1, all products were safe to use. But, it is better to take precaution in using whitening creams since the presence of excess heavy metals may lead to accumulative toxicity in the body beyond the acceptable limit.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".