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Record W3189341243

Assessment of heavy metals contamination incommercial cosmetic products / Nur Farhanah Zainuddin

2017· article· en· W3189341243 on OpenAlexaboutno aff
Nur Farhanah Zainuddin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicHeavy Metals in Plants
Canadian institutionsnot available
Fundersnot available
KeywordsAtomic absorption spectroscopyChemistryHeavy metalsZincContaminationHealth riskHealth hazardEnvironmental chemistryMetalDetection limitNuclear chemistryChromatographyMedicine
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.349
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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