QUANTIFICATION OF HEAVY METALS AND OTHER CONTAMINANTS IN SOME EYE MAKE-UP PRODUCTS USING ICP-OES, XRF AND XRD TECHNIQUES
Bibliographic record
Abstract
This study focuses on the determination, as well as, the composition of some heavy metals contained in 34 eye cosmetic samples. These samples were selected from different shops at Jeddah markets in Saudi Arabia and were manufactured from different countries (China, Saudi Arabia, Italy, Canada and USA). The quantification of selected elements was achieved by ICP-OES Spectrometry for all samples after digestion with concentrated acids HNO3: HClO3 in ratio 4:1. The elemental analysis of heavy metals was performed by X-ray Fluorescence for eyeshadow samples. The composition of some samples was studied by PXRD (Powder X-Ray Diffraction). The overall results of this research revealed that heavy metals present in eye cosmetics products are within acceptable limits while some of those lower price products imported from China can be harmful, and some of them have failed some tests of SASO. The prolonged use of such products can be a potential threat to human health since heavy metals can accumulate in human tissues over time and induce allergic problems. It is highly recommended to control the quality of these products and to aware consumers to be careful when they purchase low price products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".