Trace determination of lead in lipsticks and hair dyes using microwave‐assisted dispersive liquid–liquid microextraction and graphite furnace atomic absorption spectrometry
Bibliographic record
Abstract
OBJECTIVE: A novel microwave-assisted dispersive liquid-liquid microextraction (MADLLME) technique according to the solidification of a floating organic droplet (SFO) and graphite furnace atomic absorption spectrometry (GFAAS) used for the extraction and determination of lead ions in lipsticks and hair dyes made in different countries. Lipstick and hair dye samples of different brands and colours were collected from local market in Kermanshah, Iran. METHODS: After sample treatment with microwave-assisted acid digestion, an appropriate mixture of acetone, 1-undecanol and diethyl dithiophosphoric acid was injected rapidly into the aqueous sample containing lead ions, and as a result, cloudy mixture was formed. After centrifugation, the test tube was cooled for few minutes. The solidified 1-undecanol on top of the solution was transferred into a suitable vial and injected into the analytical instrument. RESULTS: Under the optimum experimental conditions (extraction solvent: 30 μL of 1-undecanol; disperser solvent: 500 μL of acetone; ligand concentration: 0.15% (v/v); pH: ~1.5 and without salt added), the enhancement factor of 96 was obtained. The calibration graphs were linear in the range of 0.3-50 μg kg(-1) with a correlation coefficient (r(2) ) more than 0.995. The detection limit was 0.1 μg kg(-1) . Consequently, the developed method was successfully applied to extract and determine lead ions in the lipsticks and hair dyes, and favourable results were obtained. The proposed method which applied in cosmetics showed excellent relative recoveries (90-109.7%) with relative standard deviations <8.3% (n = 3) for all samples. CONCLUSION: The study revealed that the concentration of lead found in lipsticks and hair dyes on the Kermanshah market is far below the recommended limits as applied in Germany (20 mg kg(-1) ) and Canada (10 mg kg(-1) ) and confirmed that very low levels of lead are technically available in the final cosmetic 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".