The quantification of total lead in lipstick specimens by total reflection X‐ray fluorescence spectrometry
Bibliographic record
Abstract
Lipstick is known to contain lead, and this has been a general area of concern. Methods of quantifying lead in lipstick currently require the use of rather harsh digestion procedures given that lipstick specimens are high in their lipid content and contain many refractory materials. A simple method of performing lead analysis in lipstick specimens based on total reflection X‐ray fluorescence spectrometry (TXRF) is presented here. Samples were prepared by melting lipstick specimens along with a non‐ionic surfactant and an yttrium internal standard followed by homogenization. Solid prepared samples were then finely streaked directly onto a quartz reflector, and TXRF measurements made for 900‐s live time. The method was found to produce a mean limit of detection for lead of 0.04μg/g. Precisions were found to be on the order of 11–38% relative standard deviation (RSD) and apparent recoveries for lead between 92% and 106% ( n = 8). Although the spreading technique may result in thickness variations that may contribute to the higher than expected variances about the determined lead concentrations, the method presented in this work does show promise as a means of performing routine lead analysis in lipstick specimens without the need for harsh digestion procedures. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".