Multivariate Statistical Analysis of Cosmetics Due to Potentially Toxic/Heavy Metal(loid) Contamination: Source Identification for Sustainability and Human Health Risk Assessment
Bibliographic record
Abstract
Cosmetics are a staple in daily beauty routines worldwide, embraced for their role in enhancing personal appearance and self-care. However, their direct application exposes the skin to a range of ingredients, including metal(loid)s, and these can have a hazardous impact on human health if they exceed the limits defined by regulatory agencies. As a result, it is important to evaluate metal(loid) levels to ensure the safety of these cosmetic products. This research aimed to analyze several cosmetic products (massage cream, cleaner, mud mask, skin polish, scrub, lipstick, foundation, lotion, face powder, and highlighter) for metalloids (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, As, Sb, Cd, Pb, Bi, and Hg) by using inductively coupled plasma-optical emission spectrometry (ICP-OES). Multivariate and bivariate statistical approaches were used to interpret the sources and association between the metal(loid)s. The concentration ranges of metals were found to be Al (1.18–4.15 mg/kg), Cr (0.13–2.19 mg/kg), Mn (1.89–28.77 mg/kg), Fe (4.09–126.02 mg/kg), Co (0.19–31.62 mg/kg), Ni (0.11–5.39 mg/kg), Cu (0.08–0.19 mg/kg), Zn (1.99–47.81 mg/kg), Sr (0.99–10.34 mg/kg), Cd (0.16–1.50 mg/kg), Pb (0.29–2.44 mg/kg), Bi (0.23–2.18 mg/kg), and Hg (0.012–0.42 mg/kg), whereas the concentration ranges of metalloids were found to be As (0.011–0.051 mg/kg) and Sb (0.042–1.66 mg/kg). Among the tested cosmetic products for the metal(loid) levels, the estimation of the margin of safety (MOS < 100), hazard quotient (HQ > 1), hazard index (HI > 1), and cumulative cancer risk (LCR ≤ 1 × 10−3) indicated non-carcinogenic and carcinogenic health risks associated with the use of few products. Most of the cosmetics products under investigation are safe with respect to non-carcinogenic and carcinogenic health risks. By highlighting the need to limit excessive use and adhering to sustainable safety practices, this study underscores the importance of monitoring and regulating metal(loid) content to ensure consumer safety and mitigate environmental impact.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".