Analytical survey of tattoo inks—A chemical and legal perspective with focus on sensitizing substances
Bibliographic record
Abstract
BACKGROUND: Tattoo inks have been reported to elicit allergic contact dermatitis. OBJECTIVES: To investigate the labels and the contents of metals and pigments in tattoo inks, considering restrictions within the European Union. METHODS: Seventy-three tattoo inks currently available on the market, either bought or donated (already used), were investigated for trace metals and pigments by inductively coupled plasma mass spectrometry and by matrix-assisted laser desorption/ionization time of flight tandem mass spectrometry. RESULTS: Ninety-three percent of the bought tattoo inks violated European, legal requirements on labeling. Fifty percent of the tattoo inks declared at least one pigment ingredient incorrectly. Sixty-one percent of the inks contained pigments of concern, especially red inks. Iron, aluminium, titanium, and copper (most in green/blue inks) were the main metals detected in the inks. The level of metal impurities exceeded current restriction limits in only a few cases. Total chromium (0.35-139 μg/g) and nickel (0.1-41 μg/g) were found in almost all samples. The levels of iron, chromium, manganese, cobalt, nickel, zinc, lead, and arsenic were found to covary significantly. CONCLUSIONS: To prevent contact allergy and toxic reactions among users it is important for tattoo ink manufacturers to follow the regulations and decrease nickel and chromium impurities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".