Isothiazolinone Content of US Consumer Adhesives: Ultrahigh-Performance Liquid Chromatographic Mass Spectrometry Analysis
Bibliographic record
Abstract
BACKGROUND: There are limited data regarding the prevalence and concentration of isothiazolinone preservatives in consumer adhesives. OBJECTIVES: The aim of this study was to determine the prevalence and concentration of 5 specific isothiazolinones (methylisothiazolinone [MI], methylchloroisothiazolinone [MCI], benzisothiazolinone [BIT], butyl BIT, and octylisothiazolinone) in US adhesives. METHODS: Thirty-eight consumer adhesives were analyzed using ultrahigh-performance liquid chromatographic-mass spectrometry. Fisher exact tests were used to test for isothiazolinone content and: 1) glue format (2) application purpose and 3) extraction method. RESULTS: Nineteen adhesives (50%) had at least 1 isothiazolinone, and 15 contained 2 isothiazolinones. Frequencies and concentrations were as follows: MI (44.7%; 4-133 ppm), MCI (31.6%; 7-27 ppm), BIT (15.8%; 10-86 ppm), and octylisothiazolinone (2.6%; 1 ppm). Butyl BIT was not detected in any of the adhesives. Format (stick vs liquid) was not statistically associated with isothiazolinone presence. At least half of adhesives in the following application purposes had at least 1 isothiazolinone: shoe, craft, fabric, and school. All-purpose glues had a statistically significant lower concentration of MI and MCI, whereas craft glues were associated with higher concentrations of MI and MCI. Compared with other glues, fabric adhesives were associated with a higher risk of containing BIT. CONCLUSIONS: Half of the tested adhesives contained at least 1 isothiazolinone. Methylisothiazolinone and MCI were the most common. Consumers and dermatologists should be aware of adhesives as a source of isothiazolinones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 teacher head, 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".