Volatility of Fragrance Chemicals: Patch Testing Implications
Bibliographic record
Abstract
BACKGROUND: Diagnostic and predictive patch testing to determine contact allergy due to fragrance materials requires applying a fixed dose of material to the skin. This dose can be affected by the volatile nature of fragrances; little data exist on how the loss of fragrance dose due to volatility affects patch testing. OBJECTIVE: (1) To evaluate pH dependence and evaporation rates of two fragrance chemicals, geraniol, citronellol, and a common fragrance solvent, diethyl phthalate (DEP) and (2) Assess implications for predictive patch-testing methods for fragrances. METHOD: pH analysis of each material at 1% for three values (4.0, 5.0, 7.0) was done over 40 hours. Volatility experiments for each material, nonradiolabeled and radiolabeled, were conducted over a 24-hour period, taking readings at six time points (5 minutes, 15 minutes, 40 minutes, 1 hour, 3 hours, and 24 hours). RESULTS: Evaporation rates were not sensitive to pH shifts from 4.0 to 7.0. Evaporation rates for nonradiolabeled materials were low: after 24 hours, geraniol lost 8.9%, citronellol 27.0% and DEP 14.5%. The volatility data for radiolabeled materials demonstrated that geraniol loses up to 39% of its dose, citronellol loses up to 26%, and DEP up to 14% within 40 minutes. CONCLUSION: The tendency of fragrance materials to evaporate can impact the dose being applied to the patch and therefore the result of the patch and ultimately the decision-making process regarding that fragrance material's safety. These data, developed with DEP, utilized in a predictive sensitization assay cannot be generalized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".