Natural Is Not Always Better: The Prevalence of Allergenic Ingredients in “Clean” Beauty Products
Bibliographic record
Abstract
BACKGROUND: Consumers seek "clean" and "natural" products based on their perception of safety. However, there is no standard, scientific basis, or regulatory oversight in the marketing or ingredient use for "clean" products. OBJECTIVE: The aim of this study was to determine the prevalence of allergenic ingredients in "clean" products. METHODS: Target and Walgreens Web sites were queried for "clean" products with inclusion of 1470 products. Ingredient lists were analyzed for potential allergens. Analysis included descriptive statistics and χ2 test. RESULTS: The most common allergens were fragrances/botanicals (1218/1470, 82.9%), phenoxyethanol (591/1470, 40.2%), tocopherol (545/1470, 40.2%), benzoic acid and benzoates (434/1470, 29.5%), propylene glycol (369/1470, 25.1%), alkyl glucosides (305/1470, 20.7%), ethylhexylglycerin (304/1470, 20.7%), cetyl alcohol (282/1470, 19.2%), cocamidopropyl betaine (258/1470, 17.6%), and benzyl alcohol (232/1470, 15.8%). Among fragrances/botanicals, the most common ingredients found were fragrance/perfume/aroma (911/1470, 68.2%), citrus derivatives (375/1470, 25.5%), linalool (305/1470, 20.7%), limonene (279/1470, 19.0%), and benzyl alcohol (231/1470, 15.7%). A total of 93.8% of the products (1379/1470) contained at least 1 potential allergen. CONCLUSIONS: Most "clean" products contain a potential allergen, predominately fragrances and botanicals.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".