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Development of a Predictive Classification Model for Surfactant-Induced Skin Irritation
Manuela Lechuga1, Pedro A García2, Ana I García-López1
1Department of Chemical Engineering, Faculty of Sciences, University of Granada, Campus Fuente Nueva S/N, Granada 18071, Spain.
ACS Omega
|December 1, 2025
Summary
This study identifies key chemical properties influencing surfactant skin irritation. Hydrophilic-lipophilic balance (HLB) is the primary factor, guiding the development of safer surfactant products.
Area of Science:
- Cosmetic Science
- Toxicology
- Computational Chemistry
Background:
- Surfactants are widely used in consumer products.
- Skin irritation is a significant concern for surfactant safety.
- Predictive models are needed to assess surfactant-induced irritation.
Purpose of the Study:
- To investigate chemical properties influencing surfactant skin irritability.
- To develop predictive classification models for skin irritation.
- To identify key predictors of surfactant-induced skin irritation.
Main Methods:
- Utilized multiple linear regression and conditional inference trees.
- Generated a dataset of irritation values (Zein number, ZN) for 20 surfactants and mixtures.
- Evaluated hydrophilic-lipophilic balance (HLB), concentration, and ionic character.
Main Results:
- Multiple regression model explained 80% of skin irritation variability (adjusted R² = 0.801).
- Classification tree achieved 72% accuracy, with 0.70 precision and 0.68 recall.
- HLB was the most significant predictor, followed by concentration and ionic character.
Conclusions:
- Surfactant chemical properties, particularly HLB, hierarchically influence skin irritation.
- Anionic and nonionic surfactant mixtures exhibit reduced irritation potential.
- Findings aid in formulating safer surfactant-based products.
