日用化学工业（中英文）2025，Vol.55Issue(10)：1284-1290,7.DOI:10.3969/j.issn.2097-2806.2025.10.008
基于机器学习模型评估化妆品原料眼刺激性
Assessment of eye irritation of cosmetic ingredients based on machine learning
摘要
Abstract
To enhance the predictive performance of computer simulation-based alternative test methods for assessing the eye irritation potential of cosmetic ingredients,this study proposes a machine learning hybrid model that combines ensemble learning algorithms with similarity algorithms.The research collected data from six in vitro alternative test results,such as the fluorescein leakage test and neutral red uptake test,for 84 cosmetic ingredients,as well as data from in vivo test results.The predictive assessment effectiveness of the machine learning hybrid model was validated based on the collected test result data.The final experimental results indicate that the proposed machine learning hybrid model performs well in predicting the irritancy of cosmetic ingredients based on in vitro alternative test result data,with a predictive assessment accuracy rate of 100%in the validation experiment results.Furthermore,to improve the convenience,efficiency,and interpretability of the machine learning hybrid model in practical use,this study further proposes a method for evaluating the eye irritation potential of cosmetic ingredients based on a stratified combination of in vitro alternative tests,which is based on the hybrid model.This method includes a quick reference guide for predictive evaluation generated through computer simulation and a hierarchical combination evaluation strategy derived from experimental data analysis.By integrating the results of in vitro tests conducted under this tiered strategy,it enables the rapid prediction of eye irritation potential of cosmetic ingredients.关键词
眼刺激性/替代方法/机器学习/分层组合评估Key words
eye irritation/alternative methods/machine learning/stratified combination assessment分类
化学化工引用本文复制引用
黄丽霞,刘梓乐,潘丙珍,鲍佳生,乔栖梧,周智明..基于机器学习模型评估化妆品原料眼刺激性[J].日用化学工业（中英文）,2025,55(10):1284-1290,7.基金项目
广州海关科技项目(2022GZCK06) （2022GZCK06）
广东省药品监督管理局化妆品风险评估重点实验室专项(2023ZDZ11) （2023ZDZ11）
广州海关2023年科研项目(2023GZCK07) （2023GZCK07）
