PEER-REVIEWED ARTICLE · 同行评议论文
拉曼成像结合近邻算法评估皮肤屏障损伤
K-Nearest Neighbour-based classification of skin barrier damage using Raman imaging: A non-invasive assessment method for skin damage severity输入索引记载：研究通过胶带剥脱和摩擦建立屏障损伤模型，利用拉曼成像分析角质层厚度、脂质含量与角蛋白信号，并输入K近邻分类算法。该方法在受控损伤模型中能够区分正常至较重损伤的不同状态，并对未知样本进行分类。
护肤研发启示
该索引记载的方法为屏障护理研究增加了结构、脂质和蛋白层面的观察手段，可与经皮水分流失等指标互补。
The supplied index reports: Tape stripping and friction were used to establish controlled barrier-damage models; Raman imaging measured stratum corneum thickness, lipid content and keratin signals, which were input into a K-nearest neighbour classifier. The method distinguished normal to more severe damage and classified unknown samples under the tested conditions.
Relevance to skincare research
As reported by the index, the approach adds structural, lipid and protein endpoints to barrier-care assessment, complementing measures such as transepidermal water loss.
STUDY INFORMATION
研究信息
实验范围与结果
- 输入索引记载，未取得原始全文。
- 损伤模型由胶带剥脱和摩擦建立，不能直接等同于敏感皮肤或皮肤疾病。
- 研究仅评价损伤分类方法，未报告具体修护产品的使用效果。
PUBLICATION & FULL TEXT
PDF 与出版来源
PUBLICATION METADATA · 出版信息
论文身份与引用信息。
- 原文题名
- K-Nearest Neighbour-based classification of skin barrier damage using Raman imaging: A non-invasive assessment method for skin damage severity
- 作者
- Feng Zhong · Qiaochu Du · Wenrou Su · Feifei Wang
- DOI
- 10.1111/ics.70033
- ISSN
- 发表日期
- 2025-10-20
- 期刊
- International journal of cosmetic science
- 卷 / 期 / 文章号
- 48 / 2 /
- 出版社
- 关键词