交叉点规范化和主要组件分析合并方法,利用时间依赖的材料数据对特征进行分类
Makoto Furukawa1, Yasuhiro Niida2, Kyoko Kobayashi2
1PerkinElmer Japan G.K., 134 Godo, Hodogaya, Yokohama, Kanagawa, 240-0005, Japan. makoto.furukawa@perkinelmer.com.
概括
一种新的PCA-合并方法通过分析时间依赖性质来分类油漆. 这种技术使用角规范化和重心转移来准确地描述材料,而无需广泛的峰值识别.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 描述时间依赖的涂料特性对于理解材料进化至关重要.
- 传统的方法与来自多种分析技术的各种数据维度作斗争.
- 准确的分类需要整合静态和动态材料信息的方法.
研究的目的:
- 开发一种新的多变量分析技术,用于对具有时间依赖性质的油漆进行分类.
- 引入一种"PCA-合并"方法,将不同时间组件的主要组件分析集成在一起.
- 为了能够同时分析静态和动态涂料特性.
主要方法:
- 在干燥 (1-48小时) 期间使用FTIR,ICP-MS和HS-GC/MS对油漆进行全面的表征.
- 使用角度参数 (θ) 的数据规范化,通过对角变换来规范强度和时间变量.
- 开发和应用"PCA-合并"方法来分析合并的主要组件分析数据组.
主要成果:
- 交叉端正常化有效地减少了不同数据强度和分析仪器差异的影响.
- 对规范化数据的多变量分析使得涂料样本能够成功地被分类为类别.
- 通过PCA-merge方法,通过利用PCA得分图的重心转移,整合时间依赖的数据,成功地区分了样本.
结论:
- 拟议的"PCA-合并"方法提供了一个强大的方法来分类时间依赖的材料,如油漆.
- 这种技术可以同时分析静态和动态特性,提供更深入的材料见解.
- 该方法通过利用PCA分数中的重心位移来增强涂料特征,从而最大限度地减少了对详细峰值识别的需求.
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