Principal component analysis of TOF-SIMS images of organic monolayers
Principal component analysis of TOF-SIMS images of organic monolayers
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DOI：
10.1021/ac020311n
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发表时间：
2002-11-15
影响因子：
7.4
通讯作者：
Petersent, NO
中科院分区：
文献类型：
--
作者：
Biesinger, MC;Paepegaey, PY;Petersent, NO
主成分分析（PCA）是一种统计方法，用于寻找描述数据中最重要趋势的变量或因子的组合。PCA已与飞行时间二次离子质谱（TOF - SIMS）数据相结合，以提取新信息并找出复杂系统中所含物质之间的关系。讨论了使用朗缪尔 - 布洛杰特技术制备的单独的二棕榈酰磷脂酰胆碱单分子层以及与棕榈酰油酰磷脂酰甘油混合的单分子层。PCA软件为每个显著的主成分提供图像得分和相应的载荷。得分的图像图显示了由载荷图（质谱特征）所定义的物质的空间分布和强度。图像得分的强度和分辨率可导致比常规TOF - SIMS图像有显著的改进，特别是当在小分析区域使用静态条件时。此外，图像中地形和基质的一些影响可以被消除，从而能够更好地呈现化学变化。
Principal component analysis (PCA) is a statistical method used to find combinations of variables or factors that describe the most important trends in the data. PCA has been combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) data to extract new information and find relations between species contained in complex systems. Monolayers of dipalmitoylphosphatidylcholine alone and mixed with palmitoyloleoylphosphatidylglycerol prepared using the Langmuir- Blodgett technique are discussed. PCA software provides image scores and corresponding loadings for each significant principal component. Image plots of the scores show the spatial distribution and intensity of the species defined by the loading plots (mass spectral features). The intensity and resolution of the image scores can result in substantial improvement over that of the regular TOF-SIMS images especially when static conditions are used for small analysis areas. Also, some of the effects of topography and matrix in the images can be removed, allowing for a better presentation of chemical variations.