Biesinger Mark C, Paepegaey Pierre-yves, McIntyre N Stewart, Harbottle Robert R, Petersen Nils O
Surface Science Western and Department of Chemistry, The University of Western Ontario, London, Ontario, Canada N6A 5B7.
Anal Chem. 2002 Nov 15;74(22):5711-6. doi: 10.1021/ac020311n.
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.
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素组合。PCA已与飞行时间二次离子质谱（TOF-SIMS）数据相结合，以提取新信息并找到复杂系统中所含物种之间的关系。本文讨论了单独的二棕榈酰磷脂酰胆碱以及与棕榈酰油酰磷脂酰甘油混合使用朗缪尔-布洛杰特技术制备的单层膜。PCA软件为每个重要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）定义的物种的空间分布和强度。图像得分的强度和分辨率相比常规TOF-SIMS图像可得到显著改善，特别是在对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消除，从而更好地呈现化学变化。
