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Simplifying the interpretation of ToF-SIMS spectra and images using careful application of multivariate analysis

机译：仔细应用多元分析，简化了ToF-SIMS光谱和图像的解释

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As analytical problems addressed using time-of-flight secondary ion mass spectrometry (ToF-SIMS) increase in chemical complexity, multivariate analysis (MVA) methods have become standard tools for simplifying the interpretation of ToF-SIMS spectra and images. MVA methods can significantly simplify ToF-SIMS datasets by providing a comprehensive description of the data using a small number of variables, typically in an automated fashion requiring minimal user intervention. However, successful and widespread application of MVA methods to SIMS data analysis is limited by a lack of understanding of the outputs of MVA methods and optimization of these methods for ToF-SIMS data analysis. Appropriate selection of data pre-processing and MVA tools are critical for accurate interpretation of ToF-SIMS spectra and images. As an example, an image dataset of a selectively ion-etched polymer film was analyzed to identify and characterize the chemically distinct regions in the image. Principal component analysis (PCA) and multivariate curve resolution (MCR) after pre-processing using normalization or Poisson-scaling were compared to identify the etched and non-etched regions of the image. The utility of each pre-processing and MVA method was examined, with MCR coupled with Poisson-scaling being the appropriate choice for identifying the different chemical phases present in the image. However, appropriate selection of data pre-processing and MVA methods generally depends on the specific dataset being analyzed and the goals of the analysis. (c) 2006 Elsevier B.V. All rights reserved.
机译：随着使用飞行时间二次离子质谱（ToF-SIMS）解决的分析问题的化学复杂性增加，多变量分析（MVA）方法已成为简化ToF-SIMS光谱和图像解释的标准工具。 MVA方法通过使用少量变量来提供数据的全面描述，从而可以大大简化ToF-SIMS数据集，通常以自动化的方式，只需最少的用户干预即可。但是，由于缺乏对MVA方法输出的理解以及对ToF-SIMS数据分析的这些方法的优化，MVA方法在SIMS数据分析中的成功和广泛应用受到了限制。正确选择数据预处理和MVA工具对于准确解释ToF-SIMS光谱和图像至关重要。例如，分析了选择性离子蚀刻的聚合物膜的图像数据集，以识别和表征图像中化学上不同的区域。比较了使用归一化或泊松缩放进行预处理后的主成分分析（PCA）和多变量曲线分辨率（MCR），以识别图像的蚀刻区域和非蚀刻区域。检查了每种预处理和MVA方法的效用，将MCR与泊松缩放相结合是识别图像中存在的不同化学相的适当选择。但是，适当选择数据预处理和MVA方法通常取决于要分析的特定数据集和分析目标。 （c）2006 Elsevier B.V.保留所有权利。

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