Multivariate image analysis strategies for ToF-SIMS images with topography
Multivariate image analysis strategies for ToF-SIMS images with topography
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DOI：
10.1002/sia.3070
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发表时间：
2009-08-01
影响因子：
1.7
通讯作者：
Seah, M. P.
中科院分区：
文献类型：
--
作者：
Lee, J. L. S.;Gilmore, I. S.;Seah, M. P.
尽管多变量分析方法具有诸多优点，但其在与工业相关的现实样本中的稳健应用仍然存在许多挑战。在这里，我们使用经过多组分配方预处理的头发纤维来研究实际分析中获得的复杂飞行时间二次离子质谱 (ToF-SIMS) 图像的不同多元分析策略。由于极端的地形、大量未知的化学成分和探测器饱和，这是一项具有挑战性的工作。我们比较了主成分分析 (PCA) 和多元曲线分辨率 (MCR) 的结果，无缩放、泊松缩放和二项式缩放。由于地形条件恶劣，缩放方法被修改为仅在谱域中操作。我们建议使用最大离子强度谱来突出局部化学特征并诊断探测器饱和度。事实证明，采用适当的数据缩放比例进行死区时间校正对于检测微小的局部化学变化至关重要。虽然 PCA 结果难以解释，但 MCR 结果直接类似于二次离子质谱 (SIMS) 谱图和分布。在检测多种成分之间的重要相互作用方面，MCR 也优于手动分析。然而，与 PCA 不同的是，不同 MCR 因素获得的分数和载荷是相关的。详细讨论了独立化学特征的最佳分辨率的结果。由于探测器饱和，二项式缩放被认为是该图像最合适的数据缩放方法。这项研究为复杂的 ToF-SIMS 图像提供了强大的分析策略，这对于日益复杂的多有机表面和生物材料至关重要。 (C) 皇家版权 2009。经女王陛下文具办公室许可复制。由约翰·威利父子有限公司出版
Despite the benefits of multivariate analysis methods, many challenges remain with their robust applications to real-life samples relevant to industry. Here, we use hair fibres pre-treated with a multi-component formulation to investigate different multivariate analysis strategies for complex time-of-flight secondary ion mass spectrometry (ToF-SIMS) images obtained in practical analysis. This is challenging because of extreme topography, a large number of unknown chemical components and detector saturation. We compare results from principal component analysis (PCA) and multivariate curve resolution (MCR) with no scaling, Poisson scaling and binomial scaling. Because of severe topography, scaling methods are modified to operate in the spectral domain only. We propose the use of a maximum ion intensity spectrum to highlight localised chemical features and diagnose detector saturation. Dead time correction with suitable data scaling is demonstrated to be essential for the detection of small, localised chemical variations. While PCA results are difficult to interpret, MCR results resemble secondary ion mass spectrometry (SIMS) spectra and distributions directly. MCR is also superior to manual analysis for the detection of an important interaction between multiple ingredients. However, unlike PCA, the scores and loadings obtained on different MCR factors are correlated. The consequence of this for the optimal resolution of independent chemical features is discussed in detail. Binomial scaling is identified as the most appropriate data scaling method for this image due to detector saturation. This study provides a robust analysis strategy for complex ToF-SIMS images, essential for increasingly complex multi-organic surfaces and biomaterials. (C) Crown copyright 2009. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.