Quantification and methodology issues in multivariate analysis of ToF-SIMS data for mixed organic systems

• Published in: Surface and interface analysis 2008-01, Vol.40 (1), p.1-14
• Main Authors: Lee, J. L. S., Gilmore, I. S., Seah, M. P.
• Format: Article
• Language: English
• Subjects: Atomic, molecular, and ion beam impact and interactions with surfaces; Condensed matter: electronic structure, electrical, magnetic, and optical properties; Electron and ion emission by liquids and solids; impact phenomena; Exact sciences and technology; image analysis; Impact phenomena (including electron spectra and sputtering); multivariate analysis; multivariate curve resolution; Physics; principal component analysis; quantification; static SIMS
• ISSN: 0142-2421; 1096-9918
• DOI: 10.1002/sia.2713

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.