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

Citation

Wagner, M. S.; Graharn, D. J.; & Castner, D. G. (2006). Simplifying the interpretation of ToF-SIMS spectra and images using careful application of multivariate analysis. Applied Surface Science, 252(19), 6575-6581.

Abstract

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.

Keyword(s)

films
image analysis
ion mass-spectrometry
multivariate analysis
multivariate curve resolution
neural-networks
poisson
principal component analysis
statistical-analysis
tof-sims

Notes

Sp. Iss. SI
085ND
Times Cited:32
Cited References Count:17

Reference Type

Journal Article

Secondary Title

Applied Surface Science

Author(s)

Wagner, M. S.
Graharn, D. J.
Castner, D. G.

Year Published

2006

Date Published

1753833600

Volume Number

252

Issue Number

19

Pages

6575-6581

DOI

DOI 10.1016/j.apsusc.2006.02.073