Multivariate analysis of ToF-SIMS data from multicomponent systems: the why, when, and how.
Daniel J Graham1, David G Castner
1Department of Bioengineering, National ESCA and Surface Analysis for Biomedical Problems, University of Washington, Seattle, WA 98195-1653, USA. djgraham@uw.edu
Biointerphases
|August 16, 2012
Summary
Multivariate analysis (MVA) enhances processing of complex time-of-flight secondary ion mass spectrometry (ToF-SIMS) data. Principal component analysis (PCA) and other MVA methods help identify compounds and differences in multicomponent surfaces.
Area of Science:
- Analytical Chemistry
- Surface Science
- Spectroscopy
Background:
- Time-of-flight secondary ion mass spectrometry (ToF-SIMS) generates complex data from multicomponent surfaces.
- Biological materials and biosensors often present challenges due to their intricate composition.
Purpose of the Study:
- To discuss the application of multivariate analysis (MVA) methods for processing ToF-SIMS data.
- To provide guidelines for using MVA, particularly principal component analysis (PCA), on complex ToF-SIMS datasets.
Main Methods:
- Application of multivariate analysis (MVA) techniques.
- Focus on principal component analysis (PCA) as a primary tool.
- Analysis of multicomponent ToF-SIMS data.
Main Results:
- MVA methods are increasingly utilized for ToF-SIMS data processing.
- PCA is a common and effective starting point for MVA in ToF-SIMS.
- MVA aids in identifying compound distributions and sources of variation in complex samples.
Conclusions:
- MVA provides powerful tools for interpreting complex ToF-SIMS data.
- Proper application of MVA, including PCA, is crucial for understanding multicomponent surfaces.
- Guidelines are provided for effective MVA implementation in ToF-SIMS analysis.
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