Statistically rigorous analysis of imaging SIMS data in the presence of detector saturation

• Published in: Surface and interface analysis Vol. 47; no. 9; pp. 889 - 895
• Main Authors: Gelb, Lev D., Bakhtiari, Layla A., Walker, Amy V.
• Format: Journal Article
• Language: English
• Published: Bognor Regis Blackwell Publishing Ltd 01-09-2015; Wiley Subscription Services, Inc
• Subjects: data analysis; Detectors; Imaging; MCR; multivariate analysis; PCA; Reconstruction; Samples; Saturation; Secondary ion mass spectrometry; SIMS; Statistical methods
• ISSN: 0142-2421; 1096-9918
• DOI: 10.1002/sia.5790

We present a new strategy for analyzing imaging time‐of‐flight SIMS data sets affected by detector saturation. Rather than attempt to correct the measured data to remove saturation, we incorporate the detector behavior into the statistical basis of the analysis. This is performed within the framework of maximum a posteriori reconstruction. The proposed approach has several advantages over previous techniques. No approximations are involved other than the assumed model of the detector. The method performs well even when applied to highly saturated and/or single‐scan data sets. It is statistically rigorous, correctly treating the underlying statistical distribution of the data. It is also compatible with Bayesian methods for incorporating prior knowledge about sample properties. An efficient iterative scheme for solving the proposed equations is presented for the case of the bilinear model commonly used in analyses of SIMS data. The correctness of the approach and its efficacy are demonstrated on synthetic data sets. The method is found to perform better than a widely‐used data‐correction method used in combination with alternating‐least‐squares Multivariate Curve Resolution analysis. Copyright © 2015 John Wiley & Sons, Ltd.