Multivariate analysis strategies for processing ToF-SIMS images of biomaterials

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a hyperspectral imaging technique. Each pixel in a two-dimensional ToF-SIMS image (or each voxel in a three-dimensional (3-D) ToF-SIMS image) contains a full mass spectrum. Thus, multivariate analysis methods are being increasingly used to process biomaterial ToF-SIMS images so the maximum amount of information can be extracted from the images. This study examines the use of principal component analysis (PCA) and maximum autocorrelation factors (MAF) on four different ToF-SIMS images. These images were selected because they represent significant challenges for biomedical ToF-SIMS image processing (topographical features, low count rates, surface contaminants, etc.). With PCA four different types of scaling methods (auto, root mean, filter, and shift variance scaling) were used. The effect of two preprocessing methods (normalization and mean centering) was also examined for both PCA and MAF. The more computational intense MAF provided the best results for all the images investigated in this study, doing the best job of reducing the number of variables required to describe the image, enhancing image contrast and recovering key spectral features. MAF was particularly good at identifying subtle features that were often lost in PCA and impossible to visualize in single peak images. However, the combination of PCA with either root mean or shift variance scaling provided similar results to MAF. Thus, these combinations offer promising alternatives to MAF for working with large data sets encountered in 3-D imaging. Also, the new method of filter scaling is promising for processing low count rate images with salt and pepper noise. Normalization proved an important tool for deconvoluting chemical effects from topographic and/or matrix effects. Mean centering aided in reducing the dimensionality of the data, but in one case resulted in a loss of information.