Visualizing ToF-SIMS hyperspectral imaging data using color-tagged toroidal self-organizing maps

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface characterization technique capable of producing high spatial resolution hyperspectral images, in which each pixel comprises an entire mass spectrum. Such images can provide insight into the chemical composition across a surface. However, issues arise due to the size and complexity of the data produced. Data are particularly complicated for biological samples, primarily due to overlapping spectra produced by similar components. The traditional approach of selecting individual ion peaks as representative of particular components is insufficient for such complex data sets. Multivariate analysis (MVA) can help to overcome this significant hurdle. We demonstrate that Kohonen self-organizing maps (SOMs) with a toroidal to-pology can be used to analyze a ToF-SIMS hyperspectral imaging data set and identify spectral similarities between pixels. We present a method for color-tagging the toroidal SOM output, which reduces the entire dat...