Invited Paper AS+SS-TuA9
Multivariate Analysis of Very Large Hyperspectral SIMS Datasets: What Can We Do, and What Would We Like to Do?

Tuesday, November 8, 2016, 5:00 pm, Room 101B

Advances in instrumentation capabilities, as well as increases in the complexity of modern materials have resulted in a corresponding increase in the size and complexity of data acquired during sample analysis. The increase in the spatial and spectral resolution of the instrumentation is nominally a boon to the analyst, as the measured data more accurately depicts the sample. However, the resulting hyperspectral images routinely consist of upwards of ten thousand pixel spectra for 2D analyses (e.g. a 128x128 pixel image), or millions of voxel spectra for 3D analyses, each of which may consist of hundreds or thousands of ion peaks. Because of the sheer amount of information contained within such an image, it is often no longer feasible to conduct a full manual analysis of the data. An additional factor exacerbating this issue is the fact that many studies necessitate the analysis of a series of spatially resolved replicate measurements of a single sample, or of multiple similar samples. In these studies the aim is not only to characterize the contents of each individual measurement, but also to determine the similarities and differences between the measurements, while ignoring subtle differences caused by changes in analysis conditions between the individual measurements.

A solution to the problem of information overload is the use of multivariate analysis techniques to help guide the analyst, in order to reduce the time needed for determining the chemical make-up of the analyzed samples. These techniques use different approaches in order to reduce the dimensionality of the measured data, resulting in a small set of factors which recreate a simplified model of the data.

The use of MVA approaches, such as Principal Component Analysis (PCA) and Maximum Autocorrelation Factors (MAF), has become an established method of simplifying the analysis of SIMS data arising from a single measurement. We will discuss alternatives to these commonly used methods, including new variations of Multivariate Curve Resolution (MCR) which use additional optimization criteria, as well as MVA approaches not commonly used in SIMS data analysis. Additionally, we will discuss the unique challenges which may arise when applying MVA techniques to the full hyperspectral data contents of a series of measurements.