This DigitalMicrograph plugin makes Multivariate Statistical Analysis of STEM EELS or EDX data cubes. The core of the package is Principal Component Analysis (PCA) that extracts several components with the highest variance from raw data. In this way the dimensionality of data is greatly reduced and data sets are denoised. For better interpretation, obtained PCA components can be further rotated in the factor space using Varimax or ICA methods. PCA results can also be casted in endmembers spectra. These easy-to-interpret spectra are found by the manupulations in the factor space. For datasets with the complicated internal data distribution the package offers clustering data in the factor space.
Lecture introducing into the MSA stuff delivered in the EELS school in July 2019:
Multivariate Statistical Analysis to denoise EELS spectrum-images
This lecture explains how to denoise spectrum images treats the central problem of PCA – truncation of principal components. A new simple and robust method for automatic truncation is invented: Automatic Truncation of Principal Components
How to use temDM MSA:
temDM MSA 2.37 basic version (open source, Anscomb transform debugged):
temDM MSA 2.36 (some bugs are fixed) advanced version with acceleration:
This trial version is valid till 31.12.2022.
Example of simulated EELS datacube:
Example – Mag*I*Cal EELS datacube:
Example – Mag*I*Cal XEDS datacube:
Example – Velox XEDS superalloy:
Example – synthetic STEM XEDS spectrum-images of a CMOS device: