Multivariate Statistical Analysis (MSA) is a DigitalMicrograph plug-in based on the routine developed by Masashi Watanabe. The MSA implements the PCA (Principle Component Analysis), and finds statistically significant features from 2D and 3D spectrum images (SIs) gathered by spectrometric techniques such as XEDS, EELS, EFTEM and cathodoluminescence.
The PCA tries to explain the observed data using a small number of the principal components, and thus reduces the random noise substantially. Although the PCA approach is very efficient and useful, it may create unexpected artifacts especially in higher noise conditions. Especially, a small amount of signal will be buried in the random noise over the whole processing area. MSA v2.0 (Local PCA) tries to cope with these problems.

MSA v2.0 (Local PCA)
The Local PCA performs the analysis locally by dividing the SI data to small segments in order to increase the PCA sensitivity and thus reduce artifacts. Read More

MSA v3.0
From this version, an opened SI data can be directly processed with the latest DigitalMicrograph. Read More

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