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. The Local PCA tries to reduce such artifacts by increasing the PCA sensitivity.

MSA v2.0 (Local PCA) Read More

MSA v3.0 Read More



References and Technical Notes (*Basic Readings)

Examples (Sample Data)


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Required plug-ins.

User Key Driver

Previous versions



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