Cluster analysis of soft X-ray spectromicroscopy data.

Area of Science:

• Materials Science
• Chemistry
• Environmental Science

Background:

• Soft X-ray spectromicroscopy offers high spatial resolution for chemical analysis.
• Characterizing unknown chemical species in complex samples remains a challenge.

Purpose of the Study:

• To develop a novel approach for analyzing soft X-ray spectromicroscopy data with unknown chemical species.
• To enable accurate chemical speciation and concentration mapping in complex biological and environmental samples.

Main Methods:

• Applied principal component analysis (PCA) for data orthogonalization and noise reduction.
• Utilized cluster analysis (unsupervised pattern matching) for pixel classification based on spectral similarity.
• Extracted representative spectra and determined concentration gradients for identified clusters.

Main Results:

• Successfully classified pixels and extracted high signal-to-noise ratio spectra for unknown components.
• Demonstrated the method's efficacy on simulated organic compounds and a lutetium-hematite mixture.
• Provided a robust framework for quantitative chemical speciation in complex matrices.

Conclusions:

• The combined PCA and cluster analysis approach effectively addresses limitations in analyzing unknown chemical species with soft X-ray spectromicroscopy.
• This method enhances the capability for detailed chemical mapping in fields like environmental science and biology.
• Facilitates a deeper understanding of material composition and properties in complex systems.