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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping.

Wil Gardner¹, David A Winkler², Davide Ballabio³

¹Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia.

|November 17, 2020

Summary

This summary is machine-generated.

We introduce SOM-RPM, a novel 3D visualization method for mass spectrometry imaging data. This technique effectively maps chemical similarities in depth profiles, aiding in material characterization and degradation analysis.

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Area of Science:

Surface Science
Analytical Chemistry
Materials Science
Data Science

Background:

Multivariate analysis is crucial for interpreting complex mass spectrometry imaging (MSI) data, revealing insights into surface chemistry.
Nonlinear relationships in MSI data necessitate advanced machine learning approaches for effective analysis.
Previous work demonstrated the utility of self-organizing maps (SOM) for 2D time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images.

Purpose of the Study:

To adapt and apply the novel SOM-relational perspective mapping (SOM-RPM) methodology for the characterization and interpretation of 3D TOF-SIMS depth profile data.
To demonstrate the capability of SOM-RPM in visualizing and segmenting complex 3D chemical information on a voxel-by-voxel basis.

Main Methods:

Depth profiling of an organic Irganox multilayer standard sample using TOF-SIMS.
Application of SOM-RPM to generate 3D similarity maps where voxel spectral similarity is represented by color similarity.
Unsupervised segmentation of the 3D data based on the generated similarity maps.

Main Results:

SOM-RPM successfully created 3D similarity maps, enabling meaningful differentiation between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films.
The method identified distinct surface clusters associated with environmental exposure and sample degradation.
Key fragment ions were identified for each cluster, linking them to specific underlying chemistries.

Conclusions:

SOM-RPM is a powerful, unsupervised method for reducing large 3D TOF-SIMS datasets into simplified, interpretable 3D visualizations.
The technique effectively clusters data and visualizes complex chemical relationships within depth-profiled samples.
SOM-RPM offers significant potential for material characterization, quality control, and understanding degradation mechanisms in multilayer systems.