Removing cosmic ray features from Raman map data by a refined nearest neighbor comparison method as a precursor for...

Area of Science:

• Spectroscopy
• Data Analysis
• Materials Science

Background:

• Raman spectroscopy mapping generates large datasets.
• Cosmic ray (CR) artifacts can contaminate spectral data, hindering analysis.
• Existing methods may struggle with varying signal-to-noise ratios.

Purpose of the Study:

• To develop and present a robust algorithm for removing CR features from CCD-based Raman spectra.
• To ensure the accurate recovery of underlying spectral information.
• To provide a method applicable to various data types beyond Raman mapping.

Main Methods:

• Compares each spectrum to adjacent spectra using correlation.
• Identifies CRs using the most similar neighbor (MSN) spectrum, with offset values adjusted for signal-to-noise ratio.
• Replaces contaminated pixels with scaled MSN spectra for data recovery.

Main Results:

• The algorithm effectively removes CRs, including those that would interfere with chemometric analysis like principal component analysis (PCA).
• No misdetections occurred with appropriate offset values.
• Underlying spectral features are fully recovered, demonstrating the algorithm's efficacy.

Conclusions:

• The presented algorithm offers a reliable solution for CR removal in Raman mapping.
• It is adaptable to diverse datasets, including time series and temperature profiles.
• This method enhances the quality of spectral data for subsequent quantitative and qualitative analyses.