Non-negative assisted principal component analysis: A novel method of data analysis for raman spectroscopy

A novel method for the analysis of multivariate Raman spectroscopy data is presented. The method combines non-negative matrix factorisation and principal component analysis, integrating the advantages and combating the disadvantages of both techniques. It involves the derivation of physically realistic spectra and the analysis of chemical and spatial trends across a sample surface. Proof of concept is demonstrated through two investigations. The first is a set of Raman spectra taken from a powder sample containing potassium sulphate, calcium carbonate and sodium sulphate. A second uses Raman data taken from an artificially corroded sample of superalloy material commonly used in gas turbine engines. This successful proof of concept for samples with unknown surface content sets the way for future development of the technique.

Automated summary

A novel method combining non-negative matrix factorization and principal component analysis is presented for the analysis of multivariate Raman spectroscopy data. The method allows derivation of physically realistic spectra and analysis of chemical and spatial trends across a sample surface. Proof of concept is demonstrated through two investigations, paving the way for future development of the technique.