Bio-Raman non-negative matrix factorization: its practical methodology

Abstract

Research on live cells using a Raman microscope (bio-Raman research) has been attractive due to its versatility; but informative bio-Raman data has been complicated and largely sized. Non-negative matrix factorization (NMF) is expected an effective method to disentangle it; but the problem was that NMF does not give the unique decomposition, depending on different initial settings. That is, NMF causes cross-talks among factorized signals that disturb the quantitative analysis. To exemplify the problem, Raman imaging of a cross section of a rice grain was analyzed. To solve the problem, a practical methodology of bio-Raman NMF was described.

Principal component analysis for the protein amide I region of Raman imaging data before/after straight perm of human grey hair

“…[1][2][3] Among these analytical techniques, Raman spectroscopy is one of powerful and versatile techniques, as this approach is non-labelling, non-destructive to living tissues / cells, and less influenced by water. As Raman data of a biological tissue is usually complicated and large, techniques for spectral pre-treatments 4,5 and advanced spectral analytical methods, such as principal component analysis (PCA) and non-negative matrix factorization (NMF), 6,7 have been developed and applied.…”