A Proposal for Automated Background Removal of Bio-Raman Data

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

“…For the spectral pretreatments, the baseline fluctuations due to the autofluorescence of the hair was corrected using the Zhao's method; 4 then, glue components at 1607 and 1600 cm -1 (the positions were indicted by *) were removed using our developed background removal method. 5 For the observed broad band at 1660 cm -1 , we consider that there exist multiple overlapped components with different peak positions due to the different secondary structures of protein. [8][9][10] Almost all of the protein detected for Raman signal is considered keratin and keratin associated proteins (KAPs), which are the main components of the cortex.…”

“…[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.…”

Realization of bio-Raman black hair analysis by automatic signal separation of melanin from other hair components

“…7,8 Bio-Raman data is usually large and the number of spectra frequently reaches thousands. 9,10 Spectral analysis, such as automatic baseline correction, 11 automatic background removal, 12,13 principal component analysis (PCA), 9 non-negative matrix factorization, [14][15][16] and time-series correlation analysis, 17 have been frequently used and sophisticated to extract and visualize intrinsic information.…”

Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis

“…We optimize the scaling factors k so that multiple r(wj) become independent of q(wj). To do so, by considering the shortest lengths of spectra r(wj), the scaling factors k can be automatically and approximately determined by the following simple relation, 11 that is,…”