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Blind separation of fluorescence spectra using sparse non‐negative matrix factorization on right hand factor
Author(s) -
Yang Ruifang,
Zhao Nanjing,
Xiao Xue,
Yu Shaohui,
Liu Jianguo,
Liu Wenqing
Publication year - 2015
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2723
Subject(s) - non negative matrix factorization , initialization , matrix (chemical analysis) , matrix decomposition , nonnegative matrix , similarity (geometry) , factorization , spectral line , chemistry , pattern recognition (psychology) , artificial intelligence , mathematics , computer science , chromatography , algorithm , image (mathematics) , physics , eigenvalues and eigenvectors , symmetric matrix , astronomy , programming language , quantum mechanics
Sparse non‐negative matrix factorization on right side factor (SNMF/R) has better performance in feature extraction than non‐negative matrix factorization. In this work, SNMF/R was first used to separate the overlapped three‐dimensional fluorescence spectra of polycyclic aromatic hydrocarbons mixtures in pure water, lake water, and river water, respectively. It is found that the similarity coefficients between the acquired three‐dimensional spectra and the corresponding reference spectra with random initials are all above 0.80; the recognition rate of SNMF/R is higher than that of PARAFAC and non‐negative matrix factorization algorithms, especially in the case of lake water and river water samples. In addition, SNMF/R does not need any initialization scheme designing during spectra separation. These results demonstrate that SNMF/R is an appropriate algorithm to separate the overlapped fluorescence spectra of polycyclic aromatic hydrocarbons in aquatic environment accurately and effectively. Copyright © 2015 John Wiley & Sons, Ltd.