Blind separation of fluorescence spectra using sparse non-negative matrix factorization on right hand factor

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 (c) 2015 John Wiley & Sons, Ltd.