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Component recognition with three‐dimensional fluorescence spectra based on non‐negative matrix factorization
Author(s) -
Yu Shaohui,
Zhang Yujun,
Liu Wenqing,
Zhao Nanjing,
Xiao Xue,
Yin Gaofang
Publication year - 2011
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.1404
Subject(s) - non negative matrix factorization , pattern recognition (psychology) , similarity (geometry) , component (thermodynamics) , artificial intelligence , matrix decomposition , spectral line , principal component analysis , biological system , fluorescence , computer science , matrix (chemical analysis) , chemistry , physics , image (mathematics) , chromatography , optics , biology , eigenvalues and eigenvectors , quantum mechanics , astronomy , thermodynamics
Non‐negative matrix factorization (NMF) is a widely used approach in signal processing. In this work, we apply it to the component recognition of mixtures with multicomponent three‐dimensional fluorescence spectra. Compared with the popular PARAFAC for component recognition, NMF has the following advantages: on one hand, the decomposed spectra are three dimensional, and thus, more information can be obtained, which is beneficial for component recognition; on the other hand, the decomposed spectra are non‐negative and thus have a certain physical significance. More importantly, we propose a type of integrated similarity indices for the three‐dimensional fluorescence spectra, which, by construction, is good at component recognition from overlapping fluorescence spectra. Experiment results demonstrate that NMF combined with integrated similarity index provides an effective method for component recognition of multicomponent three‐dimensional overlapping fluorescence spectra. Copyright © 2011 John Wiley & Sons, Ltd.