Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13428
Title: An efficient approach for compound identification based on the frequency features of mass spectra
Authors: Sun, ZL
Lam, KM 
Zhang, J
Keywords: Discrete Fourier transform
Similarity measure
Spectrum matching
Issue Date: 2015
Publisher: Elsevier
Source: Chemometrics and intelligent laboratory systems, 2015, v. 142, p. 117-123 How to cite?
Journal: Chemometrics and Intelligent Laboratory Systems 
Abstract: Similarity-measure-based spectrum matching is an effective approach to chemical compound identification. When the sizes of both the query library and the reference library become increasingly large, most existing spectrum-matching methods encounter a seriously heavy computation burden. In this paper, an effective and efficient compound-identification approach is proposed based on the frequency features of mass spectra. Considering the sparsity of mass spectra, a nonzero feature-selection strategy is proposed to decrease the feature dimensionality of mass spectra. To further improve its efficiency, a correlation-based filtering strategy is presented to select the most correlated reference spectra in order to create a reduced reference library. Based on the decreased features and the reduced reference library, the frequency-feature-based composite similarity measures are computed to estimate the chemical abstracts service (CAS) registry numbers of the mass spectra blue in a query library. Due to the reduction in both the feature dimensionality and the reference library, the computation time of the proposed method is only about 6%-11% of that of the existing methods, while the identification performance remains sufficiently competitive. Experimental results demonstrate the feasibility and efficiency of the proposed method.
URI: http://hdl.handle.net/10397/13428
ISSN: 0169-7439
DOI: 10.1016/j.chemolab.2015.01.019
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