Wavelet- and fourier-transform-based spectrum similarity approaches to compound identification in gas chromatography/mass spectrometry

Imhoi Koo, Xiang Zhang, Seongho Kim

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

The high-throughput gas chromatography/mass spectrometry (GC/MS) technology offers a powerful means of analyzing a large number of chemical and biological samples. One of the important analyses of GC/MS data is compound identification. In this work, novel spectral similarity measures based on the discrete wavelet and Fourier transforms were proposed. The proposed methods are composite similarities that are composed of weighted intensities and wavelet/Fourier coefficients using cosine correlation. The performance of the proposed approaches along with the existing similarity measures was evaluated using the NIST Chemistry WebBook mass database maintained by the National Institute of Standards and Technology (NIST) as a library of reference spectra and repetitive mass spectral data as query spectra. The analysis results showed that the identification accuracies of the wavelet- and Fourier-transform-based methods were improved by 2.02% and 1.95%, respectively, compared to that of the weighted dot product (cosine correlation) and by 3.01% and 3.08%, respectively, compared to that of the composite similarity measure. The improved identification accuracy demonstrates that the proposed approaches outperformed the existing similarity measures in the literature.

Original languageEnglish (US)
Pages (from-to)5631-5638
Number of pages8
JournalAnalytical chemistry
Volume83
Issue number14
DOIs
StatePublished - Jul 15 2011

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Gas chromatography
Wavelet transforms
Mass spectrometry
Fourier transforms
Discrete wavelet transforms
Composite materials
Discrete Fourier transforms
Throughput

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry

Cite this

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abstract = "The high-throughput gas chromatography/mass spectrometry (GC/MS) technology offers a powerful means of analyzing a large number of chemical and biological samples. One of the important analyses of GC/MS data is compound identification. In this work, novel spectral similarity measures based on the discrete wavelet and Fourier transforms were proposed. The proposed methods are composite similarities that are composed of weighted intensities and wavelet/Fourier coefficients using cosine correlation. The performance of the proposed approaches along with the existing similarity measures was evaluated using the NIST Chemistry WebBook mass database maintained by the National Institute of Standards and Technology (NIST) as a library of reference spectra and repetitive mass spectral data as query spectra. The analysis results showed that the identification accuracies of the wavelet- and Fourier-transform-based methods were improved by 2.02{\%} and 1.95{\%}, respectively, compared to that of the weighted dot product (cosine correlation) and by 3.01{\%} and 3.08{\%}, respectively, compared to that of the composite similarity measure. The improved identification accuracy demonstrates that the proposed approaches outperformed the existing similarity measures in the literature.",
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Wavelet- and fourier-transform-based spectrum similarity approaches to compound identification in gas chromatography/mass spectrometry. / Koo, Imhoi; Zhang, Xiang; Kim, Seongho.

In: Analytical chemistry, Vol. 83, No. 14, 15.07.2011, p. 5631-5638.

Research output: Contribution to journalArticle

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