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

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.