Comparative analysis of mass spectral matching-based compound identification in gas chromatography-mass spectrometry

Abstract

Compound identification in gas chromatography-mass spectrometry (GC-MS) is usually achieved by matching query spectra to spectra present in a reference library. Although several spectral similarity measures have been developed and compared using a small reference library, it still remains unknown how the relationship between the spectral similarity measure and the size of reference library affects on the identification accuracy as well as the optimal weight factor. We used three reference libraries to investigate the dependency of the optimal weight factor, spectral similarity measure and the size of reference library. Our study demonstrated that the optimal weight factor depends on not only spectral similarity measure but also the size of reference library. The mixture semi-partial correlation measure outperforms all existing spectral similarity measures in all tested reference libraries, in spite of the computational expense. Furthermore, the accuracy of compound identification using a larger reference library in future is estimated by varying the size of reference library. Simulation study indicates that the mixture semi-partial correlation measure will have the best performance with the increase of reference library in future.

Figure 1. Contour plot of compound identification accuracy calculated using the WC similarity measure and NIST11 as reference library

The four points in the plot show the minimum and the maximum of weight factors for intensity and m/z, respectively, for identification accuracy of 79.9%.

Figure 2. Accuracy of compound identification by random sampling

The solid lines stand for results of 100 time random sampling. The dash lines represent the predicted identification accuracy of each spectral similarity measure using large reference library to be developed in future.

Figure 3. Accuracy of compound identification

An identification result is considered as correct if the correct reference spectrum is one of the multiple top ranked reference spectra.

Figure 4

Computational time of each spectral similarity measure.