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.

A Prototype of Mathematical Treatment of Pen Pressure Data for Signature Verification

“…According to the literature, Pearson's correlation coefficient has wide applications for pattern recognition in analytical chemistry, for example, comparative analysis of mass spectral similarity in gas chromatography-mass spectrometry (20). In statistics, Pearson's correlation coefficient, commonly represented by the letter r, is a measure of the strength and direction of the linear relationship between two variables.…”

Compound identification in GC-MS by simultaneously evaluating the mass spectrum and retention index

“…11 In this study, we perform compound identification using both mass spectrum and retention index information. Therefore, we first studied the performance of the four mass spectral similarity measures in identifying compounds for each of the three query datasets constructed using the method described in the section of Materials and Methods.…”

“…Several mass spectral libraries have been generated, 1-4 and various mass spectral similarity measures have been developed, including composite similarity, 5 probability-based matching system, 6 Hertz similarity index, 7 normalized Euclidean and absolute value distance, 8 wavelet and Fourier transform-based composite similarity, 9 partial and semi-partial correlations-based composite similarity. 10 Most recently, Koo et al 11 compared the performance of several spectral similarity measures and concluded that the performance compound identification depends on multiple factors including the mass spectrum library, spectral similarity measure and weight factors. They further discussed that the compound identification based on mass spectra only has limited accuracy and the high accuracy compound identification can be achieved by incorporating compound separation information into mass spectrum matching.…”

iMatch2: Compound identification using retention index for analysis of gas chromatography–mass spectrometry data

“…Multiple mass spectral similarity measures have been proposed, including composite similarity [1], wavelet transform-based composite measure [2], mixture partial and semi-partial correlation [3]. Some efforts have been also devoted to find the optimal weight factors to improve the identification accuracy [4,5]. The performance of mass spectral matching can be affected by many factors such as the reference spectral library, spectral similarity measure and the weight factors [4,5].…”

“…Some efforts have been also devoted to find the optimal weight factors to improve the identification accuracy [4,5]. The performance of mass spectral matching can be affected by many factors such as the reference spectral library, spectral similarity measure and the weight factors [4,5]. Furthermore, the mass spectrum matching-based compound identification cannot differentiate the isomers from each other.…”