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

• Published in: Journal of Chromatography A Vol. 1298; pp. 132 - 138
• Main Authors: Koo, Imhoi, Kim, Seongho, Zhang, Xiang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 12-07-2013; Elsevier
• Subjects: Accuracy; Algorithms; Analytical chemistry; chemical analysis; Chemistry; Chromatographic methods and physical methods associated with chromatography; Compound identification; computer software; Correlation analysis; equipment performance; Exact sciences and technology; Gas chromatographic methods; Gas chromatography; gas chromatography-mass spectrometry; Gas Chromatography-Mass Spectrometry - methods; GC–MS; Libraries; Mass spectrometry; Molecular Weight; Optimization; reference standards; Similarity; Spectra; Spectral matching; Weight factor; Spectral matching; GC–MS; Compound identification; Weight factor; GC―MS; Chemical analysis; Similarity; Coupled method; Chemical compound; Gas chromatography; Spectral library; Mass spectrum; Qualitative analysis; Mass spectrometry; Comparative study
• ISSN: 0021-9673; 1873-3778
• DOI: 10.1016/j.chroma.2013.05.021

•Compared performance of 5 literature-reported compound identification measures.•Estimated dependency of identification accuracy on the size of reference library.•Weight factors dependent on both reference library and spectral similarity measures.•The semi-partial correlation mixture measure performs the best.
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.