Improving annotation of known-unknowns with accurately reconstructed mass spectra

文献类型： 外文期刊

第一作者： Chua, Chun Kiang

作者： Chua, Chun Kiang;Lv, Yunbo;Zhang, Hua Jun;Zhao, Wen;Ren, Yi

作者机构：

关键词： Mass spectrometry; Gas chromatography; Deconvolution; Unknown compound; Pure spectra reconstruction

期刊名称：INTERNATIONAL JOURNAL OF MASS SPECTROMETRY （ 影响因子：1.986； 五年影响因子：1.799 ）

ISSN： 1387-3806

年卷期： 2020 年 451 卷

页码：

收录情况： SCI

摘要： Chemical profiling with gas chromatography/mass spectrometry (GC/MS) full-spectrum acquisition mode often leads to the discovery of known-unknown components. These are non-identified components which arise from the limitation of the data processing method or limited breadth of mass spectral libraries. The recent introduction of the NIST Hybrid Search sought to relieve the latter limitation by providing an improved class annotation technique. Herein, we demonstrate the importance of using a precise mass spectral reconstruction technique to increase the confidence of detecting the presence of known-unknowns and subsequently identifying them successfully with NIST Hybrid Search function. We compared the AMDIS algorithm against the rBTEM algorithm and found that the latter could more accurately reconstruct the mass spectra of co-eluting known-unknown components. This has a farreaching implication to increase the number of identified compounds in GC/MS scan data. (C) 2020 Elsevier B.V. All rights reserved.

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