Title |
An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components
|
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Published in |
Analytical & Bioanalytical Chemistry, July 2018
|
DOI | 10.1007/s00216-018-1260-y |
Pubmed ID | |
Authors |
Chun Kiang Chua, Bo Lu, Yunbo Lv, Xiao Yu Gu, Ai Di Thng, Hua Jun Zhang |
Abstract |
Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components. Graphical abstract ᅟ. |
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Mendeley readers
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Student > Master | 2 | 15% |
Other | 1 | 8% |
Unspecified | 1 | 8% |
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Physics and Astronomy | 1 | 8% |
Other | 2 | 15% |
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