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.
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We would like to thank Dr. Ni Wangdong for providing valuable insights on how to draft the manuscript.
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Chua, C.K., Lu, B., Lv, Y. et al. An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components. Anal Bioanal Chem 410, 6549–6560 (2018). https://doi.org/10.1007/s00216-018-1260-y
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DOI: https://doi.org/10.1007/s00216-018-1260-y