| Title |
An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components
|
|---|---|
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 40% |
| Members of the public | 2 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 15% |
| Student > Master | 2 | 15% |
| Other | 1 | 8% |
| Unspecified | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 5 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Nursing and Health Professions | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Physics and Astronomy | 1 | 8% |
| Other | 2 | 15% |
| Unknown | 6 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2018.
All research outputs
#16,728,456
of 25,385,509 outputs
Outputs from Analytical & Bioanalytical Chemistry
#5,260
of 9,619 outputs
Outputs of similar age
#208,940
of 340,393 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#77
of 186 outputs
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