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An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components

Overview of attention for article published in Analytical & Bioanalytical Chemistry, July 2018
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 50%
Student > Ph. D. Student 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Agricultural and Biological Sciences 1 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 September 2018.
All research outputs
#7,971,742
of 12,713,955 outputs
Outputs from Analytical & Bioanalytical Chemistry
#2,360
of 4,717 outputs
Outputs of similar age
#162,353
of 274,552 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#81
of 210 outputs
Altmetric has tracked 12,713,955 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,717 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,552 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.