| Title |
Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, October 2016
|
| DOI | 10.1007/s00216-016-9919-8 |
| Pubmed ID | |
| Authors |
Meng Hu, Martin Krauss, Werner Brack, Tobias Schulze |
| Abstract |
Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS(2) experiments is suggested for nontarget screening. MZmine 2 detected 75-100 % of the peaks compared to manual peak detection at an intensity level of 10(5) in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 80 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 21% |
| Researcher | 14 | 17% |
| Student > Master | 11 | 13% |
| Student > Bachelor | 6 | 7% |
| Student > Doctoral Student | 5 | 6% |
| Other | 12 | 15% |
| Unknown | 17 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 24 | 29% |
| Environmental Science | 13 | 16% |
| Agricultural and Biological Sciences | 5 | 6% |
| Medicine and Dentistry | 4 | 5% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 7 | 9% |
| Unknown | 26 | 32% |
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 27 October 2016.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from Analytical & Bioanalytical Chemistry
#6,060
of 9,619 outputs
Outputs of similar age
#240,001
of 327,752 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#70
of 195 outputs
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