Literature DB >> 15880604

Statistical design of experiments as a tool in mass spectrometry.

Leah S Riter1, Olga Vitek, Karen M Gooding, Barry D Hodge, Randall K Julian.   

Abstract

This Tutorial is an introduction to statistical design of experiments (DOE) with focus on demonstration of how DOE can be useful to the mass spectrometrist. In contrast with the commonly used one factor at a time approach, DOE methods address the issue of interaction of variables and are generally more efficient. The complex problem of optimizing data-dependent acquisition parameters in a bottom-up proteomics LC-MS/MS analysis is used as an example of the power of the technique. Using DOE, a new data-dependent method was developed that improved the quantity of confidently identified peptides from rat serum. Copyright 2005 John Wiley & Sons, Ltd.

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Year:  2005        PMID: 15880604     DOI: 10.1002/jms.871

Source DB:  PubMed          Journal:  J Mass Spectrom        ISSN: 1076-5174            Impact factor:   1.982


  14 in total

1.  Optimization and comparison of ESI and APCI LC-MS/MS methods: a case study of Irgarol 1051, Diuron, and their degradation products in environmental samples.

Authors:  Niki C Maragou; Nikolaos S Thomaidis; Michael A Koupparis
Journal:  J Am Soc Mass Spectrom       Date:  2011-07-06       Impact factor: 3.109

2.  Factorial experimental designs elucidate significant variables affecting data acquisition on a quadrupole Orbitrap mass spectrometer.

Authors:  Shan M Randall; Helene L Cardasis; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2013-08-03       Impact factor: 3.109

3.  DART-MS analysis of inorganic explosives using high temperature thermal desorption.

Authors:  Thomas P Forbes; Edward Sisco; Matthew Staymates; Greg Gillen
Journal:  Anal Methods       Date:  2017-05-04       Impact factor: 2.896

4.  Influence of Desorption Conditions on Analyte Sensitivity and Internal Energy in Discrete Tissue or Whole Body Imaging by IR-MALDESI.

Authors:  Elias P Rosen; Mark T Bokhart; H Troy Ghashghaei; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2015-04-04       Impact factor: 3.109

5.  Comprehensive Optimization of LC-MS Metabolomics Methods Using Design of Experiments (COLMeD).

Authors:  Seth D Rhoades; Aalim M Weljie
Journal:  Metabolomics       Date:  2016-10-24       Impact factor: 4.290

6.  Accelerating Lipidomic Method Development through in Silico Simulation.

Authors:  Paul D Hutchins; Jason D Russell; Joshua J Coon
Journal:  Anal Chem       Date:  2019-07-25       Impact factor: 6.986

7.  IR-MALDESI mass spectrometry imaging of biological tissue sections using ice as a matrix.

Authors:  Guillaume Robichaud; Jeremy A Barry; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2014-01-03       Impact factor: 3.109

8.  Improving proteome coverage on a LTQ-Orbitrap using design of experiments.

Authors:  Genna L Andrews; Ralph A Dean; Adam M Hawkridge; David C Muddiman
Journal:  J Am Soc Mass Spectrom       Date:  2011-02-15       Impact factor: 3.109

9.  Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS).

Authors:  Mary A Molina; Weixiang Zhao; Shankar Sankaran; Michael Schivo; Nicholas J Kenyon; Cristina E Davis
Journal:  Anal Chim Acta       Date:  2008-09-11       Impact factor: 6.558

10.  Evaluation and optimization of mass spectrometric settings during data-dependent acquisition mode: focus on LTQ-Orbitrap mass analyzers.

Authors:  Anastasia Kalli; Geoffrey T Smith; Michael J Sweredoski; Sonja Hess
Journal:  J Proteome Res       Date:  2013-05-31       Impact factor: 4.466

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