Literature DB >> 24408180

Comment on: "Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a Fourier transform ion cyclotron resonance mass spectrometer" by D. Keith Williams Jr., Alexander L. Kovach, David C. Muddiman, and Kenneth W. Hanck. J. Am. Soc. Mass Spectrom. 20, 1303-1310 (2009).

Charles Proctor1.   

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Year:  2014        PMID： 24408180     DOI： 10.1007/s13361-013-0805-8

Source DB:  PubMed          Journal:  J Am Soc Mass Spectrom        ISSN： 1044-0305            Impact factor:   3.109


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  2 in total

1.  Parts-per-billion mass measurement accuracy achieved through the combination of multiple linear regression and automatic gain control in a Fourier transform ion cyclotron resonance mass spectrometer.

Authors:  D Keith Williams; David C Muddiman
Journal:  Anal Chem       Date:  2007-06-01       Impact factor: 6.986

2.  Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a fourier transform ion cyclotron resonance mass spectrometer.

Authors:  D Keith Williams; Alexander L Kovach; David C Muddiman; Kenneth W Hanck
Journal:  J Am Soc Mass Spectrom       Date:  2009-03-11       Impact factor: 3.109
  2 in total

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