Doi.org/10.1016/j.jasms.2009.02.030: Difference between revisions
(Created page with "== Target Article == Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a Fourier transform ion cyclotron resonance mass spectrometer.; Williams, D.K., Kovach, A.L., Muddiman, D.C., Hanck, K.W.: ; Journal of The American Society for Mass Spectrometry; 2009; https://doi.org/10.1016/j.jasms.2009.02.030 == Article providing comments == Comment on: “Utilizing Artificial Neural Networks in MATLAB to Achieve Parts-Per-B...") |
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Latest revision as of 07:14, 18 August 2022
Target Article[edit | edit source]
Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a Fourier transform ion cyclotron resonance mass spectrometer.; Williams, D.K., Kovach, A.L., Muddiman, D.C., Hanck, K.W.: ; Journal of The American Society for Mass Spectrometry; 2009; https://doi.org/10.1016/j.jasms.2009.02.030
Article providing comments[edit | edit source]
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; Proctor, Charles; ; Journal of The American Society for Mass Spectrometry; 2014-4-01 https://doi.org/10.1007/s13361-013-0805-8
Summary[edit | edit source]
Note: Opened to append information.