Utilizing Artificial Neural Networks in MATLAB to Achieve Parts-Per-Billion Mass Measurement Accuracy with a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

Journal of the American Society for Mass Spectrometry

Volumn 20, Issue 7, 2009, Pages 1303-1310

  Williams Jr , D Keith ^a   Kovach, Alexander L ^a   Muddiman, David C ^a   Hanck, Kenneth W ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTOMATIC GAIN CONTROL; EXPERIMENTAL SPECTRA; EXTERNAL CALIBRATION; FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETERS; FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY; ION POPULATION; MASS MEASUREMENTS; MOLECULAR SPECIFICITY; PARTS-PER-BILLION; SPACE CHARGE EFFECTS; SPECTROMETRIC MEASUREMENTS;

BACKPROPAGATION; CALIBRATION; CYCLOTRONS; ELECTRON CYCLOTRON RESONANCE; GAIN CONTROL; IONS; MASS SPECTROMETERS; MASS SPECTROMETRY; MATLAB; NEURAL NETWORKS; PLASMAS; RESONANCE;

FOURIER TRANSFORMS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; ION CYCLOTRON RESONANCE MASS SPECTROMETRY; MOLECULAR DYNAMICS; SENSITIVITY AND SPECIFICITY;

FOURIER ANALYSIS; LINEAR MODELS; MASS SPECTROMETRY; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 67649372680     PISSN: 10440305     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jasms.2009.02.030     Document Type: Article

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