@Article{williams09utilizing,
  author    = {D. Keith {Williams Jr.} and Alexander L. Kovach and David C. Muddiman and Kenneth W. Hanck},
  title     = {Utilizing artificial neural networks in {MATLAB} to achieve parts-per-billion mass measurement accuracy with a {Fourier} transform ion cyclotron resonance mass spectrometer},
  journal   = {J Am Soc Mass Spectrom},
  year      = {2009},
  volume    = {20},
  number    = {7},
  pages     = {1303--1310},
  abstract  = {Fourier transform ion cyclotron resonance mass spectrometry has the ability to realize exceptional mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement, as well as relative ion abundance of a given species. Artificial neural network calibration in conjunction with automatic gain control (AGC) is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. In addition, artificial neural network calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level.},
  doi       = {10.1016/j.jasms.2009.02.030},
  file      = {WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:2009/WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:PDF},
  keywords  = {Fourier Analysis; Linear Models; Mass Spectrometry; Neural Networks; Reproducibility of Results; Sensitivity and Specificity; TrACReview},
  optmonth  = jul,
  owner     = {kerstin},
  pmid      = {19362012},
  timestamp = {2012.03.01},
}

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W."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["D.","Keith"],"propositions":[],"lastnames":["Williams Jr."],"suffixes":[]},{"firstnames":["Alexander","L."],"propositions":[],"lastnames":["Kovach"],"suffixes":[]},{"firstnames":["David","C."],"propositions":[],"lastnames":["Muddiman"],"suffixes":[]},{"firstnames":["Kenneth","W."],"propositions":[],"lastnames":["Hanck"],"suffixes":[]}],"title":"Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a Fourier transform ion cyclotron resonance mass spectrometer","journal":"J Am Soc Mass Spectrom","year":"2009","volume":"20","number":"7","pages":"1303–1310","abstract":"Fourier transform ion cyclotron resonance mass spectrometry has the ability to realize exceptional mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement, as well as relative ion abundance of a given species. Artificial neural network calibration in conjunction with automatic gain control (AGC) is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. In addition, artificial neural network calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level.","doi":"10.1016/j.jasms.2009.02.030","file":"WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:2009/WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:PDF","keywords":"Fourier Analysis; Linear Models; Mass Spectrometry; Neural Networks; Reproducibility of Results; Sensitivity and Specificity; TrACReview","optmonth":"July","owner":"kerstin","pmid":"19362012","timestamp":"2012.03.01","bibtex":"@Article{williams09utilizing,\n author = {D. Keith {Williams Jr.} and Alexander L. Kovach and David C. Muddiman and Kenneth W. Hanck},\n title = {Utilizing artificial neural networks in {MATLAB} to achieve parts-per-billion mass measurement accuracy with a {Fourier} transform ion cyclotron resonance mass spectrometer},\n journal = {J Am Soc Mass Spectrom},\n year = {2009},\n volume = {20},\n number = {7},\n pages = {1303--1310},\n abstract = {Fourier transform ion cyclotron resonance mass spectrometry has the ability to realize exceptional mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement, as well as relative ion abundance of a given species. Artificial neural network calibration in conjunction with automatic gain control (AGC) is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. In addition, artificial neural network calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level.},\n doi = {10.1016/j.jasms.2009.02.030},\n file = {WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:2009/WilliamsEtAl_UtilizingArtificalNeuralNetworks_JASMS_2009.pdf:PDF},\n keywords = {Fourier Analysis; Linear Models; Mass Spectrometry; Neural Networks; Reproducibility of Results; Sensitivity and Specificity; TrACReview},\n optmonth = jul,\n owner = {kerstin},\n pmid = {19362012},\n timestamp = {2012.03.01},\n}\n\n","author_short":["Williams Jr., D. 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