Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry
Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a sto...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2007-01-01
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| Series: | Cancer Informatics |
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| Online Access: | http://la-press.com/article.php?article_id=368 |
| _version_ | 1818159281153769472 |
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| author | John M. Koomen Miray Mutlu Hyunjin Shin Mia K. Markey |
| author_facet | John M. Koomen Miray Mutlu Hyunjin Shin Mia K. Markey |
| author_sort | John M. Koomen |
| collection | DOAJ |
| description | Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling. |
| first_indexed | 2024-12-11T15:43:29Z |
| format | Article |
| id | doaj.art-63d314a6156e42cd93cfae4b1b1f6c4b |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-11T15:43:29Z |
| publishDate | 2007-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-63d314a6156e42cd93cfae4b1b1f6c4b2022-12-22T00:59:46ZengSAGE PublishingCancer Informatics1176-93512007-01-013219230Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass SpectrometryJohn M. KoomenMiray MutluHyunjin ShinMia K. MarkeyNoise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling.http://la-press.com/article.php?article_id=368MassSpectrometryMassMatrix-Assisted Laser Desorption-IonizationNoiseArtifactsFourier AnalysisSignal ProcessingComputer-AssistedComputer SimulationModelsComputer. |
| spellingShingle | John M. Koomen Miray Mutlu Hyunjin Shin Mia K. Markey Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry Cancer Informatics Mass Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Noise Artifacts Fourier Analysis Signal Processing Computer-Assisted Computer Simulation Models Computer. |
| title | Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry |
| title_full | Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry |
| title_fullStr | Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry |
| title_full_unstemmed | Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry |
| title_short | Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry |
| title_sort | parametric power spectral density analysis of noise from instrumentation in maldi tof mass spectrometry |
| topic | Mass Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Noise Artifacts Fourier Analysis Signal Processing Computer-Assisted Computer Simulation Models Computer. |
| url | http://la-press.com/article.php?article_id=368 |
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