Fractal Stochastic Signal Processing Based on the Affine Spectral Correlation Method

2006 International Conference on Communications, Circuits and Systems ◽

10.1109/icccas.2006.284665 ◽

2006 ◽

Author(s):

Lin Jun ◽

Xiong Gang ◽

Yang xiao-niu ◽

Wang Fang

Keyword(s):

Signal Processing ◽

Correlation Method ◽

Spectral Correlation ◽

Stochastic Signal ◽

Stochastic Signal Processing

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Stochastic Signal Processing

Signal Processing for Communications ◽

10.1201/9781439808009.ch8 ◽

2008 ◽

pp. 217-234

Keyword(s):

Signal Processing ◽

Stochastic Signal ◽

Stochastic Signal Processing

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Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry

Cancer Informatics ◽

10.1177/117693510700300019 ◽

2007 ◽

Vol 3 ◽

pp. 117693510700300 ◽

Cited By ~ 12

Author(s):

Hyunjin Shin ◽

Miray Mutlu ◽

John M. Koomen ◽

Mia K. Markey

Keyword(s):

Mass Spectrometry ◽

Signal Processing ◽

Mass Spectra ◽

Thermal Noise ◽

Electrical Noise ◽

Noise Sources ◽

Stochastic Signal ◽

Power Spectral ◽

Stochastic Signal Processing ◽

Periodic Components

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.

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