DocumentCode :
2054292
Title :
Bayesian inversion of multi-mode NEMS mass spectrometry signal
Author :
Perenon, R. ; Sage, E. ; Mohammad-Djafari, A. ; Duraffourg, L. ; Hentz, S. ; Brenac, A. ; Morel, R. ; Grangeat, Pierre
Author_Institution :
CEA Leti, Grenoble, France
fYear :
2013
fDate :
9-13 Sept. 2013
Firstpage :
1
Lastpage :
5
Abstract :
Nano ElectroMechanical Systems are a new class of sensors that offers high sensitivity and opens new perspectives in the mass spectrometry field. This acquisition is performed in counting-mode, and the main tasks of associated information processing are to detect the molecules, to quantify their respective mass and to combine this information in order to recover the mass spectrum of the analysed solution. We propose a joint detection-quantification method based on a hierarchical description of the measurement system. Computation is done using a Reversible Jumps Monte-Carlo Markov-Chain algorithm. The approach we are describing in this communication solves the two problems of the joint impulse deconvolution on multiple output signals (multi-mode acquisition) and the non-linear relation between the observed signals and the mass of molecules, including the localization of the molecules on the sensor. We validate our method on both simulated and experimental data.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; deconvolution; mass spectroscopy; measurement systems; nanoelectromechanical devices; signal processing; Bayesian inversion; hierarchical description; information processing; joint detection-quantification method; joint impulse deconvolution; mass spectrum; measurement system; multi-mode NEMS mass spectrometry signal; multi-mode acquisition; nanoelectromechanical systems; reversible jumps Monte-Carlo Markov-chain algorithm; Adsorption; Bayes methods; Harmonic analysis; Nanoelectromechanical systems; Noise; Resonant frequency; Sensors; Bayesian inference; Detection-Estimation; Information processing; Inverse problems; MCMC; Mass spectrometry; NEMS; Nanotechnologies; Proteomics; Statistical signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location :
Marrakech
Type :
conf
Filename :
6811472
Link To Document :
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