Document Detail


A new maximum likelihood approach with asymmetric residual distribution for multicomponent mass spectra analysis.
MedLine Citation:
PMID:  15531789     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper proposes a new maximum likelihood approach for the deconvolution of identity and quantity of individual compounds based on the multicomponent mass spectra measured by mass spectrometry (MS). Mixture analysis of multicomponent mass spectra is, typically, based on a linear multicomponent mass spectrum model, where the compounds of the measured spectra to be solved are explicitly stated and assumed to be known. In many cases, however, the measured spectrum may contain unknown compounds that are not explicitly stated in the model and a commonly used least square (LS) solution fails. Moreover, a standard improvement over the LS method in these cases, namely the M-estimation (ME) approach, also suffers from this same problem. Our method overcomes the limitations of the LS and ME methods by modeling the effect of the unknown compound(s) to the residual of the linear model. The experimental results presented show that this new approach can separate more robustly the complex multicomponent mass spectra into their individual constituents compared to the LS and ME methods.
Authors:
Jukka Heikkonen; Jouni Juujarvi; Marianna Ridderstad; Tapio Kotiaho; Raimo A Ketola; Virpi Tarkiainen
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  European journal of mass spectrometry (Chichester, England)     Volume:  10     ISSN:  1469-0667     ISO Abbreviation:  Eur J Mass Spectrom (Chichester, Eng)     Publication Date:  2004  
Date Detail:
Created Date:  2004-11-08     Completed Date:  2005-01-04     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  101124748     Medline TA:  Eur J Mass Spectrom (Chichester, Eng)     Country:  England    
Other Details:
Languages:  eng     Pagination:  573-8     Citation Subset:  IM    
Affiliation:
Helsinki University of Technology, Laboratory of Computational Engineering, PO Box 9203, FIN-02015 Hut, Finland. jukka.heikkonen@hut.fi
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MeSH Terms
Descriptor/Qualifier:
Air Pollutants / analysis
Complex Mixtures / analysis*
Likelihood Functions*
Models, Statistical*
Organic Chemicals / analysis
Spectrometry, Mass, Electrospray Ionization / methods*,  statistics & numerical data*
Volatilization
Chemical
Reg. No./Substance:
0/Air Pollutants; 0/Complex Mixtures; 0/Organic Chemicals

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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