Estimation of low concentrations in the presence of high concentrations using Bayesian algorithms for interpretation of spectrophotometric data

A Bayesian method for interpretation of spectral data, dedicated to the monitoring‐type applications of mini‐ and micro‐spectrometers, is addressed. The method generates estimates of the concentrations of the components of an analyzed substance on the basis of the data representative of its absorpti...

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Bibliographic Details
Published inJournal of chemometrics Vol. 18; no. 5; pp. 217 - 230
Main Authors Niedziński, Cezary, Morawski, Roman Z.
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 01.05.2004
Wiley
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Summary:A Bayesian method for interpretation of spectral data, dedicated to the monitoring‐type applications of mini‐ and micro‐spectrometers, is addressed. The method generates estimates of the concentrations of the components of an analyzed substance on the basis of the data representative of its absorption spectrum, provided that both the normalized spectra of the components and statistical information on historical measurements of the monitored concentrations are available. The Bayesian method is systematically compared with the constrained least‐squares method, under an assumption that the estimated concentrations differ considerably and the processed data are subject not only to random instrumental noise but also to some random disturbances introduced by the residual content of components of the analyzed substance being not identified. A study, performed using both synthetic and real‐world spectrophotometric data, is aimed at the assessment of the robustness of the Bayesian method to the maximum‐to‐minimum ratio of concentrations and imprecision of a priori information. Copyright © 2004 John Wiley & Sons, Ltd.
Bibliography:ark:/67375/WNG-C82L8JWD-8
State Committee of Scientific Research, Poland - No. 8 T10C 025 11; No. 8 T10C 040 17
istex:1C7A9971F377CED2CF7FDD5FDDC52599B994468E
ArticleID:CEM851
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
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ISSN:0886-9383
1099-128X
DOI:10.1002/cem.851