Bayesian approach to spectrophotometric analysis of multicomponent substances

Cezary Niedziński , R.Z. Morawski


The spectrophotometric analysis of a chemical substance is based on the interpretation of the measurement data acquired by means of a spectrophotometer, i.e., on estimation of the concentrations of its components. In this paper, a Bayesian approach to the estimation of those concentrations is proposed. Its effective application requires a considerable amount of statistical a priori information, viz., the probability density functions characterizing the distributions of the concentrations, of the errors in the data, and of the residual components in the analyzed substance whose concentrations are not estimated. The proposed approach is studied using synthetic data generated on the basis of some real-world reference spectra. The results of study are compared with those obtained by means of the currently used method for estimation of concentrations, viz., constrained least-squares curve fitting
Author Cezary Niedziński (FEIT / RE)
Cezary Niedziński,,
- The Institute of Radioelectronics
, R.Z. Morawski - [IEEE]
R.Z. Morawski,,
Journal seriesIEEE Transactions On Instrumentation And Measurement, ISSN 0018-9456
Issue year2000
Keywords in Englishabsorbance spectra, Bayesian approach, Bayesian methods, Bayes methods, Biochemical analysis, Biomedical Engineering, chemical analysis, Chemistry, curve fitting, data processing, deconvolution, electrochemical impedance spectroscopy, Information analysis, MAP estimation, Mass spectroscopy, maximum likelihood estimation, multicomponent substances, parameter estimation, probability, probability density functions, real-world reference spectra, residual components, spectrochemical analysis, spectrophotometric analysis, spectrophotometry, spectroscopy computing, statistical a priori information
ASJC Classification2208 Electrical and Electronic Engineering; 3105 Instrumentation
Score (nominal)25
Score sourcejournalList
Publication indicators Scopus Citations = 2; Scopus SNIP (Source Normalised Impact per Paper): 2000 = 0.861; WoS Impact Factor: 2006 = 0.572 (2) - 2007=0.952 (5)
Citation count*4 (2020-09-11)
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