Fast on-line data evaluation of flow-injection analysis signals based on parameter estimation by an extended Kalman filter

verfasst von: X. Wu, K. H. Bellgardt
Abstract: The present paper is concerned with the fast evaluation of the flow injection analysis (FIA) signals and the automatic correction of the analytical values interfered by systematic and stochastic disturbances. With the application of the extended Kalman filter, the highest amount of information for the data evaluation of analytical signals can be extracted from FIA peaks. The concentration of the analyte and the offset of the baseline are estimated as time-variable parameters by filtering. The results of the application to simulated and real FIA data show that the parameters corresponded to the evaluation of the analytical data, but is already available before the maximum of the FIA peak, i.e. the evaluation of the data with the Kalman filter can be done during the running peak. Good estimates of the measured values are already obtained short after start of the peak. For this reason, the measuring dead-time of the FIA system can be reduced by the use of this method. The evaluation of FIA signals disturbed often by fluctuation of the baseline and other noise can be corrected by estimation of the offset of FIA peaks and smoothing of real FIA signals. Therefore, the application of the extended Kalman filter can also improve conventional evaluation methods. Copyright (C) 1998 Elsevier Science B.V.
Organisationseinheit(en): Institut für Technische Chemie
Typ: Artikel
Journal: Journal of biotechnology
Band: 62
Seiten: 11-28
Anzahl der Seiten: 18
ISSN: 0168-1656
Publikationsdatum: 11.06.1998
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Biotechnologie, Bioengineering, Angewandte Mikrobiologie und Biotechnologie
Elektronische Version(en): https://doi.org/10.1016/S0168-1656(98)00039-X (Zugang: Geschlossen)

BibTeX

@article{cdaa2260e87b4b538a82f5d2017ecbf4,
title = "Fast on-line data evaluation of flow-injection analysis signals based on parameter estimation by an extended Kalman filter",
abstract = "The present paper is concerned with the fast evaluation of the flow injection analysis (FIA) signals and the automatic correction of the analytical values interfered by systematic and stochastic disturbances. With the application of the extended Kalman filter, the highest amount of information for the data evaluation of analytical signals can be extracted from FIA peaks. The concentration of the analyte and the offset of the baseline are estimated as time-variable parameters by filtering. The results of the application to simulated and real FIA data show that the parameters corresponded to the evaluation of the analytical data, but is already available before the maximum of the FIA peak, i.e. the evaluation of the data with the Kalman filter can be done during the running peak. Good estimates of the measured values are already obtained short after start of the peak. For this reason, the measuring dead-time of the FIA system can be reduced by the use of this method. The evaluation of FIA signals disturbed often by fluctuation of the baseline and other noise can be corrected by estimation of the offset of FIA peaks and smoothing of real FIA signals. Therefore, the application of the extended Kalman filter can also improve conventional evaluation methods. Copyright (C) 1998 Elsevier Science B.V.",
keywords = "Data processing, Extended Kalman filter, Flow injection, On-line analysis, Parameter estimation, State space model",
author = "X. Wu and Bellgardt, {K. H.}",
note = "Funding Information: This work was financially supported by the `Bundes Ministerium f{\"u}r Forschung und Technologie (BMFT)', Biotechnology program (No. BEO 0310087A) and by the `Arbeitsgemainschaft Industrieller Forschungsvereinigungen (AIF)', (Project No. 8959). We would like to thank Dr Hitzmann, Dr Brandt and Dr Weigel who helpfully offered the experimental data for this work.",
year = "1998",
month = jun,
day = "11",
doi = "10.1016/S0168-1656(98)00039-X",
language = "English",
volume = "62",
pages = "11--28",
journal = "Journal of biotechnology",
issn = "0168-1656",
publisher = "Elsevier",
number = "1",
}