1994
DOI: 10.1016/0003-2670(94)00362-9
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Systematic comparison of data-processing options for kinetic-based single-component determinations of non-catalysts

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Cited by 16 publications
(1 citation statement)
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“…Curve-fitting methods for multicomponent kinetic analysis are based on the preliminary postulation of a chemical model (hard-modeling methods), for instance, through the order and the stoichiometry of the reaction. The experimental data (a data vector or a data matrix) are then fitted to the proposed model, and the best set of parameters is obtained by using a least-squares optimization. The Kalman filter procedure has been also widely used in the analysis of kinetic data, allowing the estimation of a set of optimized parameters that describe the system under the assumption of a certain kinetic model. Depending on the model to be fitted, either linear or nonlinear (extended) Kalman filters can be used. The Kalman filter approach has been successfully applied to the simultaneous determination of several analytes .…”
mentioning
confidence: 99%
“…Curve-fitting methods for multicomponent kinetic analysis are based on the preliminary postulation of a chemical model (hard-modeling methods), for instance, through the order and the stoichiometry of the reaction. The experimental data (a data vector or a data matrix) are then fitted to the proposed model, and the best set of parameters is obtained by using a least-squares optimization. The Kalman filter procedure has been also widely used in the analysis of kinetic data, allowing the estimation of a set of optimized parameters that describe the system under the assumption of a certain kinetic model. Depending on the model to be fitted, either linear or nonlinear (extended) Kalman filters can be used. The Kalman filter approach has been successfully applied to the simultaneous determination of several analytes .…”
mentioning
confidence: 99%