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Title: Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering
Authors: Zeng, N
Wang, Z
Li, Y
Du, M
Liu, X
Keywords: Extended Kalman filtering;Gold immunochromatographic strip;Lateral flow immunoassay;Mathematical model;Parameter estimation
Issue Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on Biomedical Engineering, Forthcoming, Jan 2011
Abstract: In this paper, a mathematical model for sandwichtype lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extend Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also inspecting the effects from various design parameters in a both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes of the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize and design the properties of lateral flow immunoassay devices.
Description: Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
metadata.dc.relation.isreplacedby: 2438/5745
ISSN: 0018-9294
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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