Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter

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• 作者：Nianyin Zeng ; Zidong Wang ; Hong Zhang
• 关键词：lateral flow immunoassay ; biochemical reaction networks ; modelling ; unscented Kalman filter
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：59
• 期：11
• 全文大小：456 KB
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919
• 卷排序：59

文摘

This paper is concerned with the problem of learning structure of the lateral flow immunoassay (LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize the unscented Kalman filter (UKF) algorithm to simultaneously identify not only the states but also the parameters of the improved state-space model by using short but high-dimensional experiment data in terms of images. It is shown via experiment results that the UKF approach is particularly suitable for modelling the LFIA devices. The identified model with time-delay is of great significance for the quantitative analysis of LFIA in both the accurate prediction of the dynamic process of the concentration distribution of the antigens/antibodies and the performance optimization of the LFIA devices.