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

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.概要创新点1) 本文建立的非线性状态空间模型由生化反应系统方程和观测方程组成. 特别地, 模型还考虑了存在于各个反应中的时滞现象. 2) 基于获取的短时间序列数据, 无迹卡尔曼滤波能够同时准确辨识出模型中的状态及参数. 3) 该模型可观察和预测试条的免疫反应动态特性, 并能够辅助优化试条的定量特性.