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SCIENCE CHINA Information Sciences, Volume 59, Issue 11: 112204(2016) https://doi.org/10.1007/s11432-016-0280-9

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

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  • ReceivedApr 27, 2016
  • AcceptedMay 10, 2016
  • PublishedOct 14, 2016

Abstract

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.


Acknowledgment

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Grant No. 61403319), Fujian Natural Science Foundation (Grant No. 2015J05131), Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, and Fundamental Research Funds for the Central Universities.


References

[1] Kolosova A, Saeger S, Sibanda L, et al. Development of a colloidal gold-based lateral-flow immunoassay for the rapid simultaneous detection of zearalenone and deoxynivalenol. Anal Bioanal Chem, 2007, 389: 2103-2107 CrossRef Google Scholar

[2] Laderman E, Whitworth E, Dumaual E, et al. Rapid, sensitive, and specific lateral-flow immunochromatographic point-of-care device for detection of herpes simplex virus type 2-specific immunoglobulin G antibodies in serum and whole blood. Clin Vaccine Immunol, 2008, 5: 159-163 Google Scholar

[3] Raphael C, Harley Y. Lateral Flow Immunoassay. New York: Humana Press, 2008. Google Scholar

[4] Gillespie J, Gannot G, Tangrea M, et al. Molecular profiling of cancer. Toxicol Pathol, 2004, 32: 67-71 CrossRef Google Scholar

[5] Huang S, Wei H, Lee Y. One-step immunochro-matographic assay for the detection of Staphylococcus aureus. Food Control, 2007, 18: 893-897 CrossRef Google Scholar

[6] Lundblad R, Wagner P. The potential of proteomics in developing diagnostics. IVD Tech, 2005, 3: 20-22 Google Scholar

[7] Zhang G, Wang X, Zhi A, et al. Development of a lateral flow immunoassay strip for screening of sulfamonomethoxine residues. Food Addit Contam A, 2008, 25: 413-423 CrossRef Google Scholar

[8] Zhu J, Chen W, Lu Y, et al. Development of an immunochromatographic assay for the rapid detection of bromoxynil in water. Environ Pollut, 2008, 156: 136-142 CrossRef Google Scholar

[9] Chuang L, Hwang J, Chang H, et al. Rapid and simple quantitative measurement of a-fetoprotein by combining immunochromatographic strip test and artificial neural network image analysis system. Cli Chim Acta, 2004, 348: 87-93 CrossRef Google Scholar

[10] Kaur J, Singh K, Boro R, et al. Immunochromatographic dipstick assay format using gold nanoparticles labeled protein-hapten conjugate for the detection of atrazine. Environ Sci Tech, 2007, 41: 5028-5036 CrossRef Google Scholar

[11] Li D, Wei S, Yang H, et al. A sensitive immunochromatographic assay using colloidal gold-antibody probe for rapid detection of pharmaceutical indomethacin in water samples. Biosens Bioelectron, 2009, 24: 2277-2280 CrossRef Google Scholar

[12] Tanaka R, Yuhi T, Nagatani N, et al. A novel enhancement assay for immunochromatographic test strips using gold nanoparticles. Anal Bioanal Chem, 2006, 385: 1414-1420 CrossRef Google Scholar

[13] Du M, Fang Z, Fei H. Application of photoelectric sensor to quantitative determination of immunochro-matographic assay strip. Chin J Sci Instr, 2005, 36: 671-673 Google Scholar

[14] Faulstich K, Gruler R, Eberhard M, et al. Developing rapid mobile POC systems. Part 1: devices and applications for lateral-flow immunodiagnostics. IVD Tech, 2007, 13: 47-53 Google Scholar

[15] Huang L, Zhang Y, Xie C, et al. Research of reflectance photometer based on optical absorption. Optik, 2010, 121: 1725-1728 CrossRef Google Scholar

[16] Li J, Ouellette A, Giovangrandi L, et al. Optical scanner for immunoassays with up-converting phosphorescent labels. IEEE Trans Bio-med Eng, 2008, 55: 1560-1571 CrossRef Google Scholar

[17] Li Y R, Zeng N, Du M. Study on the methodology of quantitative gold immunochromatographic strip assay. In: Proceedings of International Workshop on Intelligent Systems and Application, Wuhan, 2010. 182--185. Google Scholar

[18] Zeng N, Hung Y, Li Y, et al. A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay. Expert Syst Appl, 2014, 41: 1708-1715 CrossRef Google Scholar

[19] Zeng N, Wang Z, Zineddin B, et al. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Trans Med Imaging, 2014, 33: 1129-1136 CrossRef Google Scholar

[20] Qian S, Haim H. A mathematical model of lateral flow bioreactions applied to sandwich assays. Anal Biochem, 2003, 322: 89-98 CrossRef Google Scholar

[21] Qian S, Haim H. Analysis of lateral flow biodetectors: competitive format. Anal Biochem, 2004, 326: 211-224 CrossRef Google Scholar

[22] Zeng N, Wang Z, Li Y, et al. Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering. IEEE Trans Bio-med Eng, 2011, 58: 1959-1966 CrossRef Google Scholar

[23] Zeng N, Wang Z, Li Y, et al. Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach. IEEE Trans Nanotechnol, 2012, 11: 321-327 CrossRef Google Scholar

[24] Zeng N, Wang Z, Li Y, et al. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models. IEEE ACM Trans Comput Biol, 2012, 9: 321-329 CrossRef Google Scholar

[25] Zeng N, Wang Z, Li Y, et al. Time series modeling of nano-gold immunochromatographic assay via expectation maximization algorithm. IEEE Trans Bio-med Eng, 2013, 60: 3418-3424 CrossRef Google Scholar

[26] Zeng N, Wang Z, Zhang H, et al. A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cogn Comput, 2016, 8: 143-152 CrossRef Google Scholar

[27] Quach M, Brunel N, d'Alché-Buc F. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics, 2007, 23: 3209-3216 CrossRef Google Scholar

[28] Xiong K, Chan C, Zhang H. Detection of satellite attitude sensor faults using the UKF. IEEE Trans Aero Elec Syst, 2007, 43: 480-491 CrossRef Google Scholar

[29] Giannitrapani A, Ceccarelli N, Scortecci F, et al. Comparison of EKF and UKF for spacecraft localization via angle measurements. IEEE Trans Aero Electron Syst, 2011, 47: 75-84 CrossRef Google Scholar

[30] Lei M, Han C. Sequential nonlinear tracking using UKF and raw range-rate measurements. IEEE Trans Aero Electron Syst, 2007, 43: 239-250 CrossRef Google Scholar

[31] Li W, Wei G, Han F, et al. Weighted average consensus-based unscented Kalman filtering. IEEE Trans Cybernetics, 2016, 46: 558-567 CrossRef Google Scholar

[32] Meng W, Chen X, Li C, et al. UKF-based iterative channel estimation using two-dimensional block spread coding for uplink transmission in multicarrier CDMA networks. IEEE Trans Veh Tech, 2013, 62: 4444-4457 CrossRef Google Scholar

[33] Wu N, Li B, Wang H, et al. Distributed cooperative localization based on Gaussian message passing on factor graph in wireless networks. Sci China Inf Sci, 2015, 58: 042305-4457 Google Scholar

[34] Xue M F, Li X P, Fu L Z, et al. X-ray pulsar-based navigation using pulse phase and Doppler frequency measurements. Sci China Inf Sci, 2015, 58: 122202-4457 Google Scholar

[35] Sun X, Jin L, Xiong M. Extended Kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks. PLoS ONE, 2008, 3: e3758-4457 CrossRef Google Scholar

[36] Xu B, Zhu H, Ji W. State estimation of bearingless permanent magnet synchronous motor using improved UKF. In: Proceedings of the 31st Chinese Control Conference, Hefei, 2012. 4430--4433. Google Scholar

[37] Ljung L. System Identification: Theory for the User. 2nd ed. Upper Saddle River: Prentice-Hall, 1999. Google Scholar

[38] Wang Z, Yang F, Ho D, et al. Stochastic dynamic modeling of short gene expression time series data. IEEE Trans Nanobiosci, 2008, 7: 44-55 CrossRef Google Scholar

[39] Hou N, Dong H, Wang Z, et al. Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing, 2016, 179: 238-245 CrossRef Google Scholar

[40] Liu Y, Liu W, Obaid M, et al. Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays. Neurocomputing, 2016, 177: 409-415 CrossRef Google Scholar

[41] Li Q, Shen B, Liu Y, et al. Event-triggered H infinity state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays. Neurocomputing, 2016, 174: 912-920 CrossRef Google Scholar

[42] Liu S, Wei G, Song Y, et al. Error-constrained reliable tracking control for discrete time-varying systems subject to quantization effects. Neurocomputing, 2016, 174: 897-905 CrossRef Google Scholar

[43] Luo Y, Wei G, Liu Y, et al. Reliable H-infinity state estimation for 2-D discrete systems with infinite distributed delays and incomplete observations. Int J Gen Syst, 2015, 44: 155-168 CrossRef Google Scholar

[44] Liu Y, Alsaadi F, Yin X, et al. Robust H-infinity filtering for discrete nonlinear delayed stochastic systems with missing measurements and randomly occurring nonlinearities. Int J Gen Syst, 2015, 44: 169-181 CrossRef Google Scholar

[45] Yu Y, Dong H, Wang Z, et al. Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties. Neucomputing, 2016, 182: 18-24 CrossRef Google Scholar

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