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A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay

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Abstract

In this paper, the parameter identification problem of the lateral flow immunoassay (LFIA) devices is investigated via a new switching delayed particle swarm optimization (SDPSO) algorithm. By evaluating an evolutionary factor in each generation, the velocity of the particle can adaptively adjust the model according to a Markov chain in the proposed SDPSO method. During the iteration process, the SDPSO can adaptively select the inertia weight, acceleration coefficients, locally best particle pbest and globally best particle gbest in the swarm. It is worth highlighting that the pbest and the gbest can be randomly selected from the corresponding values in the previous iteration. That is, the delayed information of the pbest and the gbest can be exploited to update the particle’s velocity in current iteration according to the evolutionary states. The strategy can not only improve the global search but also enhance the possibility of eventually reaching the gbest. The superiority of the proposed SDPSO is evaluated on a series of unimodal and multimodal benchmark functions. Results demonstrate that the novel SDPSO algorithm outperforms some well-known PSO algorithms in aspects of global search and efficiency of convergence. Finally, the novel SDPSO is successfully exploited to estimate the unknown time-delay parameters of a class of nonlinear state-space LFIA model.

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References

  1. Abdullah A, Deris S, Hashim S, Mohamad M, Arjunan S. An improved local best searching in particle swarm optimization using differential evolution, In: 11th international conference on hybrid intelligent systems pp. 115–120, 2011.

  2. Clerc M, Kennedy J. The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput. 2002;6(1):58–73.

    Article  Google Scholar 

  3. Ding D, Wang Z, Shen B, Dong H. Event-triggered distributed \(H_{\infty }\) state estimation with packet dropouts through sensor networks. IET Control Theory Appl. 2015;9(13):1948–55.

    Article  Google Scholar 

  4. Ding D, Wang Z, Alsaadi FE, Shen B. Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements. Int J Gen Syst. 2015;44(2):198–211.

    Article  Google Scholar 

  5. Ding D, Wang Z, Shen B, Wei G. Event-triggered consensus control for discrete-time stochastic multi-agent systems: the input-to-state stability in probability. Automatica. 2015;62:284–91.

    Article  Google Scholar 

  6. Ding D, Wang Z, Lam J, Shen B. Finite-Horizon \(H_{\infty }\) control for discrete time-varying systems with randomly occurring nonlinearities and fading measurements. IEEE Trans Autom Control. 2016;60(9):2488–93.

    Article  Google Scholar 

  7. Ding D, Wang Z, Shen B, Dong H. \(H_{\infty }\) state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays. Int J Robust Nonlinear Control. 2015;25(13):2180–95.

    Google Scholar 

  8. Gillespie J, Gannot G, Tangrea M, Ahram M, Best C, Bichsel V, Petricoin E, Emmert-Buck M, Chuaqui R. Molecular profiling of cancer. Toxicol Pathol. 2004;32:67–71.

    Article  CAS  PubMed  Google Scholar 

  9. Hou N, Dong H, Wang Z, Ren W, Alsaadi FE. Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing. 2016;179:238–45.

    Article  Google Scholar 

  10. Huang S, Wei H, Lee Y. One-step immunochro-matographic assay for the detection of Staphylococcus aureus. Food Control. 2007;18(8):893–7.

    Article  CAS  Google Scholar 

  11. Kaur J, Singh K, Boro R, Thampi K, Raje M, Varshney G. Immunochromatographic dipstick assay format using gold nanoparticles labeled protein-hapten conjugate for the detection of atrazine. Environ Sci Technol. 2007;41(14):5028–36.

    Article  CAS  PubMed  Google Scholar 

  12. Kennedy J, Eberhart R. Particle swarm optimization, In: Proceedings of IEEE international conference on neural network pp. 1942–1948, 1995.

  13. Laderman E, Whitworth E, Dumaual E, Jones M, Hudak A, Hogrefe W, Carney J, Groen J. 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;5:159–63.

    Article  Google Scholar 

  14. Lundblad R, Wagner P. The potential of proteomics in developing diagnostics. IVD Technol. 2005;3:20–2.

    Google Scholar 

  15. Li D, Wei S, Yang H, Li Y, Deng A. A sensitive immunochromatographic assay using colloidal gold-antibody probe for rapid detection of pharmaceutical indomethacin in water samples. Biosens Bioelectron. 2009;24(7):2277–80.

    Article  CAS  PubMed  Google Scholar 

  16. Liu Y, Alsaadi FE, Yin X, Wang Y. Robust \(H_{\infty }\) filtering for discrete nonlinear delayed stochastic systems with missing measurements and randomly occurring nonlinearities. Int J Gen Syst. 2015;44(2):169–81.

    Article  Google Scholar 

  17. Luo Y, Wei G, Liu Y, Ding X. Reliable \(H_{\infty }\) state estimation for 2-D discrete systems with infinite distributed delays and incomplete observations. Int J Gen Syst. 2015;44(2):155–68.

    Article  Google Scholar 

  18. Qian S, Haim H. A mathematical model of lateral flow bioreactions applied to sandwich assays. Anal Biochem. 2003;322(1):89–98.

    Article  CAS  PubMed  Google Scholar 

  19. Qian S, Haim H. Analysis of lateral flow biodetectors: competitive format. Anal Biochem. 2004;326(2):211–24.

    Article  CAS  PubMed  Google Scholar 

  20. Raphael C, Harley Y. Lateral flow immunoassay. New York: Humana Press; 2008.

    Google Scholar 

  21. Ratnaweera A, Halgamure SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8:240–55.

    Article  Google Scholar 

  22. Shi Y, Eberhart RC. Empirical study of particle swarm optimization. In: Proceedings of the 1999 IEEE congress on evolutionary computation, pp. 1945–1950, 1999.

  23. Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. In: Proceedings of the 7th international conference on evolutionary programming, pp. 591–600, 1998.

  24. Tanaka R, Yuhi T, Nagatani N, Endo T, Kerman K, Takamura Y. A novel enhancement assay for immunochromatographic test strips using gold nanoparticles. Anal Bioanal Chem. 2006;385(8):1414–20.

    Article  CAS  PubMed  Google Scholar 

  25. Tang Y, Wang Z, Fang J. Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm. Expert Syst Appl. 2011;38:2523–35.

    Article  Google Scholar 

  26. Tang YG, Guan X. Parameter estimation for time-delay chaotic system by particle swarm optimization. Chaos Solitons Fractals. 2009;40(3):1391–8.

    Article  Google Scholar 

  27. Valle Y, Venayagamoorthy G, Mohagheghi S, Hernandez J, Harley R. Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput. 2008;12(2):171–95.

    Article  Google Scholar 

  28. Yang H, Wang Z, Shu H, Alsaadi FE, Hayat T. Almost sure \(H_{\infty }\) sliding mode control for nonlinear stochastic systems with Markovian switching and time-delays. Neurocomputing. 2016;175:392–400.

    Article  Google Scholar 

  29. Yu Y, Dong H, Wang Z, Ren W, Alsaadi FE. Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties. Neurocomputing. 2016;182:18–24.

    Article  Google Scholar 

  30. Zeng N, Wang Z, Li Y, Du M, Liu X. Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach. IEEE Trans Nanotechnol. 2012;11(2):321–7.

    Article  Google Scholar 

  31. Zeng N, Wang Z, Li Y, Du M, Liu X. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):321–9.

    Article  PubMed  Google Scholar 

  32. Zeng N, Wang Z, Li Y, Du M, Liu X. Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering. IEEE Trans Biomed Eng. 2011;58(7):1959–66.

    Article  PubMed  Google Scholar 

  33. Zeng N, Wang Z, Li Y, Du M, Cao J, Liu X. Time series modeling of nano-gold immunochromatographic assay via expectation maximization algorithm. IEEE Trans Biomed Eng. 2013;60(12):3418–24.

    Article  PubMed  Google Scholar 

  34. Zeng N, Hung YS, Li Y, Du M. A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay. Expert Syst Appl. 2014;41(4):1708–15.

    Article  Google Scholar 

  35. Zeng N, Wang Z, Zineddin B, Li Y, Du M, Xiao L, Liu X, Young T. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Trans Med Imaging. 2014;33(5):1129–36.

    Article  PubMed  Google Scholar 

  36. Zhan Z, Zhang J, Li Y, Chung H. Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B. 2009;39(6):1362–81.

    Article  Google Scholar 

  37. Zhang G, Wang X, Zhi A, Bao Y, Yang Y, Qu M, Luo J, Li Q, Guo J, Wang Z, Yang J, Xing G, Chai S, Shi T, Liu Q. Development of a lateral flow immunoassay strip for screening of sulfamonomethoxine residues. Food Addit Contam Part A. 2008;25(4):413–23.

    Article  CAS  Google Scholar 

  38. Zhu J, Chen W, Lu Y, Cheng G. Development of an immunochromatographic assay for the rapid detection of bromoxynil in water. Environ Pollut. 2008;156(1):136–42.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported in part by the Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of China under Grant 61403319, the Fujian Natural Science Foundation under Grant 2015J05131, and the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, Fujian, 361005, People’s Republic of China

    Nianyin Zeng & Hong Zhang

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

  3. Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Zidong Wang & Fuad E. Alsaadi

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  1. Nianyin Zeng
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  2. Zidong Wang
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  4. Fuad E. Alsaadi
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Correspondence to Nianyin Zeng.

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Nianyin Zeng, Zidong Wang, Hong Zhang and Fuad E. Alsaadi declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Zeng, N., Wang, Z., Zhang, H. et al. A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay. Cogn Comput 8, 143–152 (2016). https://doi.org/10.1007/s12559-016-9396-6

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  • DOI: https://doi.org/10.1007/s12559-016-9396-6

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