In　this　paper,　the　particle　filtering　approach　is　used,　together　with　the　kernel　smoothing　method,　to　identify　the　state-space　model　for　the　lateral　flow　immunoassay　through　available　but　short　time-series　measurement.　The　lateral　flow　immunoassay　model　is　viewed　as　a　nonlinear　dynamic　stochastic　model　consisting　of　the　equations　for　the　biochemical　reaction　system　as　well　as　the　measurement　output.　The　renowned　extended　Kalman　filter　is　chosen　as　the　importance　density　of　the　particle　filter　for　the　purpose　of　modeling　the　nonlinear　lateral　flow　immunoassay.　By　using　the　developed　particle　filter,　both　the　states　and　parameters　of　the　nonlinear　state-space　model　can　be　identified　simultaneously.　The　identified　model　is　of　fundamental　significance　for　the　development　of　lateral　flow　immunoassay　quantification.　It　is　shown　that　the　proposed　particle　filtering　approach　works　well　for　modeling　the　lateral　flow　immunoassay.