Quantitative Detection Of Immunochromatographic Strips Based On COMSOL Mnltiphysics And Image Processing

Lateral flow immunoassay is an immunoassay technique based on antigen antibody specific reaction and chromatography.It is widely used in many fields,such as medicine,environmental detection,food supervision,animal husbandry,because of its simplity,rapidity,high sensitivity,strong specificity,easy manipulation,etc.In the current application,affecting by the detection environment,process and results of visual observation,the immunoassay detection has large errors,so it is only suitable for qualitative or semi-quantitative detection.However the qualitative or semi-quantitative detection results provide less information than demanded,which greatly limits the further promotion of immunochromatographic technology.Because it can provide more accurate and comprehensive information,the quantitative detection of immunochromatography has been welcomed in various fields,as has become the inevitable trend of the development of immunochromatography.Therefore,aiming at solving the problems existing in the clinical application and popularization of quantitative detection of immunochromatographic strips,in this paper we studied the quantitative detection of immunochromatographic strips.We divide the method into two kinds:sandwich method and competition method according to the difference between the detected substances.The models combined the convection diffusion equation and the fluid dynamics,and are established and simulated by COMSOL software respectively,according to the mechanism of the dynamic reaction process.In the molding and simulation,we analyzed the effects of the initial concentration of material,nitrocellulose membrane material,the porosity and the position of the detection line on the test results.The simulation results are consistent with the results of the final model.The construction and simulation results of this model are designed to help researchers to design and optimize the test strips through fast and low cost methods,which can create the conditions for improving the detection sensitivity and quantitative detection performance.At the same time,this paper realizes the quantitative detection of immunochromatographic strips based on image segmentation algorithm.In view of the characteristics of the test strips image and the characteristics of the FCM clustering algorithm,this paper optimizes the FCM algorithm by using the PSO algorithm with strong global search ability to make it more accurate and effective to extract the test line and quality control line.That is based on the FCM clustering algorithm is sensitive to the initial value and easy to fall into the local minimum,and its other defects which may impact the image segmentation.The peak signal-to-noise ratio index is used to evaluate the image quality after segmentation.The value of the relative density(RIOD)is taken as the characteristic quantity of quantitative detection.