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IQVision: An Image-Based Evaluation Tool for Quantitative Lateral Flow Immunoassay Kits

The development of quantitative lateral flow immunoassay test strips involves a lot of research from kit manufacturers’ standpoint. Kit providers need to evaluate multiple parameters, including the location of test regions, sample flow speed, required sample volumes, reaction stability time, etc. A...

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Detalles Bibliográficos
Autores principales: Bheemavarapu, Lalitha Pratyusha, Shah, Malay Ilesh, Joseph, Jayaraj, Sivaprakasam, Mohanasankar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428085/
https://www.ncbi.nlm.nih.gov/pubmed/34203515
http://dx.doi.org/10.3390/bios11070211
Descripción
Sumario:The development of quantitative lateral flow immunoassay test strips involves a lot of research from kit manufacturers’ standpoint. Kit providers need to evaluate multiple parameters, including the location of test regions, sample flow speed, required sample volumes, reaction stability time, etc. A practical visualization tool assisting manufacturers in this process is very much required for the design of more sensitive and reliable quantitative LFIA test strips. In this paper, we present an image-based quantitative evaluation tool determining the practical functionality of fluorescence-labelled LFIA test cartridges. Image processing-based algorithms developed and presented in this paper provide a practical analysis of sample flow rates, reaction stability times of samples under test, and detect any abnormalities in test strips. Evaluation of the algorithm is done with Glycated Hemoglobin (HbA1C) and Vitamin D test cartridges. Practical sample flow progress for HbA1C test cartridges is demonstrated. The reaction stability time of HbA1C test samples is measured to be 12 min, while that of Vitamin D test samples is 24 min. Experimental evaluation of the abnormality detection algorithm is carried out, and sample flow abnormalities are detected with 100% accuracy while membrane irregularities are detected with 96% accuracy.