Cargando…

Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection met...

Descripción completa

Detalles Bibliográficos
Autores principales: Foysal, Kamrul H., Seo, Sung Eun, Kim, Min Ju, Kwon, Oh Seok, Chong, Jo Woon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864604/
https://www.ncbi.nlm.nih.gov/pubmed/31694281
http://dx.doi.org/10.3390/s19214812
_version_ 1783471920780410880
author Foysal, Kamrul H.
Seo, Sung Eun
Kim, Min Ju
Kwon, Oh Seok
Chong, Jo Woon
author_facet Foysal, Kamrul H.
Seo, Sung Eun
Kim, Min Ju
Kwon, Oh Seok
Chong, Jo Woon
author_sort Foysal, Kamrul H.
collection PubMed
description Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.
format Online
Article
Text
id pubmed-6864604
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-68646042019-12-23 Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone Foysal, Kamrul H. Seo, Sung Eun Kim, Min Ju Kwon, Oh Seok Chong, Jo Woon Sensors (Basel) Article Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time. MDPI 2019-11-05 /pmc/articles/PMC6864604/ /pubmed/31694281 http://dx.doi.org/10.3390/s19214812 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Foysal, Kamrul H.
Seo, Sung Eun
Kim, Min Ju
Kwon, Oh Seok
Chong, Jo Woon
Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title_full Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title_fullStr Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title_full_unstemmed Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title_short Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
title_sort analyte quantity detection from lateral flow assay using a smartphone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864604/
https://www.ncbi.nlm.nih.gov/pubmed/31694281
http://dx.doi.org/10.3390/s19214812
work_keys_str_mv AT foysalkamrulh analytequantitydetectionfromlateralflowassayusingasmartphone
AT seosungeun analytequantitydetectionfromlateralflowassayusingasmartphone
AT kimminju analytequantitydetectionfromlateralflowassayusingasmartphone
AT kwonohseok analytequantitydetectionfromlateralflowassayusingasmartphone
AT chongjowoon analytequantitydetectionfromlateralflowassayusingasmartphone