A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones
The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strip...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/14/6401 |
| _version_ | 1797587511197827072 |
|---|---|
| author | Shenglan Zhang Xincheng Jiang Siqi Lu Guangtian Yang Shaojie Wu Liqiang Chen Hongcheng Pan |
| author_facet | Shenglan Zhang Xincheng Jiang Siqi Lu Guangtian Yang Shaojie Wu Liqiang Chen Hongcheng Pan |
| author_sort | Shenglan Zhang |
| collection | DOAJ |
| description | The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm’s sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity. |
| first_indexed | 2024-03-11T00:39:58Z |
| format | Article |
| id | doaj.art-529b2976bd624136930f2ee1c811482d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:39:58Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-529b2976bd624136930f2ee1c811482d2023-11-18T21:17:13ZengMDPI AGSensors1424-82202023-07-012314640110.3390/s23146401A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in SmartphonesShenglan Zhang0Xincheng Jiang1Siqi Lu2Guangtian Yang3Shaojie Wu4Liqiang Chen5Hongcheng Pan6Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaSchool of Information Science and Engineering, Guilin University of Technology, Guilin 541006, ChinaGuangxi Key Laboratory of Electrochemical and Magneto-Chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaGuangxi Key Laboratory of Electrochemical and Magneto-Chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541004, ChinaThe traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm’s sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.https://www.mdpi.com/1424-8220/23/14/6401lateral flow immunoassayimmunosensorsmachine visionsupport vector machinesmartphone application |
| spellingShingle | Shenglan Zhang Xincheng Jiang Siqi Lu Guangtian Yang Shaojie Wu Liqiang Chen Hongcheng Pan A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones Sensors lateral flow immunoassay immunosensors machine vision support vector machine smartphone application |
| title | A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones |
| title_full | A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones |
| title_fullStr | A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones |
| title_full_unstemmed | A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones |
| title_short | A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones |
| title_sort | quantitative detection algorithm for multi test line lateral flow immunoassay applied in smartphones |
| topic | lateral flow immunoassay immunosensors machine vision support vector machine smartphone application |
| url | https://www.mdpi.com/1424-8220/23/14/6401 |
| work_keys_str_mv | AT shenglanzhang aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT xinchengjiang aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT siqilu aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT guangtianyang aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT shaojiewu aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT liqiangchen aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT hongchengpan aquantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT shenglanzhang quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT xinchengjiang quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT siqilu quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT guangtianyang quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT shaojiewu quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT liqiangchen quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones AT hongchengpan quantitativedetectionalgorithmformultitestlinelateralflowimmunoassayappliedinsmartphones |