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Research Article | Open Access

Smartphone-Based Fluorescent Diagnostic System for Immunochromatographic Chip

Yafei Hou1Kan Wang1,2( )Mengdi Yang3Weijian Qin1Kun Xiao1Wenqiang Yan1
Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Shanghai 200240, China
Shanghai Sixth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200011, China
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Abstract

In order to achieve fast and quantitative detection of fluorescence immunochromatographic chip, a rapid detection system based on smartphone has been developed. In this system, fluorescent signal from quantum dots (QDs) on lateral flow test strips (LFTSs) can be accurately extracted, and the system also can calculate the concentration of the analyte. The method of extraction and recognition of fluorescence signal intensity can be applied to different fluorescent chip detection systems. Based on the fluorescence tomography chip image, a specific program is used for image acquisition, processing and data handling. The Sobel operator algorithm was used in the software,which improved greatly the ability of distinguishing between the test area and the background boundary information. Extracting the components from the red format of the fluorescent strips,the high-signal intensity and sensitivity were achieved. The simulation results show that the proposed method can be applied to the detection system of fluorescence immunochromatographic chip. The experimental results show that the signal intensity has a good correlation with the concentration of immunoassay, which indicates the detection system can extract the intensity of fluorescence signal of the chip.

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Nano Biomedicine and Engineering
Pages 21-26
Cite this article:
Hou Y, Wang K, Yang M, et al. Smartphone-Based Fluorescent Diagnostic System for Immunochromatographic Chip. Nano Biomedicine and Engineering, 2017, 9(1): 21-26. https://doi.org/10.5101/nbe.v9i1.p21-26

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Received: 16 March 2017
Accepted: 21 March 2017
Published: 21 March 2017
© 2017 Yafei Hou, Kan Wang, Mengdi Yang, Weijian Qin, Kun Xiao, and Wenqiang Yan.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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