Development of a Low-Cost Fluorescence Analyzer Based on Software Filtering for Reading Fluorescence Strips
Lateral flow assays (LFA) are paper-based rapid detection technologies that enable efficient and convenient quantitative or qualitative analysis of samples in a very short period. This research focuses on the development of a low-cost fluorescence analyzer with a unique design that successfully achieves high-precision quantitative reading of fluorescence lateral immunochromatography strips, eliminating the traditional requirements for hardware filtering, electromagnetic shielding, and other complex technologies. The critical role of Fast Fourier Transform (FFT) filter technology in processing weak positive test strip signals is thoroughly examined. Additionally, we have developed a comprehensive and reliable fluorescence characteristic peak recognition method, effectively distinguishing and quantifying the fluorescence signal on the test strip, providing robust technical support for accurate diagnosis. Tuberculosis samples were selected as test objects in this study. Comparative experiments demonstrated that the performance of the low-cost fluorescence analyzer is comparable to that of high-end commercial equipment, such as the RTR-FS100 gold standard instrument, thereby confirming its technical feasibility and application potential. The core contribution of this paper is the effective reduction of the analyzer's reliance on expensive hardware components through software algorithm optimization strategies, which significantly lowers production costs. This achievement not only paves the way for the popularization and application of immunochromatography technology but also provides an important reference for the research and development of low-cost and portable medical detection equipment, showcasing broad application prospects and social value.
Authors
- Jinlai Yuan
- Fang Li (ORCID: https://orcid.org/0000-0002-5644-7683)
- Zhifeng Wu
- Yuliang Feng (ORCID: https://orcid.org/0009-0002-9227-9717)
Institutions
- Beijing University of Chemical Technology (CN)
Publication Details
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1109/cisp-bmei64163.2024.10906202
- Primary Topic
- Advanced Computing and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China