A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones  
[PERSON]  
1 Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guanggi Zhuang Autonomous Region, Guilin 54100, China; [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (L.C. )1  
[PERSON]  
1 Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guanggi Zhuang Autonomous Region, Guilin 54100, China; [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (L.C. )1  
[PERSON]  
2 School of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China; [EMAIL_ADDRESS]. or signal change of the lines. By extracting the characteristic information and comparing it with a known concentration standard curve, the concentration of the target substance can be obtained. In recent years, researchers have increasingly studied the use of computer vision technology for immunoassay detection. However, graded LFIA strips present certain challenges, such as cross-interference [10] and peak overlap [11], which result in decreased detection accuracy and reliability. Therefore, developing efficient methods for processing the images of graded LFIA strips to improve quantitative accuracy and reliability is an important research direction and a promising way forward. Image processing technology is a powerful tool for optimizing digital images [12]. Techniques such as removal of backgrounds and noise, enhancement of detection sensitivity and specificity, and segmentation of target bands can improve the image quality and quantification of graded LFIA strips. Deep learning algorithms [13], a machine learning method, can automatically recognize different test lines on an LFIA strip and perform classification and quantitative analysis. These technologies provide new ways and methods for analyzing and detecting graded LFIA strips, thereby improving detection accuracy and reliability. However, a substantial amount of data are required to train these algorithms and ensure validity. Recent research studies have focused on developing novel technologies for the quantification of immunomodmetographic assays. For example, [PERSON] et al. (2021) developed an image segmentation method that uses gold LFIA strips for quantitative analysis [14]. This method reduces image noise caused by environmental factors, solves problems with irregular and fuzzy boundaries between the control line and the test line, and improves recognition errors caused by shallow test lines. [PERSON] et al. (2020) proposed a low-cost, highly sensitive lateral flow biosensing platform based on soluble polymer mixtures [15], which is combined with a smartphone-based reader for high-performance point-of-care testing. [PERSON] et al. (2020) integrated fluorescence lateral flow detection, detection boxes, and signal detection devices, resulting in a multi-channel LFIA quantitative reader that can be used for high-throughput screening of infectious diseases [16]. In this study, we developed an intelligent detection algorithm based on image processing techniques, with the following main contributions:  
1. A dark box was designed to reduce the impact of ambient light on image acquisition, and differences in image quality obtained with different models of smartphone were compared. 2. An intelligent algorithm was developed that utilizes binarization to identify the darker control line and detects the test line through operations such as image denoising, pixel accumulation, and convolution. 3. Machine learning methods were employed to fit the relationship between feature values and concentration, thereby validating the accuracy of the algorithm. The algorithm's effectiveness under various conditions was verified by adding noise to the images.