## 2 Materials and Methods  
### Experimental Materials and Instruments  
In normal human conditions, the concentration of serum amyloid A protein (SAA) is typically below 10 ug/mL. However, during inflammatory infections, SAA concentration can increase by 1000 times within the acute phase response (APR) period of 24 to 72 h. Given this characteristic, SAA was selected as the research subject for this study's experiments. Lateral flow immunoassay quantitative detection requires materials and instruments that typically include the following: immunoassay test strips consist of a paper base, a probe, labeling agents, and other components. As shown in Figure 1, these strips utilize chemical reactions to convert target substances into visible color signals. Samplepreparation involved using bovine serum albumin (BSA), which was purchased from Hefei Bomei Biotechnology Co. (Hefei, China) The SAA monoclonal antibody pair and SAA antigen used for labeling and capturing were obtained from Wuhan Huamei Biological Engineering Co. (Wuhan, China). Colloidal gold (25 nm) was synthesized in the laboratory, and glass cellulose membranes and polyvinyl chloride backing plates (PVC plates) were purchased from Shanghai Jining Biotechnology Co. (Shanghai, China).  
In this study, we conducted experiments using SAA test strips. We prepared samples with different concentrations by diluting the standard solution 700 times. The concentrations included 0, 5, 10, 30, 50, 80, 100, 300, 500, 800, 1000, 1300, and 1500 \(\upmu\)g/mL, totaling 260 samples. Each concentration had 20 samples. We chose such a wide concentration gradient to have more data points for better curve fitting and calibration when the samples' color changed drastically. An example of samples at different concentrations is shown in Figure 2. The experiments were conducted at a temperature of 25 \({}^{\circ}\)C and a humidity of 50%. After adding the samples, we waited for 5 min before collecting the samples for feature extraction.  
[PERSON] et al. (2021) investigated the impact of flash usage while capturing images with smartphones ([PERSON] et al., 2021). They found that enabling the flashlight resulted in a higher recognition accuracy as the built-in flashlight of smartphones reduced the impact of ambient light. However, different models of smartphone flashlights also exhibit variations, which can  
Figure 1: Test strip schematic diagram.  
Figure 2: Example of samples at different concentrations.  
introduce errors that are difficult to correct. To control the lighting conditions impacting test strip detection, we utilized a dark box to provide a stable lighting environment, thus ensuring the accuracy and reproducibility of image acquisition. The dark box shell was made of plastic material, sealed internally, and colored using black dye. It features a black frosted glass surface as the platform for placing test strips, which is characterized by a low reflection of light. A white LED light (wavelength of 450 nm, color temperature of 3000 K, power of 4.8 watts/meter, luminous flux of 450 LM, and power supply of 12 V) was installed inside the dark box to replace the smartphone flashlight. This not only reduces the influence of natural light but also addresses the issue of different smartphone flashlight models. Both the test strips and the smartphone being used were inserted into the dark box, and the structure and usage of the dark box are demonstrated in Figure 3.  
Three common smartphone models were selected for the experiments: Xiaomi 11, iPhone 12, and Huawei P30. The parameters of these three models are shown in Table 1. All three smartphones have a high camera performance and high popularity in China. Under consistent lighting, images were acquired using each smartphone to evaluate color value differences and to assess the practicality and applicability of the proposed detection method.