## 2 Materials and Methods
### Fitting Eigenvalues Versus Concentration  
As \(Y1\) represents the reading value for the four lines, it better reflects the detection performance of the algorithm for multi-line test strips. Therefore, we extracted five sets of samples for the \(Y1\) and concentration values, as shown in Table 2:  
Support vector machine is a supervised learning model used for classification and regression analysis. Its core concept centers on finding hyperplanes that map sample features to high-dimensional spaces and segmenting them correspondingly. Employing SVM allows us to establish nonlinear relationships between feature values and concentration values. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Concentration** & \multirow{2}{*}{**0**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**10**} & \multirow{2}{*}{**30**} & \multirow{2}{*}{**50**} & \multirow{2}{*}{**80**} & \multirow{2}{*}{**100**} & \multirow{2}{*}{**300**} & \multirow{2}{*}{**500**} & \multirow{2}{*}{**800**} & \multirow{2}{*}{**1000**} & \multirow{2}{*}{**1300**} & \multirow{2}{*}{**1500**} \\ (**g/mL) & & & & & & & & & & & & & \\ \hline
1 & 0 & 0.0774 & 0.1297 & 0.0873 & 0.1784 & 0.2187 & 0.4009 & 0.9596 & 1.0008 & 1.3142 & 1.4978 & 1.9040 & 1.7000 \\
2 & 0 & 0.0626 & 0.0824 & 0.1343 & 0.1438 & 0.1348 & 0.3498 & 0.9069 & 1.2781 & 1.4234 & 1.4498 & 2.0319 & 2.0939 \\
3 & 0 & 0.0927 & 0.0663 & 0.0829 & 0.1249 & 0.1857 & 0.2821 & 0.7958 & 1.3111 & 1.5778 & 1.4626 & 1.5254 & 1.6717 \\
4 & 0 & 0.0477 & 0.0367 & 0.1586 & 0.1584 & 0.1992 & 0.3389 & 0.8353 & 1.0767 & 1.3373 & 1.6317 & 1.4284 & 1.6454 \\
5 & 0 & 0.0116 & 0.0314 & 0.0967 & 0.1352 & 0.2375 & 0.6874 & 0.8599 & 0.9237 & 0.9765 & 1.1935 & 1.2317 & 1.3246 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Randomly selected five groups of sample reading values. Using SVM, we constructed an optimization problem to seek out the optimal hyperplane positioning that evenly segregates all data points while maintaining maximal distance from either side of the grouping. Data points within close proximity to the hyperplane are referred to as \"support vectors\". Their role is to collectively define the decision boundary when classifying new data. The following equation clarifies this:  
\[\min_{w,b,\xi}\frac{||w||^{2}}{2}+C\sum_{i}\xi_{i} \tag{8}\]  
Here, \(w\) represents the weight vector and b refers to the bias term. \(\zeta_{i}\) indicates the distance between sample \(i\) and the hyperplane. The purpose of this equation is to minimize model complexity with regard to misclassifications while finding a segmentation hyperplane that classifies all training samples accordingly. The \"soft margin\" allows some points to be around or even exceeding the margin boundary to better fit the relationship between feature values and outcomes. C is a hyperparameter that regulates model complexity and penalizes wrong classifications. By training the appropriate model, we can fit the test sample data into it and predict their respective concentration values. It is noteworthy that SVM performs well when solving issues with small samples and nonlinear data distribution while demonstrating high reliability and stability. Therefore, combining the results from our experiments in this study, we opted to utilize an SVM algorithm for fitting feature values to concentration values to achieve optimal effects. When using SVM to fit data, the goodness of fit, \(R^{2}\) value, is a commonly used statistical metric for evaluating the fitting performance of the model. The  
The histogram analysis showed minimal differences in color distribution, contrast, and saturation between the Xiaomi 11 and Huawei P30 smartphones. However, the images taken by the iPhone 12 smartphone show detection values earlier on the \(x\)-axis, with a less pronounced first histogram peak. This suggests that the iPhone 12 is capable of capturing low-light details, such as details in darker or shadowed areas, more effectively. Additionally, its image displays a more even overall brightness distribution.