Analytical Device and Prediction Method for Urine Component Concentrations

To tackle the low-accuracy problem with analyzing urine component concentrations in real time, a fully automated dipstick analysis device of urine dry chemistry was designed, and a prediction method combining an image acquisition system with a whale optimization algorithm (WOA) for BP neural network optimization was proposed. The image acquisition system, which comprised an ESP32S3 chip and a GC2145 camera, was used to collect the urine test strip images, and then color data were calibrated by image processing and color correction on the upper computer. The correlations between reflected light and concentrations were established following the Kubelka-Munk theory and the Beer-Lambert law. A mathematical model of urine colorimetric value and concentration was constructed based on the least squares method. The WOA algorithm was applied to optimize the weight and threshold of the BP neural network, and substantial data were utilized to train the neural network and perform comparative analysis. The experimental results show that the MAE, RMSE and R² of predicted versus actual urine protein values were, respectively, 3.1415, 4.328 and approximately 1. The WOA-BP neural network model exhibited high precision and accuracy in predicting the urine component concentrations.