Analytical Device and Prediction Method for Urine Component Concentrations

TL;DR

A new device and method were developed to accurately predict urine component concentrations using image analysis and an optimized neural network.

Contribution

A novel WOA-BP neural network model was proposed for precise urine analysis using image data and optimization algorithms.

Findings

The WOA-BP model achieved high accuracy in predicting urine protein concentrations.

The device uses an image acquisition system and color correction for reliable data collection.

Mathematical models based on Kubelka–Munk and Beer–Lambert laws improved prediction reliability.

Abstract

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.

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