Feasibility Study of Visible Spectrum as a Tool for Discriminating Urinary Microalbumin

This study validated the feasibility of visible spectroscopy in rapidly detecting Urinary Microalbumin (UALB). Based on 127 clinical urine samples, spectra ranging from 400 to 750 nm were collected using a microspectrometer. The successive projections algorithm (SPA) was used to screen for nine wavelengths highly correlated with UALB, and the spectral index (SI) method was fused to construct an m‐SPA‐SI strategy. The m‐SPA‐SI was combined with a random forest (RF) model for discriminant analysis. The results showed that the m‐SPA‐SI 6 ‐RF model for low‐dimensional spectra exhibited the best performance, with a training accuracy of 100% and improved testing accuracy and sensitivity of 97.37% and 0.9333, respectively. The number of wavelengths was reduced from 457 to 9. Research has confirmed that the combination of low‐dimensional spectroscopy and a wavelength selection algorithm can achieve efficient discrimination of UALB, providing a new method for portable screening of diseases such as chronic kidney disease.