Abstract
Renal biomarker estimation is important for evaluating kidney health status. Traditional methods for determining creatinine and urea levels rely on chemical reagents and analytical instruments. This study explores an alternative, reagent-free approach for determining creatinine concentration and estimating urea levels in human plasma samples using near-infrared (NIR) spectroscopy, chemometrics, and artificial intelligence. NIR spectra were acquired from 500 blood samples using a portable NIR spectrometer operating between 1000 and 2600 nm, with laboratory-determined creatinine and urea concentrations used as reference values for calibration. The spectra were preprocessed using Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), and Savitzky–Golay (SG) filtering, which improved the PLS coefficient of determination from R2=0.72 to R2=0.94. Interval Partial Least Squares (iPLS) was then used to select the most informative wavelengths, yielding five spectral intervals: 1020–1060, 1430–1480, 1590–1650, 1950–2020, and 2080–2150 nm. Creatinine concentration was predicted using Partial Least Squares (PLS), a Generative Adversarial Network (GAN), a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM) network, with the data split into 400 training and 100 test observations. On the held-out internal test set, the GAN achieved the best performance among all tested methods, with R2=0.97, RMSE of 1.31 mg/L, and MAE of 0.94 mg/L, while the CNN, LSTM, and PLS models achieved R2 values of 0.95, 0.94, and 0.93, respectively. An indirect method for urea estimation, based on the statistical correlation between creatinine and urea, was also examined: the NIR-predicted creatinine levels were used in a linear regression model relating creatinine and urea, yielding R2=0.92, RMSE = 0.18 g/L, and MAE = 0.13 g/L. Overall, these findings demonstrate the feasibility of combining NIR spectroscopy, spectral preprocessing, iPLS-based wavelength selection, and deep learning for the analysis of renal biomarkers. The proposed NIR-iPLS-GAN framework successfully predicted creatinine concentration and enabled the extrapolation of spectral information to indirectly estimate urea concentration.