First, principal component analysis (PCA) was performed for data exploration purposes. Then different ML models were constructed for both classification and regression tasks. All spectra underwent standard normal variate (SNV) scaling, Savitzky-Golay first derivative , and mean centering. The classification models aimed to distinguish between diabetic kidney disease (DKD) and control groups, including partial least squares discriminant analysis (PLS-DA), support vector machines for classification (SVM-DA), and random forest (RF) for classification. Additionally, corresponding regression models were built using albumin concentration as the target variable, specifically partial least squares (PLSR), support vector regression (SVR), and RF. To assess domain transfer classification and the feasibility of constructing a global model, different combinations of training and test sets were evaluated, considering the sample origin.