In this research, we evaluated the IR based methodology for diagnosing microalbuminuria in a global setting, by employing new sample data obtained under conditions markedly distinct from those of the calibration dataset. Our aim was to evaluate the robustness of its diagnostic capabilities using different machine learning algorithms for practical application across various laboratories worldwide. To overcome challenges in measuring certain samples with both types of instruments for calibration transfer, we opted for a strategy of interpolating wavenumber values and normalizing spectra. Furthermore, we investigated the enhancement in overall performance of the classification models when the model was recalibrated using samples from both instruments. In total, we compared the adaptability capabilities of three different ML algorithms: Partial Least Squares (PLS), Support Vector Machines (SVM) and Random Forest (RF). We also extended the reach of the method by evaluating the methodology for the identification of patients diagnosed with DKD, regardless of their levels of albuminuria.