Despite this, PLS models were able to adapt to the new dataset and provide consistent identifications of DKD, while PLS, RF and SVM models showed strong prediction capabilities for the diagnosis of albuminuria. Furthermore, incorporating a representative subset from both datasets into the calibration set and updating the model substantially increased considerably the prediction capability of the model, providing sensitivity and specificity values above 90%. Regression vectors showed that the spectral signatures captured by the models featured bands of albumin for the albuminuria prediction and bands of albumin and glycoprotein for the diagnosis of DKD.