The technique was previously evaluated for the detection of microalbuminuria levels , but patients diagnosed with DKD may did not exhibit microalbuminuria levels in their urine samples. Therefore, here we studied the differentiation of DKD diagnosed patients' samples (including those with low levels of albuminuria) from control group samples. Classification performance indicators can be found in the first row of Table and Figure . PLSDA yielded the best prediction error, obtaining sensitivity, specificity and AUROC values of 82.9, 88.5 and 0.87, respectively. Figure depicts the probabilities predicted by the PLSDA model, showing that the separation among classes enabled the independent prediction of 52 of the 61 Spanish samples correctly (85%). This value clearly indicates that the spectral markers found in the Australian model were robust and consistent enough to be transferred to the Spanish dataset, even if the model had never been trained with any Spanish data and even both datasets contained inconsistencies in sample population and experimental measurement conditions. On the other hand, interestingly, SVMDA and RF exhibited very poor specificities of 27.0% and 42.3%, respectively. This may be explained by the imbalance of the datasets. Australian number of control samples, 22, was really small compared to the 155 DKD Australian samples, making models prone to predict spectra as positive. Figure and 3C shows that the predictions tended to favour the classification of almost all the samples as DKD, leading to the poor performance of these non-linear machine learning methods.