The best parameters were obtained for an array composed of only two types of sensors: the valinomycin based electrodes and ammonium selective sensors (Fig. ). Fig. shows the comparison of the information on the package with the predicted quantities of potassium from 4 models created on different 25 % test/training splits of the data. The best performance of the MLR algorithm was obtained for the array composed from all ion-selective electrodes and the pH electrode. Although the general metrics presented in Table are promising, the actual comparison of predicted and real values shown in Fig. 3A reveal that for some categories of samples this model is subject to considerable error. PLS test score was highest for the ion-selective array without the pH measurement and the PCR for an array with further reduced size (without valinomycin based sensors). However the best results in total were obtained for the ensemble Random Forest algorithm based on only two types of sensors. As previously seen on PCA loadings plot the two chosen sensors provide different information regarding the sample. Although pH and anion-selective electrodes contribute with also diverse information when compared to valinomycin electrodes, such arrays are not optimal (Tab. 3., Fig. ). The best performing model was also quite stable, as the difference between R 2 for test samples was around 1 % between the 4 tested random train/test splits.