Data analysis Data was processed using MS Office Excel and Origin 2020b. Multivariate analysis was performed in Python using Google Colab space and numpy, matplotlib, pandas, seaborn, sklearn libraries. Data was first scaled using sklearn StandardScaler. In case of K+Val, K+DB, NH4+, An, Cat, pH array and the same array without pH measurement PLS and PCR algorithms were based on 4 components/latent variables, for all other compositions of the matrixes 2 components/latent variables were used. Supervised analysis was performed with train/test split of 25 %. All algorithms were tested for 4 different random splits the same for each algorithm (sklearn train_test_split routine with random_states: 1,2,3,4) to allow proper comparison of algorithms metrics. Evaluation of the models was based on sklearn metrics routines.