## 4 Results
### Global  
Table 3 and Figures 2-5 summarise the results of the 2-step workflow, namely NMF-RFR referred to earlier. In Table 3, three separate values are referred to, namely the R\({}^{2}\), the root mean squared error (RMSE) and the standard deviation of the RMSE. Column 1 names the dataset in question and lists the number of drillholes that are present in each dataset. Any row that refers to the training dataset are the values as returned by applying the models to the 5000 validation samples while the remaining datasets are the results of applying the models to the unseen testing datasets. The results listed for the \"Training\" dataset are those values as returned by the validation set (5000 samples) for the RFR model trained on the training set (20,000 samples). All global averages given are the averages for the 6 testing datasets (given as Set2-Set7). In Figures 3 and 5 the results do not include any values from the training/validation dataset and are only comprised of results from testing datasets (Set2-Set7). An examination of the training/validation data R\({}^{2}\) scores in Table 3 and Figure 2 to that of Set2-Set7 shows the R\({}^{2}\) is generally maintained in the testing datasets but does \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Dominant Mineral Group Component/Group** & **Assignment** & **Literature** & **nm/cm\({}^{-1}\)** \\ \hline N & kaolin group/Kaolin Group & \(\
u\)Si-O & [44] & 9891/1011 \\ \hline O & Mg-rich Calcium carbonate/Magnesite & \({}^{\alpha}\)\(\
u\)2 peak\({}^{\alpha}\) CO\({}_{3}\) & [7] & 11,058/904 \\ \hline P & Calcium carbonate/Dolomite & \({}^{\alpha}\)\(\
u\)2 peak\({}^{\alpha}\) CO\({}_{3}\) & [7] & 11,236/890 \\ \hline Q & Mg-rich Calcium carbonate/Ankerite & \({}^{\alpha}\)\(\
u\)4 trough\({}^{\alpha}\) CO\({}_{3}\) & [7] & 13,656/732 \\ \hline R & Calcium carbonate/Calcite & \({}^{\alpha}\)\(\
u\)4 trough\({}^{\alpha}\) CO\({}_{3}\) & [7] & 13,942/717 \\ \hline S & iron oxide/Hematite & Fe-O lattice vibration & [45] & 16,393/610 \\ \hline T & iron oxide/Hematite & Fe-O lattice vibration & [45] & 22,026/454 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._decrease, notably for TiO\({}_{2}\) and K\({}_{2}\)O, compared to the R\({}^{2}\) for the training/validation data. This is not wholly unexpected as the spatial locations of the training/validation data are in some cases many tens of kilometers removed from the testing cases. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Fe** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **TiO\({}_{2}\)** & **CaO** & **MgO** & **K\({}_{2}\)O** & **LOI** \\ \hline Train/Val: 1185 & R\({}^{2}\) & 0.99 & 0.99 & 0.97 & 0.88 & 0.99 & 0.99 & 0.92 & 0.98 \\ Set 2: 1  
Figure 4: The empirical cumulative distribution of the 8 whole-rock geochemical parameters for the 6 validation datasets (sets 2–7) and the predicted values as returned by the proposed method. A successful prediction over a given dataset should produce a distribution that is the same as the actual distribution. Generally small departures are noted and would indicate that the model is accurately reproducing the true distribution and values. It is noted that the distributions for TiO\({}_{2}\), CaO, MgO and K\({}_{2}\)O show the range of values for these parameters is extremely small and primarily confined to background. Figure 3: Measured whole-rock parameters versus the predicted value for the eight geochemical parameters used (wt.%). Each plot shows the combined measured versus predicted for the six valuation datasets only. The 1-to-1 line is shown in black for each plot. While CaO and MgO show what appear to be appreciable ranges the bimodal nature of the values is also observed with the bulk of the being primarily distributed around the origin i.e., background values (refer to Figure 4). Figure 3 shows the measured versus predicted values for the 6 validation data sets with the black line in each plot representing the 1-to-1 line.