## 3 Results and Discussion
### Quantification Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **RSDr** & **RSDh** & **RSDrd** & **RPD-CV** & **RPD-Test** \\ \hline
**BC** & 2.4 & 5.5 & 12.7 & 9.7 & 11.3 \\
**SYN-S** & 1.5 & 5.3 & 9.1 & 9.2 & 16.2 \\
**NAT-S** & 2.2 & 4.2 & 4.8 & 16.1 & 27.6 \\
**NAT-S Low** & 3.2 & 13.8 & 32.9 & 9.1 & 12.6 \\
**KBr** & 3.4 & 3.6 & 4.1 & 19.8 & 36.5 \\
**KBr Low** & 5.7 & 6.2 & 11.9 & 27.5 & 50.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative standard deviation (RSD) and relative predictive determinant (RPD) values for the models generated. deviations for all wavenumbers. The resulting noise for the models was different when considering 20 normalized spectra with VN (see Table 3). Otherwise, the \(\gamma\)-values calculated from the two types of models were very similar, which is an indication that \(\gamma\) is not affected by VN preprocessing or any other types of preprocessing. The inverse of \(\gamma\) (denoted as \(\gamma^{-1}\)) provides an estimation of the minimum concentration difference (resultant) discernible by the model considering the instrumental noise as the only source of error. In the case of NAT-S Low, \(\gamma\) was 0.003% for the model with VN preprocessing and 0.002% for the model without preprocessing, with the difference being statistically insignificant. It is not possible to make a comparison of the sensitivities between the modes with matrix and without matrix because the magnitude of \(\|\mathbf{b}\|\) depends on the number of signals and number of latent variables (LV) (see Supplementary Materials: Figure S5). For the models of spectra with many signals (BC, SYN-S, and NAT-S), the magnitude of \(\|\mathbf{b}\|\) is higher than that for the models with low-intensity signals (KBr models). A better FM for this comparison is the LOD. For the models with the matrix (BC, SYN-S, and NAT-S), the LOD values are close to that of the model without the matrix (KBr models). Curves for the samples of low concentrations of DNT in NAT-S were generated to determine whether the employed concentration range influenced the LOD values of the curves. In these cases, the LOD decreased from 0.8% to 0.3%. DNT concentrations between 0.3% and 0.8% in the NAT-S Low model can be quantified with higher uncertainties and higher probabilities of false positives and missed detections because RSD should be between 10% and 33%. To test the NAT-S model, a map of three new types of samples was analyzed, and a map of \(10\times 10\) mm\({}^{2}\) (100 points) for each sample was generated. Two samples comprised the same soil as that used in the NAT-S models and were contaminated with 10% of DNT. The first sample consisted of a simple mixture (NAT-S-M). The second sample involved mixing and macerating the components (NAT-S-MM). The concentrations were predicted using the NAT-S model with three preprocessing methods: VN, MC, and no preprocessing. The map for the NAT-S model with VN is included in Supplementary Materials: Figures S6-S9, while the predictions for the 100 points for each map using different preprocessing methods are shown in Figure 4a,b. VN preprocessing applied to both samples (NAT-S-M and NAT-S-MM) resulted in better results, with the predicted values being close to the true value on average (10% DNT, see Figure 4c). MC preprocessing worked better in NAT-S-MM than NAT-S-M. This indicates that while the macerated process homogenized the size of the particles,