## 3 Results and Discussion  
### Quantification Analysis  
All spectra were converted to the Kubelka-Munk function [63] because the reflectance values for all samples were very low. This transformation is appropriate for the diffuse reflectance spectra of powders [64] with R values \(<\) 60% [65]. The models were generated using this transformation. The graphs plotting the predicted versus true values for the samples used in CV and testing are shown in Figure 2, where the CV data are represented with open diamonds. In contrast, the test data are represented with open triangles. The solid black line represents the ideal case or perfect model (y = x), where the predicted values are equal to the true values throughout the entire data range. The data set employed in CV was generated using the leave-one-out CV (LOO-CV). It can be noticed from Figure 2 that the predicted values for the CV and test data sets closely follow the best performance line (y = x). This alignment can also be confirmed by the low values of the RMSE, RMSECV, and RMSEP listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **RMSE** & **RMSECV** & **RMSE** & **Bias** & **LV** \\ \hline
**BC** & 0.41 & 0.57 & 0.70 & \(-\)0.0007 & 11 \\
**SYN-S** & 0.35 & 0.43 & 0.53 & \(-\)0.0069 & 7 \\
**NAT-S** & 0.25 & 0.39 & 0.39 & 0.0010 & 10 \\
**NAT-S Low** & 0.08 & 0.10 & 0.34 & 0.0100 & 7 \\
**KBr** & 0.18 & 0.32 & 0.41 & \(-\)0.0044 & 10 \\
**KBr Low** & 0.02 & 0.03 & 0.08 & \(-\)0.0003 & 9 \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy, bias, and the number of latent variables (LV) for the studied models and matrices. Figure 2: Predicted values vs. real values of % DNT from partial least squares (PLS) models on the matrices: (**a**) bentonite clay (BC); (**b**) synthetic soil (SYN-S); (**c**) natural soil (NAT-S); (**d**) NAT-S to low concentration (NAT-S Low); (**e**) potassium bromide (KBr) and (**f**) KBr to low concentration (KBr Low). All models were generated using the complete spectral window (1000-1600 cm\({}^{-1}\)). In this study, VN was used as a preprocessing step as it proved to be better than the other tested preprocessing steps except for KBr. Other preprocessing steps were tested, such as mean centering (MC), linear offset subtraction, straight-line subtraction, minimum and maximum normalization, multiplicative scatter correction, first derivative, second derivative, and no preprocessing step. When applying VN, the average intensity is calculated first, and then this value is subtracted from the spectrum. Next, the sum of the squared intensities is calculated, and the spectrum is divided by the square root of this sum. Models for DNT in KBr were generated to evaluate the detection in the absence of interferences: one at high concentrations (0-20%) and another at low concentrations (0-3%). The errors for these models are listed in Table 1. The most effective preprocessing method for the KBr models was MC. This result suggests that VN is a suitable preprocessing step only when interferences from the matrix are present. In a model free from the interfering matrix (KBr), applying VN for generating samples with low concentrations had the same signal intensity as samples with high concentrations. This is reflected in the lousy prediction and high uncertainty for samples with low concentrations (see Supplementary Materials: Figure S3). The spectra of the neat matrices, matrices with 20% DNT, and the corresponding standard reference spectra of DNT in KBr for transmittance are shown in Figure 3a-c, respectively. Figure 3d shows the resulting spectrum for the matrix with 2% DNT in NAT-S minus the spectrum of the neat matrix and corresponding standard reference spectra of DNT in KBr for transmittance. For clarity, DNT reference spectra are represented with the red, dotted lines.