## 4 Conclusions  
This paper presented methods for quantifying DNT and detecting HEs in natural and synthetic soil matrices. The remote detection of HEs at a distance of 15 cm was achieved using multiple benefits of QCL spectroscopy, providing the evidence that it can be used in direct field applications. The obtained LOD values were adequate for the intended application. This study demonstrated that it is possible to get proper detection in the MIR range using diffuse reflection in the back-reflection mode for matrices containing components with low reflectivity such as soils. The remote detection of HEs in soils is now much more viable due to the high-power QCLs that are commercially available. In the presented experiments, DNT signals could be observed in the spectra of solid mixtures with complex matrices, enabling the detection of explosives and quantification of DNT. For quantifying DNT in soil matrices, PLS models were useful for generating calibration curves. The best-performing models were obtained using VN preprocessing for all spectra. This preprocessing procedure proved to be suitable for samples, in which signals from analytes spectra overlapped with signals from matrices (natural soil, synthetic soil, and bentonite) spectra and relative intensity differences between spectra were small. In models that involved mixtures of explosives with KBr, VN preprocessing is not suitable because the KBr matrix does not have interfering signals in the spectroscopic window of interest. Therefore, this preprocessing step was not required. Hence, it can be concluded that VN is a useful preprocessing step for soil samples that have highly interfering matrices such as other explosives (TNT, RDX, and PETN) and non-explosives (BA and IBP). The strategy of adding samples of other analytes as possible interferences produced more selective models, making them more robust. The interferences used were organic compounds with many signals in the studied region. The interferences included explosives such as TNT, PETN, and RDX, as well as organic compounds such as BA and IBP. The NAT-S model was able to evade all interferences when it was trained with samples contaminated with these compounds. For the model to be accurate for other soils and possible interferences, it should be re-trained with corresponding samples. Furthermore, an AI-based on an ML pipeline scheme (see Figure 1) was applied to obtain an RFC model for HEs detection in soil matrices. This scheme included Pre-CLS assisted by SSLAI and ML methods. The expansion of the soil database generated by the linear combinations of QCL reflectance spectra of four different soils (natural soils, sand, and clay standards) allowed the application of Self-Simulated Learning Artificial Intelligence (SSLAI) concept. At the same time, it was possible to couple a simple forward selection algorithm (FSA) that facilitated the compression of the model when using simple multivariate analyses; CLS as additional preprocessing and PCA to reduce the number of spectral variables. Therefore, the combination of PCA, Pre-CLS, and SSLAI is an innovative ML pipeline that allows enriching the prediction of the model. Almost all tested samples of the studied soils and explosives (DNT, RDX, and TNT) were correctly classified by the model (see Table 7), providing an accuracy score of 0.996.