1 Introduction

The field detection of explosives has received considerable attention globally for many years due to its importance in forensic applications, defense and security, and environmental control. High explosives (HEs) have been used for decades in military practice and mining excavation. These are considered as a contamination source which can generate poisoning of population humans and animals, and producing genetic diseases that threaten human health. [1,2] In order to monitor the quality of HE-contaminated soils, this study focuses on the detection of HE in soil using mid-infrared (MIR) laser spectroscopy with a Quantum Cascade Laser (QCL) source as a remote method of analysis. [3-13] QCL spectroscopy first demonstrated in 1994 by Faist et al. [14]offers several benefits over conventional or thermal source MIR spectroscopy, such as room temperature operation, small beam sizes, long lifetimes, low energy consumption, long-term power stability, and fine-tuning of the output frequency. [15] Several groups have demonstrated the capability of remote sensing of HE and others analytes using MIR laser spectroscopy with QCLs.[14, 16-27] The typical methods used to detect HE is destructive, require sampling, transferring samples to the lab, and performing a proper treatment of the sample for later detection. These methods include protocols based on gas chromatography-mass spectroscopy (GC-MS), gas chromatography-chemiluminescence (GC-CL), ion mobility spectrometry (IMS), [28] immunosensors,[29] electrophoresis, [30]fluorescence, [31] electrochemical methods,[30,32] high-pressure liquid chromatography (HPLC), [33,34] and HPLC-MS.[33] However, in situ detection of HE in the soil is not easy due to the presence of solid interfering materials such as organic and inorganic compounds, which vary for each type of soil [35-37] which makes this detection a challenge for the analyst. Other studies conducted by this research group involved the characterization [38-40]interactions, [41-43] and detection[44-45] of HE in soil using Raman and FT-IR spectroscopy. [46,47] In all of these cases, the detection was marginally possible because the crystalline particles of HE had to be found in the solid matrix by microscopy to achieve the detection.
Today, AI methods are becoming more popular because they have demonstrated to be a rapidly evolving research area that offers sophisticated and advanced approaches capable of addressing complicated and challenging problems. Besides, AI-based systems have a variety of applications in different sectors, such as engineering, economics, medicine, military, marine sciences, and others.[48] Therefore, AI allows the transfer of human knowledge to machines through analytical models and learning from the data. This is a task that can be accomplished by soft-computing methodologies. [49]
AI uses minimum information (spectra of neat explosives and clean soil) for the development of the Machine Learning (ML) models without the necessity of experimental data of the mixes. AI: Self-Simulated Learning Artificial Intelligence (SSLAI) models were tested with real spectra of experimental mixes of HE/soils. SSLAI models do not need to be trained with real contaminated soil samples or real mixes of HE/soils. The model would only have to be fed with spectra of neat HE and soils for the model to train itself. This provides the possibility of HE detection in field applications with the advantages that the natural solid matrices could be unknown.