4 Conclusion

This research establishes a strategic method for the detection of HE (TNT, RDX) in soil samples. The benefits of the methodology “Self-Simulated Learning Artificial Intelligence” (SSLAI) is that simulated data generated from spectra of neat HE and clean soil were used to create the training data set used to generate ML models. SSLAI was validated using 3 tests with experimental spectra of HE/soils mixtures and obtained favorable results. The remote detection was achieved using the benefits of MIR laser spectroscopy, which can be applied in field applications.
The detection of threat chemicals in solid matrices such as natural soil is complicated, specifically in field applications where the detection time is essential, and where often these solid matrices are unknown. This methodology permits to generate the model at the moment with insufficient information for training, surpassing previous methodologies where the volume of experimental data necessary to generate good prediction models takes time to collect and analyze the data.
The synergy between QCL technology and SSLAI can be a promising strategy for the detection of weapons of mass destruction in the fields of defense and security because solid matrices such as soil contain many interferences and varieties from the multiple components that they contain and also because they are substrates/media with low reflectivity. Therefore, the detection of analytes in soil matrices is considered a challenge.

Disclosures

The authors declare no conflicts of interest.

Acknowledgments

This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs, under Grant Award 2013-ST-061-ED0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.

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