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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