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Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence

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ALERT DHS Center of Excellence for Explosives Research, Department of Chemistry, University of Puerto Rico, Mayagüez, PR 00681, USA
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School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia
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MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia
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Department of Physics, University of Miami, Coral Gables, FL 33124, USA
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Physics, Chemistry, Physics and Earth Sciences Department, Miami-Dade College, Kendall Campus, Miami, FL 33176, USA
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Grupo de Investigación Química Supramolecular Aplicada, Programa de Química, Universidad del Atlántico, Barranquilla 080001, Colombia
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Department of Electrical & Computer Engineering, University of Houston, 4800 Calhoun Rd. Eng. Bldg. 1, Rm. N308, Houston, TX 77204-4005, USA
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Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4178; https://doi.org/10.3390/app10124178
Received: 2 April 2020 / Revised: 21 May 2020 / Accepted: 10 June 2020 / Published: 18 June 2020
A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils. View Full-Text
Keywords: quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning
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MDPI and ACS Style

Pacheco-Londoño, L.C.; Warren, E.; Galán-Freyle, N.J.; Villarreal-González, R.; Aparicio-Bolaño, J.A.; Ospina-Castro, M.L.; Shih, W.-C.; Hernández-Rivera, S.P. Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence. Appl. Sci. 2020, 10, 4178.

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