Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 31 May 2020 / Approved: 31 May 2020 / Online: 31 May 2020 (20:57:47 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Appl. Sci. 2020, 10, 4178
DOI: 10.3390/app10124178

Abstract

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 (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then 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 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Subject Areas

quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning

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