Mostrar el registro sencillo del ítem

dc.contributor.authorPacheco-Londoño, Leonardo C.
dc.contributor.authorWarren, Eric
dc.contributor.authorGalán-Freyle, Nataly J.
dc.contributor.authorVillarreal-González, Reynaldo
dc.contributor.authorAparicio-Bolaño, Joaquín A.
dc.contributor.authorOspina-Castro, María L.
dc.contributor.authorShih, Wei-Chuan
dc.contributor.authorHernández-Rivera, Samuel P.
dc.description.abstractA 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.eng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.sourceRevista Applied Scienceseng
dc.sourceVol. 10, No. 12, (2020)
dc.subjectQuantum cascade lasereng
dc.subjectRemote detectioneng
dc.subjectPartial least squareseng
dc.subjectHigh explosiveseng
dc.subjectArtificial intelligenceeng
dc.subjectMachine learningeng
dc.titleMid-Infrared laser spectroscopy detection and quantification of explosives in soils using multivariate analysis and artificial intelligenceeng
dcterms.referencesFrische, T.; Höper, H. Soil microbial parameters and luminescent bacteria assays as indicators for in situ bioremediation of TNT-contaminated soils. Chemosphere 2003, 50, 415–427.eng
dcterms.referencesCorrea-Torres, S.N.; Pacheco-Londono, L.C.; Espinosa-Fuentes, E.A.; Rodriguez, L.; Souto-Bachiller, F.A.; Hernandez-Rivera, S.P. TNT removal from culture media by three commonly available wild plants growing in the Caribbean. J. Environ. Monit. 2012, 14, 30–33.eng
dcterms.referencesHildenbrand, J.; Herbst, J.; Wöllenstein, J.; Lambrecht, A. Explosive detection using infrared laser spectroscopy. Proc. SPIE 2009, 7222, 72220B.eng
dcterms.referencesNarang, U.; Gauger, P.R.; Ligler, F.S. A Displacement Flow Immunosensor for Explosive Detection Using Microcapillaries. Anal. Chem. 1997, 69, 2779–2785.eng
dcterms.referencesHilmi, A.; Luong, J.H.T. Micromachined Electrophoresis Chips with Electrochemical Detectors for Analysis of Explosive Compounds in Soil and Groundwater. Environ. Sci. Technol. 2000, 34, 3046–3050.eng
dcterms.referencesKumar, S.; Venkatramaiah, N.; Patil, S. Fluoranthene Based Derivatives for Detection of Trace Explosive Nitroaromatics. J. Phys. Chem. C 2013, 117, 7236–7245.eng
dcterms.referencesSheremata, T.W.; Halasz, A.; Paquet, L.; Thiboutot, S.; Ampleman, G.; Hawari, J. The Fate of the Cyclic Nitramine Explosive RDX in Natural Soil. Environ. Sci. Technol. 2001, 35, 1037–1040.eng
dcterms.referencesLarson, S.L.; Martin, W.A.; Escalon, B.L.; Thompson, M. Dissolution, Sorption, and Kinetics Involved in Systems Containing Explosives, Water, and Soil. Environ. Sci. Technol. 2008, 42, 786–792.eng
dcterms.referencesMarple, R.L.; LaCourse,W.R. Application of Photoassisted Electrochemical Detection to Explosive-Containing Environmental Samples. Anal. Chem. 2005, 77, 6709–6714.eng
dcterms.referencesGallagher, N.B.; Blake, T.A.; Gassman, P.L. Application of extended inverse scatter correction to mid-infrared reflectance spectra of soil. J. Chemom. 2005, 19, 271–281.eng
dcterms.referencesForouzangohar, M.; Kookana, R.S.; Forrester, S.T.; Smernik, R.J.; Chittleborough, D.J. Mid-infrared Spectroscopy and Chemometrics to Predict Diuron Sorption Coe cients in Soils. Environ. Sci. Technol. 2008, 42, 3283–3288.eng
dcterms.referencesGallagher, N.B.; Gassman, P.L.; Blake, T.A. Strategies for Detecting Organic Liquids on Soils Using Mid-Infrared Reflection Spectroscopy. Environ. Sci. Technol. 2008, 42, 5700–5705.eng
dcterms.referencesMukherjee, A.; Von der Porten, S.; Patel, C.K.N. Standoff detection of explosive substances at distances of up to 150 m. Appl. Opt. 2010, 49, 2072–2078.eng
dcterms.referencesHernández, M.D.; Santiago, I.; Padilla, I.Y. Macro-sorption of 2,4-dinitrotoluene onto sandy and clay soils. Proc. SPIE 2006, 6217, 621736.eng
dcterms.referencesBaez, B.; Correa, S.N.; Hernandez-Rivera, S.P.; de Jesus, M.; Castro, M.E.; Mina, N.; Briano, J.G. Transport of explosives I: TNT in soil and its equilibrium vapor. Proc. SPIE 2004, 5415, 1389–1399.eng
dcterms.referencesTorres, A.; Padilla, I.; Hwang, S. Physical modeling of 2,4-DNT gaseous diffusion through unsaturated soil. Proc. SPIE 2007, 6553, 65531Q.eng
dcterms.referencesHerrera-Sandoval, G.M.; Ballesteros, L.M.; Mina, N.; Briano, J.; Castro, M.E.; Hernandez-Rivera, S.P. Raman signatures of TNT in contact with sand particles. Proc. SPIE 2005, 5794, 1245–1253.eng
dcterms.referencesBlanco, A.; Mina, N.; Castro, M.E.; Castillo-Chara, J.; Hernandez-Rivera, S.P. Effect of environmental conditions on the spectroscopic signature of DNT in sand. Proc. SPIE 2005, 5794, 1281–1289.eng
dcterms.referencesBallesteros, L.M.; Herrera, G.M.; Castro, M.E.; Briano, J.; Mina, N.; Hernandez-Rivera, S.P. Spectroscopic signatures of PETN in contact with sand particles. Proc. SPIE 2005, 5794, 1254–1262.eng
dcterms.referencesHernandez-Rivera, S.P.; Manrique-Bastidas, C.A.; Blanco, A.; Primera, O.M.; Pacheco, L.C.; Castillo-Chara, J.; Castro, M.E.; Mina, N. Spectroscopic characterization of nitroaromatic landmine signature explosives. Proc. SPIE 2004, 5415, 474–485.eng
dcterms.referencesOsorio, C.; Gomez, L.M.; Hernandez, S.P.; Castro, M.E. Time-of-flight mass spectroscopy measurements of TNT and RDX on soil surfaces. Proc. SPIE 2005, 5794, 803–811.eng
dcterms.referencesManrique-Bastidas, C.A.; Mina, N.; Castro, M.E.; Hernandez-Rivera, S.P. Raman microspectroscopy and FTIR crystallization studies of 2,4,6-TNT in soil. Proc. SPIE 2005, 5794, 1358–1365.eng
dcterms.referencesBlanco, A.; Pacheco-Londoño, L.C.; Peña-Quevedo, A.J.; Hernández-Rivera, S.P. UV Raman detection of 2,4-DNT in contact with sand particles. Proc. SPIE 2006, 6217, 621737.eng
dcterms.referencesGalán-Freyle, N.J.; Pacheco-Londoño, L.C.; Figueroa-Navedo, A.M.; Hernandez-Rivera, S.P. Standoff Detection of Highly Energetic Materials Using Laser-Induced Thermal Excitation of Infrared Emission. Appl. Spectrosc. 2015, 69, 535–544.eng
dcterms.referencesGalán-Freyle, N.J.; Ospina-Castro,M.L.;Medina-González, A.R.; Villarreal-González, R.; Hernández-Rivera, S.P.; Pacheco-Londoño, L.C. Artificial Intelligence AssistedMid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils. Appl. Sci. 2020, 10, 1319.eng
dcterms.referencesRüther, A.; Pfeifer, M.; Lórenz-Fonfría, V.A.; Lüdeke, S. pH Titration Monitored by Quantum Cascade Laser-Based Vibrational Circular Dichroism. J. Phys. Chem. B 2014, 118, 3941–3949.eng
dcterms.referencesLüdeke, S.; Pfeifer, M.; Fischer, P. Quantum-Cascade Laser-Based Vibrational Circular Dichroism. J. Am. Chem. Soc. 2011, 133, 5704–5707.eng
dcterms.referencesShi, Q.; Nelson, D.D.; McManus, J.B.; Zahniser, M.S.; Parrish, M.E.; Baren, R.E.; Shafer, K.H.; Harward, C.N. Quantum Cascade Infrared Laser Spectroscopy for Real-Time Cigarette Smoke Analysis. Anal. Chem. 2003, 75, 5180–5190.eng
dcterms.referencesWörle, K.; Seichter, F.; Wilk, A.; Armacost, C.; Day, T.; Godejohann, M.; Wachter, U.; Vogt, J.; Radermacher, P.; Mizaikoff, B. Breath Analysis with Broadly Tunable Quantum Cascade Lasers. Anal. Chem. 2013, 85, 2697–2702.eng
dcterms.referencesGalán-Freyle, N.J.; Pacheco-Londoño, L.C.; Román-Ospino, A.D.; Hernandez-Rivera, S.P. Applications of Quantum Cascade Laser Spectroscopy in the Analysis of Pharmaceutical Formulations. Appl. Spectrosc. 2016, 70, 1511–1519.eng
dcterms.referencesPadilla-Jiménez, A.C.; Ortiz-Rivera, W.; Rios-Velazquez, C.; Vazquez-Ayala, I.; Hernández-Rivera, S.P. Detection and discrimination of microorganisms on various substrates with quantum cascade laser spectroscopy. OPTICE 2014, 53, 061611.eng
dcterms.referencesFaist, J.; Capasso, F.; Sivco, D.L.; Sirtori, C.; Hutchinson, A.L.; Cho, A.Y. Quantum Cascade Laser. Science 1994, 264, 553–556.eng
dcterms.referencesHvozdara, L.; Pennington, N.; Kraft, M.; Karlowatz, M.; Mizaikoff, B. Quantum cascade lasers for mid-infrared spectroscopy. Vib. Spectrosc. 2002, 30, 53–58.eng
dcterms.referencesRuiz-Caballero, J.L.; Blanco-Riveiro, L.A.; Ramirez-Marrero, I.A.; Perez-Almodovar, L.A.; Colon-Mercado, A.M.; Castro-Suarez, J.R.; Pacheco-Londoño, L.C.; Hernandez-Rivera, S.P. Enhanced RDX Detection Studies on Various Types of Substrates via Tunable Quantum Cascade Laser Spectrometer Coupled with Grazing Angle Probe. IOP Conf. Ser. Mater. Sci. Eng. 2019, 519, 012007.eng
dcterms.referencesPacheco-Londoño, L.C.; Galán-Freyle, N.J.; Figueroa-Navedo, A.M.; Infante-Castillo, R.; Ruiz-Caballero, J.L.; Hernández-Rivera, S.P. Quantum cascade laser back-reflection spectroscopy at grazing-angle incidence using the fast Fourier transform as a data preprocessing algorithm. J. Chemom. 2019, 33, e3167.eng
dcterms.referencesPacheco-Londoño, L.C.; Aparicio-Bolaño, J.A.; Galán-Freyle, N.J.; Román-Ospino, A.D.; Ruiz-Caballero, J.L.; Hernández-Rivera, S.P. Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics. Appl. Spectrosc. 2019, 73, 17–29.eng
dcterms.referencesPacheco-Londoño, L.C.; Ruiz-Caballero, J.L.; Ramírez-Cedeño, M.L.; Infante-Castillo, R.; Gálan-Freyle, N.J.; Hernández-Rivera, S.P. Surface Persistence of Trace Level Deposits of Highly Energetic Materials. Molecules 2019, 24, 3494.eng
dcterms.referencesPacheco-Londoño, L.C.; Castro-Suarez, J.R.; Galán-Freyle, N.J.; Figueroa-Navedo, A.M.; Ruiz-Caballero, J.L.; Infante-Castillo, R.; Hernández-Rivera, S.P. Mid-Infrared Laser Spectroscopy Applications I: Detection of Traces of High Explosives on Reflective and Matte Substrates. In Infrared Spectroscopy—Principles, Advances, and Applications; IntechOpen: London, UK, 2019.eng
dcterms.referencesPhillips, M.C.; Bernacki, B.E. Hyperspectral microscopy of explosives particles using an external cavity quantum cascade laser. OPTICE 2012, 52, 061302.eng
dcterms.referencesPacheco-Londoño, L.C.; Castro-Suarez, J.R.; Hernández-Rivera, S.P. Detection of Nitroaromatic and Peroxide Explosives in Air Using Infrared Spectroscopy: QCL and FTIR. Adv. Opt. Technol. 2013, 2013, 532670.eng
dcterms.referencesSirkeli, V.P.; Yilmazoglu, O.; Preu, S.; Küppers, F.; Hartnagel, H.L. Proposal for a Monolithic Broadband Terahertz Quantum Cascade Laser Array Tailored to Detection of Explosive Materials. Sens. Lett. 2018, 16, 1–7.eng
dcterms.referencesPettersson, A.; Wallin, S.; Östmark, H.; Ehlerding, A.; Johansson, I.; Nordberg, M.; Ellis, H.; Al-Khalili, A. Explosives standoff detection using Raman spectroscopy: From bulk towards trace detection. Proc. SPIE 2010, 7664, 76641K.eng
dcterms.referencesYang, C.S.C.; Brown, E.E.; Hommerich, U.; Jin, F.; Trivedi, S.B.; Samuels, A.C.; Snyder, A.P. Long-Wave, Infrared Laser-Induced Breakdown (LIBS) Spectroscopy Emissions from Energetic Materials. Appl. Spectrosc. 2012, 66, 1397–1402.eng
dcterms.referencesMisra, A.K.; Sharma, S.K.; Acosta, T.E.; Porter, J.N.; Bates, D.E. Single-Pulse Standoff Raman Detection of Chemicals from 120 m Distance During Daytime. Appl. Spectrosc. 2012, 66, 1279–1285.eng
dcterms.referencesGottfried, J.L.; De Lucia, F.C.;Munson, C.A.;Miziolek, A.W. Standoff Detection of Chemical and Biological Threats Using Laser-Induced Breakdown Spectroscopy. Appl. Spectrosc. 2008, 62, 353–363.eng
dcterms.referencesCastro-Suarez, J.R.; Pacheco-Londoño, L.C.; Vélez-Reyes, M.; Diem, M.; Tague, T.J.; Hernandez-Rivera, S.P. FT-IR Standoff Detection of Thermally Excited Emissions of Trinitrotoluene (TNT) Deposited on Aluminum Substrates. Appl. Spectrosc. 2013, 67, 181–186.eng
dcterms.referencesCarter, J.C.; Angel, S.M.; Lawrence-Snyder, M.; Scaffdi, J.; Whipple, R.E.; Reynolds, J.G. Standoff Detection of High Explosive Materials at 50 Meters in Ambient Light Conditions Using a Small Raman Instrument. Appl. Spectrosc. 2005, 59, 769–775.eng
dcterms.referencesAverett, L.A.; Griffths, P.R. Mid-Infrared Diffuse Reflection of a Strongly Absorbing Analyte on Non-Absorbing and Absorbing Matrices. Part II: Thin Liquid Layers on Powdered Substrates. Appl. Spectrosc. 2008, 62, 383–388.eng
dcterms.referencesPacheco-Londoño, L.; Ortiz-Rivera, W.; Primera-Pedrozo, O.; Hernández-Rivera, S. Vibrational spectroscopy standoff detection of explosives. Anal. Bioanal. Chem. 2009, 395, 323–335.eng
dcterms.referencesVan Neste, C.W.; Senesac, L.R.; Thundat, T. Standoff Spectroscopy of Surface Adsorbed Chemicals. Anal. Chem. 2009, 81, 1952–1956.eng
dcterms.referencesMoros, J.; Lorenzo, J.A.; Lucena, P.; Miguel Tobaria, L.; Laserna, J.J. Simultaneous Raman Spectroscopy– Laser-Induced Breakdown Spectroscopy for Instant Standoff Analysis of Explosives Using a Mobile Integrated Sensor Platform. Anal. Chem. 2010, 82, 1389–1400.eng
dcterms.referencesMoros, J.; Laserna, J.J. New Raman–Laser-Induced Breakdown Spectroscopy Identity of Explosives Using Parametric Data Fusion on an Integrated Sensing Platform. Anal. Chem. 2011, 83, 6275–6285.eng
dcterms.referencesOrtiz-Rivera, W.; Pacheco-Londoño, L.; Castro-Suarez, J.; Felix-Rivera, H.; Hernandez-Rivera, S. Vibrational Spectroscopy Standoff Detection of Threat Chemicals; SPIE: Bellingham, WA, USA, 2011; Volume 8031.eng
dcterms.referencesMiyazawa, M.; Pavan, M.A.; de Oliveira, E.L.; Ionashiro, M.; Silva, A.K. Gravimetric determination of soil organic matter. Braz. Arch. Biol. Technol. 2000, 43, 475–478.eng
dcterms.referencesWeiner, E.R. Applications of Environmental Chemistry: A Practical Guide for Environmental Professionals; Lewis Pub.: Boca Raton, FL, USA, 2000.eng
dcterms.referencesYanjun, C.; Achari, G.; Langford, C.H. Protocols for the analysis of transformer oil and its degradation in soil by hydrogen peroxide. Can. J. Civ. Eng. 2009, 36, 1547–1557.eng
dcterms.referencesLorber, A. Error propagation and figures of merit for quantification by solving matrix equations. Anal. Chem. 1986, 58, 1167–1172.eng
dcterms.referencesFerreira, M.H.; Braga, J.W.B.; Sena, M.M. Development and validation of a chemometric method for direct determination of hydrochlorothiazide in pharmaceutical samples by diffuse reflectance near-infrared spectroscopy. Microelectron. J. 2013, 109, 158–164.eng
dcterms.referencesOlivieri, A.C.; Faber, N.M.; Ferré, J.; Boqué, R.; Kalivas, J.H.; Mark, H. Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report). Pure Appl. Chem. 2006, 78, 633.eng
dcterms.referencesFelipe-Sotelo, M.; Cal-Prieto, M.J.; Ferre, J.; Boque, R.; Andrade, J.M.; Carlosena, A. Linear PLS regression to cope with interferences of major concomitants in the determination of antimony by ETAAS. J. Anal. Spectrom. 2006, 21, 61–68.eng
dcterms.referencesGalan-Freyle, N.J.; Figueroa-Navedo, A.M.; Pacheco-Londoño, Y.C.; Ortiz-Rivera,W.; Pacheco-Londoño, L.C.; Hernández-Rivera, S.P. Chemometrics-enhanced fiber-optic Raman detection, discrimination and quantification of chemical agents simulants concealed in commercial bottles. Anal. Chem. Res. 2014, 2, 15–22.eng
dcterms.referencesBuitinck, L.; Louppe, G.; Blondel, M.; Pedregosa, F.; Mueller, A.; Grisel, O.; Niculae, V.; Prettenhofer, P.; Gramfort, A.; Grobler, J.; et al. API design for machine learning software: Experiences from the scikit-learn project. In Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Prague, Czech Republic, 23 September 2013.eng
dcterms.referencesKubelka, P. New Contributions to the Optics of Intensely Light-Scattering Materials. Part I. J. Opt. Soc. Am 1948, 38, 448.eng
dcterms.referencesBull, C.R. Compensation for particle size effects in near-infrared reflectance. Analyst 1991, 116, 781–786.eng
dcterms.referencesSirita, J.; Phanichphant, S.; Meunier, F.C. Quantitative Analysis of Adsorbate Concentrations by Diffuse Reflectance FT-IR. Anal. Chem. 2007, 79, 3912–3918.eng
dcterms.referencesIgne, B.; Hurburgh, C.R. Local chemometrics for samples and variables: Optimizing calibration and standardization processes. J. Chemom. 2010, 24, 75–86.eng
dc.type.spaArtículo científicospa

Ficheros en el ítem


Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos
    Artículos científicos evaluados por pares

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional