Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence
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...
- Autores:
-
Pacheco-Londoño, Leonardo C.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/1002
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/1002
- Palabra clave:
- quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| title |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| spellingShingle |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning |
| title_short |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| title_full |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| title_fullStr |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| title_full_unstemmed |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| title_sort |
Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence |
| dc.creator.fl_str_mv |
Pacheco-Londoño, Leonardo C. |
| dc.contributor.author.none.fl_str_mv |
Pacheco-Londoño, Leonardo C. |
| dc.contributor.other.none.fl_str_mv |
Warren, Eric Galán-Freyle, Nataly J. Villarreal-González, Reynaldo Aparicio-Bolaño, Joaquín A. Ospina-Castro, María L. Shih, Wei-Chuan Hernández-Rivera, Samuel P. |
| dc.subject.keywords.spa.fl_str_mv |
quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning |
| topic |
quantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learning |
| description |
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 di erent 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 di erent 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. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-06-20 |
| dc.date.submitted.none.fl_str_mv |
2020-04-02 |
| dc.date.accessioned.none.fl_str_mv |
2022-11-15T21:27:05Z |
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2022-11-15T21:27:05Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Artículo |
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publishedVersion |
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https://hdl.handle.net/20.500.12834/1002 |
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10.3390/app10124178 |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://hdl.handle.net/20.500.12834/1002 |
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10.3390/app10124178 Universidad del Atlántico Repositorio Universidad del Atlántico |
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eng |
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eng |
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Pacheco-Londoño, Leonardo C.99ffd41f-d01c-4fa9-ad1a-7546da8394f4Warren, EricGalán-Freyle, Nataly J.Villarreal-González, ReynaldoAparicio-Bolaño, Joaquín A.Ospina-Castro, María L.Shih, Wei-ChuanHernández-Rivera, Samuel P.2022-11-15T21:27:05Z2022-11-15T21:27:05Z2020-06-202020-04-02https://hdl.handle.net/20.500.12834/100210.3390/app10124178Universidad del AtlánticoRepositorio Universidad del AtlánticoA 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 di erent 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 di erent 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.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2MDPI AGMid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial IntelligencePúblico generalquantum cascade laser; remote detection; partial least squares; high explosives; artificial intelligence; machine learninginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaQuímicaSede Norte1. Frische, T.; Höper, H. Soil microbial parameters and luminescent bacteria assays as indicators for in situ bioremediation of TNT-contaminated soils. Chemosphere 2003, 50, 415–4272. Correa-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, 30–33.3. Hildenbrand, J.; Herbst, J.; Wöllenstein, J.; Lambrecht, A. Explosive detection using infrared laser spectroscopy. Proc. SPIE 2009, 7222, 72220B.4. Narang, U.; Gauger, P.R.; Ligler, F.S. A Displacement Flow Immunosensor for Explosive Detection Using Microcapillaries. Anal. Chem. 1997, 69, 2779–2785.5. Hilmi, 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, 3046–3050.6. Kumar, S.; Venkatramaiah, N.; Patil, S. Fluoranthene Based Derivatives for Detection of Trace Explosive Nitroaromatics. J. Phys. Chem. C 2013, 117, 7236–7245.7. Sheremata, 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, 1037–1040.8. Larson, 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, 786–792.9. Marple, R.L.; LaCourse,W.R. Application of Photoassisted Electrochemical Detection to Explosive-Containing Environmental Samples. Anal. Chem. 2005, 77, 6709–6714.10. Gallagher, 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, 271–281.11. Forouzangohar, 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, 3283–3288.12. Gallagher, N.B.; Gassman, P.L.; Blake, T.A. 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Chemom. 2010, 24, 75–86.http://purl.org/coar/resource_type/c_6501ORIGINALapp10124178.pdfapp10124178.pdfapplication/pdf2060852https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1002/1/app10124178.pdfb8d1336815c609455161ee63782be852MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1002/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/1002/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/1002oai:repositorio.uniatlantico.edu.co:20.500.12834/10022022-11-15 16:27:06.664DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.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 |