Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications....

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Autores:: Galán-Freyle, Nataly J.
Ospina-Castro, María L.
Medina-González, Alberto R.
Villarreal-González, Reynaldo
Hernández-Rivera, Samuel P.
Pacheco-Londoño, Leonardo C.

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Universidad Simón Bolívar

Repositorio:: Repositorio Digital USB

Idioma:: eng

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dc.title.eng.fl_str_mv	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
title	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
spellingShingle	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils Mid-infrared (MIR) laser spectroscopy Quantum cascade lasers (QCLs) Artificial intelligence (AI) Chemometrics Multivariate analysis Petroleum Soil
title_short	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
title_full	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
title_fullStr	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
title_full_unstemmed	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
title_sort	Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
dc.creator.fl_str_mv	Galán-Freyle, Nataly J. Ospina-Castro, María L. Medina-González, Alberto R. Villarreal-González, Reynaldo Hernández-Rivera, Samuel P. Pacheco-Londoño, Leonardo C.
dc.contributor.author.none.fl_str_mv	Galán-Freyle, Nataly J. Ospina-Castro, María L. Medina-González, Alberto R. Villarreal-González, Reynaldo Hernández-Rivera, Samuel P. Pacheco-Londoño, Leonardo C.
dc.subject.eng.fl_str_mv	Mid-infrared (MIR) laser spectroscopy Quantum cascade lasers (QCLs) Artificial intelligence (AI) Chemometrics Multivariate analysis Petroleum Soil
topic	Mid-infrared (MIR) laser spectroscopy Quantum cascade lasers (QCLs) Artificial intelligence (AI) Chemometrics Multivariate analysis Petroleum Soil
description	A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the e ectiveness of rapidly di erentiating between di erent soil types and detecting the presence of petroleum traces in di erent soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-02-17T22:51:53Z
dc.date.available.none.fl_str_mv	2020-02-17T22:51:53Z
dc.date.issued.none.fl_str_mv	2020
dc.type.eng.fl_str_mv	article
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dc.type.driver.eng.fl_str_mv	article
dc.identifier.issn.none.fl_str_mv	20763417
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12442/4758
dc.identifier.doi.none.fl_str_mv	10.3390/app10041319
identifier_str_mv	20763417 10.3390/app10041319
url	https://hdl.handle.net/20.500.12442/4758
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.rights.none.fl_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.format.mimetype.spa.fl_str_mv	pdf
dc.publisher.eng.fl_str_mv	MDPI
dc.source.eng.fl_str_mv	Applied Sciences
dc.source.spa.fl_str_mv	Vol. 10, N° 4 (2020)
institution	Universidad Simón Bolívar
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spelling	Galán-Freyle, Nataly J.3fcf4986-14a3-49f8-9db0-165bd9e7b51fOspina-Castro, María L.59a0228b-703d-410b-8af1-d64112bcb200Medina-González, Alberto R.389640d5-9a7a-4a8b-9d1c-e80b3d8f9dc1Villarreal-González, Reynaldo0b64215d-5c8b-4e4d-b796-746ffe6b54feHernández-Rivera, Samuel P.ead5cd23-9551-47a2-962c-4a2b7f82539bPacheco-Londoño, Leonardo C.fa7d491b-ee0d-46b6-99d0-ce4a9b8c87632020-02-17T22:51:53Z2020-02-17T22:51:53Z202020763417https://hdl.handle.net/20.500.12442/475810.3390/app10041319A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the e ectiveness of rapidly di erentiating between di erent soil types and detecting the presence of petroleum traces in di erent soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.pdfengMDPIAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Applied SciencesVol. 10, N° 4 (2020)Mid-infrared (MIR) laser spectroscopyQuantum cascade lasers (QCLs)Artificial intelligence (AI)ChemometricsMultivariate analysisPetroleumSoilArtificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soilsarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Aislabie, J.M.; Balks, M.R.; Foght, J.M.;Waterhouse, E.J. Hydrocarbon Spills on Antarctic Soils: Effects and Management. Environ. Sci. Technol. 2004, 38, 1265–1274.James, W.W.; Bob, K.L.; Randall, J.C. Exposure Assessment Modeling for Hydrocarbon Spills into the Subsurface. In Transport and Remediation of Subsurface Contaminants; American Chemical Society: Washington, DC, USA, 1992; Volume 491, pp. 217–231.Lehikoinen, A.; Hanninen, M.; Storgárd, J.; Luoma, E.; Mäntyniemi, S.; Kuikka, S. A Bayesian Network for Assessing the Collision Induced Risk of an Oil Accident in the Gulf of Finland. Environ. Sci. Technol. 2015, 49, 5301–5309.Yim, U.H.; Kim, M.; Ha, S.Y.; Kim, S.; Shim,W.J. Oil Spill Environmental Forensics: The Hebei Spirit Oil Spill Case. Environ. Sci. Technol. 2012, 46, 6431–6437.Li, Y.; Li, B. Enzymatic Activities in Soils Contaminated with Diesel Oil. In Proceedings of the 2010 International Conference on Digital Manufacturing and Automation (ICDMA), Changsha, China, 18–20 December 2010; pp. 659–662.Liang, Q.; Zhang, B.; Wu, X. Gulf of Mexico oil spill impact on beach soil: UWB radars-based approach. In Proceedings of the 2012 IEEE GlobecomWorkshops (GC Wkshps), Anaheim, CA, USA, 3–7 December 2012; pp. 1445–1449.Dahish, A.S.; Ahmad, A. 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Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils

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