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Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils

dc.contributor.authorGalán-Freyle, Nataly J.
dc.contributor.otherOspina-Castro, María L.
dc.contributor.otherMedina-González, Alberto R.
dc.contributor.otherVillarreal-González, Reynaldo
dc.contributor.otherHernández-Rivera, Samuel P.
dc.contributor.otherPacheco-Londoño, Leonardo C.
dc.date.accessioned2022-11-15T20:56:34Z
dc.date.available2022-11-15T20:56:34Z
dc.date.issued2020-02-15
dc.date.submitted2020-01-15
dc.identifier.urihttps://hdl.handle.net/20.500.12834/917
dc.description.abstractA 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.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceMDPI AGspa
dc.titleArtificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soilsspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.audiencePúblico generalspa
dc.identifier.doi10.3390/app10041319
dc.identifier.instnameUniversidad del Atlánticospa
dc.identifier.reponameRepositorio Universidad del Atlánticospa
dc.rights.ccAttribution-NonCommercial 4.0 International*
dc.subject.keywordsmid-infrared (MIR) laser spectroscopy; quantum cascade lasers (QCLs); artificial intelligence (AI); chemometrics; multivariate analysis; petroleum; soilspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.type.spaArtículospa
dc.publisher.placeBarranquillaspa
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessspa
dc.publisher.disciplineQuímicaspa
dc.publisher.sedeSede Nortespa


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