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

1
School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia
2
Grupo de Investigación Química Supramolecular Aplicada, Programa de Química, Universidad del Atlántico, Barranquilla 080001, Colombia
3
Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA 02467, USA
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Grupo de Investigación en Gestión de la Innovación y la Tecnología, Universidad Simón Bolívar, Barranquilla 080002, Colombia
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ALERT DHS Center of Excellence for Explosives Research, Department of Chemistry, University of Puerto Rico, Mayagüez, PR 00681, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1319; https://doi.org/10.3390/app10041319
Received: 15 January 2020 / Revised: 30 January 2020 / Accepted: 31 January 2020 / Published: 15 February 2020
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 effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different 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. View Full-Text
Keywords: mid-infrared (MIR) laser spectroscopy; quantum cascade lasers (QCLs); artificial intelligence (AI); chemometrics; multivariate analysis; petroleum; soil mid-infrared (MIR) laser spectroscopy; quantum cascade lasers (QCLs); artificial intelligence (AI); chemometrics; multivariate analysis; petroleum; soil
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MDPI and ACS Style

Galán-Freyle, N.J.; Ospina-Castro, M.L.; Medina-González, A.R.; Villarreal-González, R.; Hernández-Rivera, S.P.; Pacheco-Londoño, L.C. Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils. Appl. Sci. 2020, 10, 1319.

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