Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils
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Fecha
2020
Autores
Galán-Freyle, Nataly J.
Ospina-Castro, María L.
Medina-González, Alberto R.
Villarreal-González, Reynaldo
Hernández-Rivera, Samuel P.
Pacheco-Londoño, Leonardo C.
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MDPI
Resumen
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.
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Palabras clave
Mid-infrared (MIR) laser spectroscopy, Quantum cascade lasers (QCLs), Artificial intelligence (AI), Chemometrics, Multivariate analysis, Petroleum, Soil
