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

Abstract

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.

API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis

“…Quantum cascade lasers (QCLs) are powerful semiconductor lasers that emit coherent high collimated MIR light [ 17 ] with a higher brightness than FTIR [ 18 ] and synchrotrons [ 19 ]. Some of the QCL applications reported include the long-distance detection of chemical compounds [ 20 , 21 , 22 ], sensing of proteins in aqueous solutions [ 23 ], quantification of explosives in soil [ 24 ] and petroleum in soil [ 25 ], and monitoring of chemical reactions [ 26 ]. These are a few of the challenging conditions wherein measurements were possible due to the high optical power of QCL.…”

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

“…Since its inception [ 45 ], artificial intelligence (AI) has shown that it can be applied to a wide spectrum of disciplines not directly related to computing. Among the most significant new uses, medicine [ 46 , 47 ], warfare [ 48 ], ecology [ 49 ], security [ 50 ], education [ 51 ], oil exploration [ 52 ], or material science [ 44 , 53 ] can be emphasized. Today, ANNs have become one of the most notable AI methods due to their incredible accomplishments and unstoppable advancement [ 10 , 54 ].…”

Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence

“…In this study, a small, portable, and easy to handle system employing mid-infrared (MIR) quantum cascade laser (QCL) source [24] was used for analyzing HEs in soils at a distance of 15 cm. Furthermore, multivariate analysis (MVA) and artificial intelligence (AI) techniques were employed to quantify and detect the analytes of interest on the soil samples studied dosed with complex matrices of organic compounds [25].…”

“…QCLs can provide significant benefits for public safety, particularly in locations such as airports, railways, bus stations, sports stadiums, and marathon sites. Therefore, this study is focused on the quantification and detection of HEs in soils [25] using a QCL source to enable remote sensing [42][43][44][45][46][47][48][49][50][51][52][53]. The limit of detection (LOD) was calculated for QCL spectroscopy based on the calibration curves of one HE in solid mixtures.…”