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ABSTRACT
This review provides a critical insight into the selection of chromatographic and spectroscopic techniques for semi-quantitative and quantitative detection of petroleum hydrocarbons in soil and sediment matrices. Advantages and limitations of both field screening and laboratory-based techniques are discussed and recent advances in chemometrics to extract maximum information from a sample by using the optimal pre-processing and data mining techniques are presented. An integrated analytical framework based on spectroscopic techniques integration and data fusion for the rapid measurement and detection of on-site petroleum hydrocarbons is proposed. Furthermore, factors influencing petroleum hydrocarbons analysis in contaminated samples are discussed and recommendations on how to reduce their influence provided.
Nomenclature
| ANN | = | Artificial neural network |
| ASTM | = | American Society of Testing and Materials |
| BRT | = | Boosted regression tree |
| BTEX | = | Benzene, toluene, ethylbenzene, xylene |
| CWA | = | Chemical warfare agent |
| DCM | = | Dichloromethane |
| DRS | = | Diffuse reflectance spectroscopy |
| ED-XRF | = | Energy dispersive X-ray fluorescence |
| EPA | = | Environment protection agency |
| FGC | = | Field gas chromatography |
| FID | = | Flame ionization detector |
| GC | = | Gas chromatography |
| GC × GC | = | Two-dimensional gas chromatography |
| H | = | High |
| IRS | = | Infrared spectroscopy |
| L | = | Low |
| LC | = | Liquid chromatography |
| LMW | = | Low molecular weight |
| LOD | = | Limit of detection |
| M | = | Medium |
| MC | = | Moisture content |
| MLR | = | Multiple linear regression |
| MPLC | = | Medium pressure liquid chromatography |
| MIRS | = | Mid-infrared spectroscopy |
| MMW | = | Medium molecular weight |
| MS | = | Mass spectrometry |
| MTBE | = | Methyl tertiary butyl ether |
| OCLC | = | Open-column liquid chromatography |
| OLR | = | Ordinal logistic regression |
| PLSR | = | Partial least squares regression |
| PAH | = | Polycyclic aromatic hydrocarbon |
| PGC | = | Portable gas chromatography |
| PHC | = | Petroleum hydrocarbon |
| PHLC | = | High performance liquid chromatography |
| PSR | = | Penalized spline regression |
| QTOF | = | Quadrupole time-of-flight |
| R2 | = | Coefficient of determination |
| RFR | = | Random forest regression |
| RI | = | Retention index |
| RMSE | = | Root mean square error |
| RMSEP | = | Root mean square error of prediction |
| RPD | = | Residual prediction deviation |
| RS | = | Raman spectroscopy |
| SimDis | = | Simulated distillation |
| SFC | = | Supercritical fluid chromatography |
| SOM | = | Soil organic matter |
| SUSE-GC | = | Sequential ultrasonic solvent extraction gas chromatography |
| SVM | = | Support vector machine |
| TAME | = | Tert-amyl methyl ether |
| TC | = | Total carbon |
| TGC | = | Transportable gas chromatography |
| TPH | = | Total petroleum hydrocarbon |
| TIC | = | Toxic industrial chemicals |
| TN | = | Total nitrogen |
| TOF | = | Total time-of-flight |
| TTEC | = | Total toxicity equivalent concentration |
| TLC | = | Thin layer chromatography |
| UE | = | European Union |
| Vis-NIR | = | Visible and near infrared spectroscopy |
| VOC | = | Volatile organic compound |
| WD-XRF | = | Wavelength dispersive X-ray fluorescence |
| XRFS | = | X-ray fluorescence spectroscopy |
Acknowledgments
The authors gratefully acknowledge the Petroleum Technology Development Fund (PTDF) of Nigeria Grant ID: PTDF/OSS/PHD/711/14, whose financial assistance in the form of doctoral studentship has led to this publication.