## 4 Conclusions  
This study demonstrated that new spectroscopic techniques, such as MIR laser spectroscopy, combined with PLS-DA and SVM, can be used for the development of practical methods for in situ detection in challenging matrices, such as sensing petroleum traces in soils. Powerful multivariate routines, such as PCA, were applied to reduce the number of primary variables and to separate the spectral signals from different solid matrices (e.g., clay soils and sandy soils). The reflectance signals of petroleum at 1450 and 1380 cm\({}^{-1}\) associated with the asymmetric and symmetric deformation of CH\({}_{3}\) were easily identified in the spectra from soil mixtures. However, the identification of petroleum signals in sea sand samples required a spectral zoom of 40 times. The detection of petroleum in sea sand was much more complicated due to the sea sand samples which absorb MIR radiation profusely in the region between 1300 to 1600 cm\({}^{-1}\). This was solved by generating specific discriminant models for each soil type. The proposed QCL/PLS-DA methodology was successfully tested and used on laboratory samples and provides a quick and simple way to analyze soils contaminated with petroleum. The methodology can be adapted for field applications. Although the experiments were limited to only two types of particulate materials (whole soils and sand), the technique is promising for detecting petroleum contamination based on the results obtained. Future work can involve expanding the models to other soil types and possibly to use other robust pattern recognition techniques. The QCL/SVM methodology demonstrated superior performance because a nonlinear classification was implemented. For low petroleum concentrations, the behavior was not linear, and for this reason, this model included these points. The SVM model improved the detection and decision limits and yielded extrapolated values of 0.02% and 0.01%, respectively. Even though an extrapolation procedure calculated these values, their certainty probability is lower than for QCL/PLS-DA. However, the QCL/SVM methodology had smaller values of LOD (0.25%) and LD (0.02%). **Supplementary Materials:** The following is available online at [[http://www.mdpi.com/2076-3417/10/4/1319/s1](http://www.mdpi.com/2076-3417/10/4/1319/s1)]([http://www.mdpi.com/2076-3417/10/4/1319/s1](http://www.mdpi.com/2076-3417/10/4/1319/s1)). Figure S1. PLS-DA model for calibrating the discrimination of several soil types with and without petroleum traces, using the spectral region from 1000 to 1600 cm\({}^{-1}\). Figure S2: Improved PLS-DA model for calibrating the discrimination of various types of soils, with and without petroleum traces, using the spectral region from 1300 to 1600 cm\({}^{-1}\). Figure S3: PLS-DA model calibrating the discrimination of red and brown soils containing 0.2% petroleum and without petroleum traces in the spectral region of 1000 to 1600 cm\({}^{-1}\). Figure S4: PLS-DA model for calibrating the discrimination of red and brown soils containing 5% petroleum and without petroleum traces in the region of 1000 to 1600 cm\({}^{-1}\). Figure S5: PLS-DA model for calibrating the discrimination of red and brown soils containing 1000 to 1600 cm\({}^{-1}\). Figure S6: PLS-DA model for calibrating the discrimination of red and brown soils in the region of 1000 to 1600 cm\({}^{-1}\). Figure S7: PLS-DA model for calibrating the discrimination of sands with 0.0625% petroleum and without petroleum traces in the region from 1000 to 1600 cm\({}^{-1}\); Figure S8: PLS-DA model for calibrating the discrimination of sands with 0.125% petroleum and without petroleum traces in the region of 1000 to 1600 cm\({}^{-1}\). Figure S9: PLS-DA model for calibrating the discrimination of sands with 0. 25% petroleum and without petroleum traces in the spectral region from 1000 to 1600 cm\({}^{-1}\). Figure S10: PLS-DA model for calibrating the discrimination of sands with 0. 5% petroleum and without petroleum traces in the spectral region from 1,000 to 1,600 cm\({}^{-1}\).