A prospective application of this technique is for the detection of crude oil in soils, which represents a severe biodiversity risk. A fundamental advantage of the MIR laser spectroscopic technique is its portability and capability for rapid analysis because the equipment can be transported to the contaminated site. Besides, it is an efficient technique for analyzing complex mixtures such as soils since it overcomes the challenges faced by other traditional methods. On the other end, several applications of artificial intelligence (AI) for real-time control of pollution minimization and mitigation processes have been recently reported. They demonstrate an emerging area for more extensive studies [38; 39]. AI is a rapidly evolving area that offers sophisticated and advanced approaches capable of addressing complicated and challenging problems. Transferring human knowledge into analytical models and learning from data is a task that can be accomplished by soft-computing methodologies [39]. Recently, some applications of AI for real-time control of pollution minimization and mitigation processes have been reported. They demonstrate an emerging area for more extensive studies [38]. AI is a rapidly evolving area that offers sophisticated and advanced approaches capable of addressing complicated and challenging problems. Transferring human knowledge into analytical models and learning from data is a task that can be accomplished by soft-computing methodologies [39]. Support vector machine (SVM) is a technique for modeling classes that includes the concept of learning. SVMs are supervised classification methods based on statistical learning theory. SVMs effectively solve problems of small sample sizes, high dimensionality, and nonlinearity in the learning process. SVMs aim to find an optimal hyperplane that maximizes the margins between those classes which have distinct features, in some instances. Currently, SVMs are widely used in recognition, facial recognition, fault classification [40; 41; 42], time series prediction [43], nonlinear system modeling and identification [44; 45], water quality event detection [46], and other fields. The use of SVMs has achieved good results [47]. SVMs have the advantage of avoiding data overfitting, which is a disadvantage of other multivariate pattern recognition methods [48]. On the other hand, traditional multivariate analysis (MVA) pattern recognition methods such as principal component analysis (PCA) and partial least squares coupled to discriminant analysis (PLS-DA), are used in conjunction with spectroscopic methods and are widely reported in the literature [33; 37; 49; 50]. PCA is a non-supervised MVA statistical technique. In the case of unknown identities and quantities, PCA is employed to sort these unknowns and to determine underlying information from multivariate raw data. PCA is often utilized to reduce the dimensionality of spectroscopic data with a linear transformation of some principal components (PCs). PLS-DA is a supervised MVA statistical technique used to group objects into classes. PLS-DA can find latent variables (LVs) with high covariance values in the dependent (y) variable. This MVA pattern recognition method attempts to identify directions and variables in multivariate space, i.e., the LVs. In the case of two classes, 0 is used to denote Class A, and 1 indicates Class B. Using a threshold defined by the operator, the separation between the classes should be clear and linear. This implies that in the optimum separation into classes, Class A should be on one side of the threshold and Class B on the other side of it. In the case where the predictions provide no clear distinction between the classes, the number of false positives increases and the model is not suitable for discrimination [33; 37; 49; 50]. In this study, QCL assisted with the recent advancements of AI-based technologies that were evaluated for the detection of petroleum pollution in soils.