A method for the detection of hydrocarbon pollution in soils by headspace mass spectrometry and pattern recognition techniques.

In the present work, we report a methodology for the rapid detection of soil pollution by hydrocarbons that is based on direct coupling of a headspace sampler with a mass spectrometer. With no prior treatment, the samples are subjected to the headspace generation process and the volatiles generated are introduced directly into the mass spectrometer, thereby obtaining a fingerprint of the sample analyzed. The mass spectrum corresponding to the mass/charge ratios (m/z) ranging between 49 and 160 atomic mass units (amu) contains the information related to the composition of the headspace and is used as the analytical signal for the characterization of the samples. Chemometric treatments, such as hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), and soft independent modeling class analogy (SIMCA) were used to characterize the different types of samples analyzed. The main advantage of the proposed methodology is that no prior chromatographic separation and no sample manipulation are required. The method is rapid, simple, and in view of the results, highly suitable for detecting pollution in soils polluted by hydrocarbons.