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Produced water (PW) is a type of wastewater that arises during oil and gas production. Due to its potential environmental impact, PW is one of the most closely monitored forms of wastewater in the petroleum industry. The total oil and grease (TOG) content in the water is a crucial parameter for assessing the environmental impact of PW. Traditional methods for analyzing TOG in PW can be time-consuming and may not be compatible with green chemistry principles. In this study, an alternative method for classifying PW samples is proposed using a one-class classifier (OCC) model, which has proven useful for classification problems. To achieve this goal, headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography (GC×GC) were employed to obtain TOG profiles from PW. A series of simulated PW samples containing TOG were generated using a mixture design comprising four petrochemicals at concentrations ranging from 10 mg L-1 to 50 mg L-1. The polydimethylsiloxane (PDMS) fiber showed the most representative extraction of analytes. The optimization of the HS-SPME method was performed using a Doehlert design with two variables, and the final conditions were set at 80 °C and 70 min for extraction temperature and time, respectively. A pixel-based data approach was used to implement data-driven soft independent modeling by class analogy (DD-SIMCA). Although DD-SIMCA is a developing area in GC×GC studies, the proposed model produced outstanding results with a sensitivity of 94.3 %, specificity of 95.0 %, and accuracy of 94.5 %, considering the complex and broad compositional range of the modeled mixtures. These findings demonstrated the effectiveness of the OCC model approach in classifying PW samples according to environmental regulations. [Display omitted] • Total oil and grease profiles can be utilized for the classification of produced water samples according to local resolution. • A Doehlert design with two variables is suitable for optimizing the HS-SPME method. • A pixel-based data approach, utilizing two-dimensional chromatograms for multivariate data processing. • One-class classifier models are appropriate for solving classification problems using chromatographic profiles. [ABSTRACT FROM AUTHOR]

Comprehensive chemical investigation of non-volatile complex mixtures, without extensive sample pretreatment, remains challenging due to the high number of constituents with different chemical properties. In past years, direct high-resolution mass spectrometry established itself as powerful technique for the detailed molecular description of ultra-complex mixtures, but was mainly used with atmospheric pressure ionization. In this study, we present a direct inlet approach with vacuum ionization and high-resolution time-of-flight mass spectrometry. Exemplary, the non-volatile fractions of crude oil were directly inserted into the ion source and volatilized under reduced pressure conditions. An applied temperature gradient enabled thermal pre-separation, according to volatility, prior to electron ionization and mass spectrometric detection. With exact mass information, peaks were assigned to elemental compositions and grouped into component classes. Moreover, the application of supervised and unsupervised statistical tools allowed differentiation of the samples on a molecular level and the identification and attribution of significant chemical features. Image 1 • Direct analysis of petroleum vacuum residues. • Thermal preseparation of non-volatile substances before mass spectrometric detection. • Discrimination of six bitumen samples according to their molecular fingerprint. • Unsupervised classification to material type and origin. • Identification of molecular markers. [ABSTRACT FROM AUTHOR]