Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics.

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2+). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

Rapeseed oil methyl ester pyrolysis: on-line product analysis using comprehensive two-dimensional gas chromatography.

Thermochemical conversion processes play a crucial role in all routes from fossil and renewable resources to base chemicals, fuels and energy. Hence, a fundamental understanding of these chemical processes can help to resolve the upcoming challenges of our society. A bench scale pyrolysis set-up has been used to study the thermochemical conversion of rapeseed oil methyl ester (RME), i.e. a mixture of fatty acid methyl esters. A GC×GC, equipped with both a flame ionization detector (FID) and a time-of-flight mass spectrometer (TOF-MS), allows quantitative and qualitative characterization of the reactor feed and product. Analysis of the latter is accomplished using a dedicated high temperature on-line sampling system. Temperature programmed analysis, starting at -40°C, permits effluent characterization from methane up to lignoceric acid methyl ester (C(25)H(50)O(2)), in a single run of the GC×GC. The latter combines a 100% dimethylpolysiloxane primary column with a 50% phenyl polysilphenylene-siloxane secondary column. Modulation is started when the oven temperature reaches 40°C, thus dividing the chromatogram in a conventional 1D and a comprehensive 2D part. The proposed quantification approach allows to combine the quantitative GC×GC analysis with 2 other on-line 1D GC analyses, resulting in a complete and detailed product composition including the measurement of CO, CO(2), formaldehyde and water. The GC×GC reveals that the product stream contains a huge variety of valuable products, such as linear alpha olefins, unsaturated esters and aromatics, that could not have been identified and quantified accurately with conventional 1D GC because of peak overlap.

On-line analysis of complex hydrocarbon mixtures using comprehensive two-dimensional gas chromatography.

This paper discusses the first setup for on-line qualitative and quantitative comprehensive two-dimensional gas chromatography (GC × GC) of complex hydrocarbon mixtures. A built-in 4-port 2-way valve allows switching between flame ionization detection (FID) and time-of-flight mass spectrometry (TOF-MS) between runs, without the need to cool down and vent the MS. Proper selection of GC carrier gas flow rates enables maximal agreement between the obtained chromatograms in both configurations. For on-line analysis of reactor effluents, a dedicated sampling system allows automatic sampling of the hot reactor effluent gases and immediate injection of the sample on the GC × GC. To determine a complete effluent composition in a single run of the GC × GC, a subzero oven starting temperature was employed. Modulation is started when the oven temperature reaches 40°C, thus dividing the chromatogram in a conventional 1D and a comprehensive 2D part. This work illustrates the mature and robust character of GC × GC, extending its capabilities from mere laboratory use to on-line routine analysis for industrial processes in the (petro-)chemical industry.