Marcio Pozzobon Pedroso

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing.

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: a review.

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) and coupled to mass spectrometry (GC×GC-MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.

Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography

The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data.

Quantification of Kerosene in Gasoline by Comprehensive Two-Dimensional Gas Chromatography and N-Way Multivariate Analysis

Abstract In brazil, gasoline is usually adulterated by diesel oil, ethanol (in addition to the amount legally specified), petrochemical raffinates, and kerosene. This is an illegal action performed mainly in an attempt to raise profits. Therefore, methods for reliable identification of adulterated gasoline are very attractive. The aim of this work was to propose a method to quantify kerosene in gasoline through N-way multivariate analysis and a homemade Comprehensive Two-Dimensional Gas Chromatography with Flame Ionization Detection (GC × GC-FID). Models generated by Parallel Factor Analysis (PARAFAC), PARAFAC2, and Multi-way Partial Least Squares (N-PLS) allowed the quantification of kerosene in gasoline with Root Mean Square Error of Cross-Validation (RMSECV) values of 2.98%, 2.65%, and 2.08%, respectively.

Identification of volatiles from pineapple (Ananas comosus L.) pulp by comprehensive two‐dimensional gas chromatography and gas chromatography/mass spectrometry

Combining qualitative data from the chromatographic structure of 2-D gas chromatography with flame ionization detection (GC×GC-FID) and that from gas chromatography-mass spectrometry (GC/MS) should result in a more accurate assignment of the peak identities than the simple analysis by GC/MS, where coelution of analytes is unavoidable in highly complex samples (rendering spectra unsuitable for qualitative purposes) or for compounds in very low concentrations. Using data from GC×GC-FID combined with GC/MS can reveal coelutions that were not detected by mass spectra deconvolution software. In addition, some compounds can be identified according to the structure of the GC×GC-FID chromatogram. In this article, the volatile fractions of fresh and dehydrated pineapple pulp were evaluated. The extraction of the volatiles was performed by dynamic headspace extraction coupled to solid-phase microextraction (DHS-SPME), a technique appropriate for slurries or solid matrices. Extracted analytes were then analyzed by GC×GC-FID and GC/MS. The results obtained using both techniques were combined to improve compound identifications.

Prediction of the physicochemical properties of gasoline by comprehensive two-dimensional gas chromatography and multivariate data processing.

The estimation of physicochemical parameters such as distillation points and relative densities still plays an important role in the quality control of gasoline and similar fuels. Their measurements according to standard ASTM procedures demands specific equipments and are time and work consuming. An alternative method to predict distillation points and relativity density by multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data is presented here. Gasoline samples, previously tested according to standard methods, were used to build regression models, which were evaluated by external validation. The models for distillation points were built using variable selection methods, while the model for relativity density was built using the whole chromatograms. The root mean square prediction differences (RMSPD) obtained were 0.85%, 0.48%, 1.07% and 1.71% for 10, 50 and 90% v/v of distillation and for the final point of distillation, respectively. For relative density, the RMSPD was 0.24%. These results suggest that GC×GC-FID combined with multivariate analysis can be used to predict these physicochemical properties of gasoline.

Quantitative analysis by comprehensive two-dimensional gas chromatography using interval Multi-way Partial Least Squares calibration

A new approach for target quantitative analysis for comprehensive two-dimensional gas chromatography (GC × GC), interval Multi-way Partial Least Square (iNPLS) is presented and evaluated in this paper. In iNPLS, the two-dimensional chromatogram is split in small sections; each of these pieces is treated as an independent new chromatogram. Separated conventional NPLS calibration models for the concentration of the target analyte are built for each of the pieces of the whole chromatogram, and the best model is selected for quantitative analysis. An algorithm for iNPLS running on MatLab platform was written, preliminarily evaluated with using solutions of model compounds with different chemical properties and subsequently applied to quantify some allergens in perfume samples. The results were found to be adequate, and good precision and accuracy was obtained even for poorly resolved peaks.

Cromatografia gasosa bidimensional abrangente (GC × GC)

This paper presents the fundamental principles, instrumentation and selected applications of comprehensive two-dimensional gas chromatography (GC × GC). In this technique, introduced in 1991, two capillary columns are coupled and proper modulating interfaces continuously collect the eluate from the first column, transferring it to the second column. The result is a geometric increment in the chromatographic resolution, ensuring separation of extremely complex mixtures in time periods shorter or comparable to those of analysis using conventional gas chromatography and with better detectabilities and sensitivities.

Volatiles identification in pineapple submitted to drying in an ethanolic atmosphere.

Pineapple slices were dried under normal and modified atmosphere in a lab-scale tunnel dryer. The atmosphere was modified by addition of 0.5% ethanol v/v to the air stream using two different temperatures and two different air velocities. A manual solid-phase micro-extractor coupled to a gas chromatographer/mass spectrometer (SPME-GC-MS) was used to determine the volatiles composition in fresh and dried pineapple samples. Important volatile compounds of pineapple aroma were detected in fresh as well as dried samples. The modified atmosphere promoted more rapid water evaporation and better retention of the volatile compounds upon drying.

Determination of fuel origin by comprehensive 2D GC-FID and parallel factor analysis

In this work, a method for differentiation of gasoline according to its geographical origin is presented. Comprehensive two-dimensional gas chromatography-flame ionization detection (GC×GC-FID) combined with multivariate analysis was used to differentiate Brazilian and Venezuelan gasoline samples. Pattern recognition of the GC×GC-FID chromatograms was performed by parallel factor analysis (PARAFAC) and it was successfully applied for the differentiation of these gasoline samples. Eluates with medium to high volatility, both aliphatic and aromatic, were responsible for this differentiation, based on the inspection of the score and loading graphs generated by PARAFAC.