Fabio Augusto

New sorbents for extraction and microextraction techniques

This review outlines recent progress in the research on some new classes of sorbents for extraction and microextraction techniques. Carbon nanotubes are allotropes of carbon with cylindrical structure. They are very stable systems having considerable chemical inertness due to the strong covalent bonds of the carbon atoms on the nanotube surface. Some applications of carbon nanotubes are presented in a perspective view. Molecular imprinting has proved to be an effective technique for the creation of recognition sites on a polymer scaffold. By a mechanism of molecular recognition, the molecularly imprinted polymers are used as selective tools for the development of various analytical techniques such as liquid chromatography, capillary electrochromatography, solid-phase extraction (SPE), binding assays and biosensors. Sol-gel chemistry provides a convenient pathway to create advanced material systems that can be effectively utilized to solve the solid phase microextraction fiber technology problems. This review is mainly focused on recent advanced developments in the design, synthesis and application of sol-gel in preparation of coatings for the SPME fibers.

Screening of Brazilian fruit aromas using solid-phase microextraction–gas chromatography–mass spectrometry

Manual headspace solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) was used for the qualitative analysis of the aromas of four native Brazilian fruits: cupuassu (Theobroma grandiflorum, Spreng.), cajá (Spondias lutea. L.), siriguela (Spondias purpurea, L.) and graviola (Anona reticulata, L). Industrialized pulps of these fruits were used as samples, and extractions with SPME fibers coated with polydimethylsiloxane, polyacrylate, Carbowax and Carboxen were carried out. The analytes identified included several alcohols, esters, carbonyl compounds and terpernoids. The highest amounts extracted, evaluated from the sum of peak areas, were achieved using the Carboxen fiber.

Sampling and sample preparation for analysis of aromas and fragrances

Some recent advances in sampling and sample-preparation technologies for fragrance analysis are addressed in this review. Procedures, such as analytical distillation (vapor distillation and simultaneous distillation-extraction), headspace-manipulation methods (static and dynamic headspace analysis and headspace solid-phase microextraction) and direct extraction methods (such as liquid-liquid, solid-phase and supercritical fluid), will be discussed and critically evaluated. Contemporary applications of these techniques to the study of natural and synthetic aromas will be presented.

Applications of solid-phase microextraction to chemical analysis of live biological samples

This work reviews some recent applications of solid-phase microextraction (SPME) for the chemical analysis of live biological samples. Application of SPME to microbiological analysis, organic volatile compounds emitted by vegetables and insect semiochemicals will be discussed. A short discussion on the principles and the basic parameters of SPME is also included.

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.

Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares.

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples. According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.

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.

Microextração por fase sólida

Fundamental aspects of Solid Phase Micro-Extraction (SPME) are discussed in the present paper. The application of SPME as a microtechnique of sample preparation for gas chromatographic analysis is considered and related to existing theoretical models. Both research prototypes and commercial SPME devices are considered.

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.