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In this article, we present and discuss an alternative for data analysis of the metabolic profiles of both healthy Eucalyptus globulus and those infected with the Mycosphaerella leaf disease. The crude extracts were analyzed by reversed-phase ultra performance liquid chromatography-mass spectrometry. In order to glean the most useful information from these complex measurements, parallel factor analysis (PARAFAC) was employed for pattern recognition. After PARAFAC modeling, inspection of the scores and loadings graph allowed distinction of the healthy from the infected E. globulus samples and determination of biomarkers related to the biotic stress. The assessment of the monoisotopic masses and the fragmentation patterns allowed the identification of these biomarkers. It is hoped that the proposed method can be used for the diagnosis of diseases in plants, as well as to provide additional insight into the plant’s defense mechanism. Potentially, this may demonstrate the advantages of employing high order chemometric techniques in metabolomic data analysis.

The bioactive compounds of freeze-dried jaboticaba peel (FJP), serum lipids and the antioxidant status of rats fed high-fat diets containing different doses of FJP were evaluated. Obesity was induced and three groups of the obese animals received high-fat diet with 1%, 2% and 4% FJP added (J1, J2 and J4, respectively). The HPLC–DAD analyses of the polyphenols in the FJP showed four compounds: gallic acid, cyanidin 3-glucoside, ellagic acid, and quercetin. The serum saturated fatty acids were reduced in J1 and J4-fed animals. The antioxidant defenses of plasma were increased in the J2 and J4-fed animals. All the FJP diets prevented lipid peroxidation in the liver and increased its antioxidant defenses. Lipid peroxidation in brain decreased with an increasing FJP content in the diet. The antioxidant status of the kidneys of J2 and J4-fed animals increased. Thus, FJP could be an alternative to minimize the high-fat diet-induced oxidative stress and circulating saturated lipids.

The relationship between rheumatoid arthritis and risk of vertebral fracture has been reported by several observational studies. However, there is no higher-level evidence study, such as meta-analysis, that has investigated the relationship, and its mechanisms are not yet fully clear. This meta-analysis aimed to provide a summary of an observational study of the relationship between rheumatoid arthritis and the risk of vertebral fractures. Relevant studies were identified by searching PubMed and EMBASE databases (up to August 1, 2016). We included published observational studies (cohort or case-control design) evaluating the relationship between rheumatoid arthritis and the risk of vertebral fractures. Two reviewers searched and abstracted the data independently. Relative risks (RRs) with 95% confidence intervals (CIs) were used throughout the whole analysis. Seven observational studies (2 cohort studies, 2 nested case-control studies, and 3 case-control studies) were included in this meta-analysis. The results showed that the pooled RR of vertebral fracture for individuals with rheumatoid arthritis was 2.34 (95% CI 2.05–2.63, I2 = 35.4%, P for heterogeneity = 0.16). Further subgroup analysis by sex showed that the pooled RRs for both women and men, and only women were 2.14 (95% CI 1.47–2.8, I2 = 48.5%, P for heterogeneity = 0.12) and 2.39 (95% CI 2.07–2.70, I2 = 34%, P for heterogeneity = 0.22), respectively. Subgroup analysis by study design showed that the pooled RRs for cohort studies, nested case-control studies, and case-control studies were 2.31 (95% CI 1.95–2.67, I2 = 4.8%, P for heterogeneity = 0.31), 1.89 (95% CI 1.01–2.77, I2 = 72.1%, P for heterogeneity = 0.06), and 2.62 (95% CI 2.04–3.91, I2 = 26.1%, P for heterogeneity = 0.26), respectively. Based on our meta-analysis, rheumatoid arthritis should be regarded as an independent risk factor of vertebral fracture. Further studies are needed to institute prevention and treatment strategies.

265 Application of IL-DLLME in the Analysis of Pharmaceutical Entities 265 IL-DLLME in the Analysis of Metal Ions 266 Application of IL-DLLME in the Analysis of Organic Environmental Pollutants 266

Extraction using headspace solid phase microextraction (HS-SPME) coupled to comprehensive two-dimensional gas chromatography with flame ionisation detection (GC×GC–FID) was employed to evaluate the effect of SPME fractionation conditions (heating time and temperature) on the generation of artifacts. The occurrence of artifacts was more pronounced at higher fractionation temperatures and times which caused significant changes in the chromatographic profiles. The identification of the volatile fraction of the honey blend was performed through a two-dimensional gas chromatograph coupled to a mass spectrometer with time of flight analyser (GC×GC–ToFMS) by comparing the first dimension linear temperature programmed retention index (1D-LTPRI) with the peak’s identities provided by the mass spectral similarity search. Several artifacts were found and identified – such as hydroxymethylfurfural, methyl–furone and furfural – and some of them were not previously detected as such in honey samples. These compounds were either the result of hydrolysis or thermal decomposition of components already present in the honey samples. This occurrence was attributed to the increased detectability provided by GC×GC compared to conventional GC. The possible emergence of previously unknown extraction artifacts as a general tendency related use of GC×GC instead of conventional GC is discussed as a result of these observations.

In this paper it is reported the use of the chromatographic profiles from volatile fractions of plant clones – in this case, hybrids of Eucalyptus grandis×Eucalyptus urophylla – to determine specimens susceptible to rust disease. The analytes were isolated by headspace solid phase microextraction (HS-SPME) and analyzed by comprehensive two-dimensional gas chromatography combined to fast quadrupole mass spectrometry (GC×GC-qMS). Parallel Factor Analysis (PARAFAC) was employed for estimate the correlation between the chromatographic profiles and resistance against Eucalyptus rust, after preliminary variable selection performed by Fisher ratio analysis. The proposed method allowed the differentiation between susceptible and non-susceptible clones and determination of three resistance biomarkers. This approach can be a valuable alternative for the otherwise time-consuming and labor-intensive methods commonly used.

In this paper, a method to determine the composition of blends of biodiesel with mineral diesel (BXX) by multivariate curve resolution with Alternating Least Squares (MRC-ALS) combined to comprehensive two-dimensional gas chromatography with Flame Ionization Detection (GC×GC-FID) is presented. Chromatographic profiles of BXX blends produced with biodiesels from different sources were used as input data. An initial evaluation carried out after multiway principal component analysis (MPCA) was used to reveal regions of the chromatograms were the signal was likely to be dependent on the concentration of biodiesel, regardless its vegetable source. After this preliminary step MCR-ALS modeling was carried out only using relevant parts of the chromatograms. The resulting procedure was able to predict accurately the concentration of biodiesel in the BXX samples regardless of its origin.

In this article, we outline and discuss some of the most recent tendencies on the research related to novel sorbent materials for solid-phase extraction (SPE). Hemimicelles and admicelles are structures formed from solutions of ionic surfactants, which can be sorbed on the surfaces of active solids, resulting in sorbents capable of simultaneously extracting a wide range of analytes with extremely varied polarity. The performance of another important class of new SPE materials is based on molecular recognition, which mimics the selective or specific affinity of several biomolecules towards their target compounds: these sorbents include molecularly-imprinted materials, immunosorbents and aptamer-modified surfaces. Finally, there is also a growing interest on sorbents based on nanostructured materials (e.g., electrospun nanofibers and carbon nanotubes).

The combination of solid-phase microextraction (SPME) with comprehensive two-dimensional gas chromatography is evaluated here for fatty acid (FA) profiling of the glycerophospholipid fraction from human buccal mucosal cells. A base-catalyzed derivatization reaction selective for polar lipids such as glycerophospholipid was adopted. SPME is compared to a miniaturized liquid-liquid extraction procedure for the isolation of FA methyl esters produced in the derivatization step. The limits of detection and limits of quantitation were calculated for each sample preparation method. Because of its lower values of limits of detection and quantitation, SPME was adopted. The extracted analytes were separated, detected, and quantified by comprehensive two-dimensional gas chromatography with flame ionization detection (FID). The combination of SPME and comprehensive two-dimensional gas chromatography with FID, using a selective derivatization reaction in the preliminary steps, proved to be a simple and fast procedure for FA profiling, and was successfully applied to the analysis of adult human buccal mucosal cells.

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.

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.

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.

Since their original discovery in 1914, ionic liquids (IL) have been widely examined and explored in chemistry due to their unique physical and chemical properties. Ionic liquids are collectively known as organic salts and have melting points of 100 °C or under. The molten salts most employed in analytical chemistry, including gas chromatography (GC), consist of an organic cation paired with an organic or inorganic anion. This class of materials exhibits negligible vapor pressure and may have their properties (e.g.thermal stability and selectivity) structurally tuned by imparting different moieties to the cation/anion. Currently, there are an estimated 1018possible combinations of IL. In this context, the prospection of highly selective IL-based stationary phases for gas-liquid chromatography has enabled high peak capacity and efficient separations of many critical pairs in complex samples. In this review, we present and discuss fundamental characteristics of ionic liquids and introduce important solvation models for gas-liquid systems. In addition, recent advances and applications of IL in conventional and multidimensional gas chromatography are outlined.

A series of dicationic ionic liquid (IL)-based stationary phases were evaluated as secondary columns in comprehensive two-dimensional gas chromatography (GC×GC) for the separation of aliphatic hydrocarbons from kerosene. In order to understand the role that structural features of ILs play on the selectivity of nonpolar analytes, the solvation parameter model was used to probe the solvation properties of the IL-based stationary phases. It was observed that room temperature ILs containing long free alkyl side chain substituents and long linker chains between the two cations possess less cohesive forces and exhibited the highest resolution of aliphatic hydrocarbons. The anion component of the IL did not contribute significantly to the overall separation, as similar selectivities toward aliphatic hydrocarbons were observed when examining ILs with identical cations and different anions. In an attempt to further examine the separation capabilities of the IL-based GC stationary phases, columns of the best performing stationary phases were prepared with higher film thickness and resulted in enhanced selectivity of aliphatic hydrocarbons.