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A new consumable-free three-dimensional comprehensive gas chromatography (GC3-FID) was designed with modulators already known to comprehensive two-dimensional gas chromatography (GC × GC) users to showcase the opportunity of implementing GC3 systems. A hybrid interface composed of a thermal desorption modulator and a fast forward-flush differential flow modulator was used with modulation periods of 6 s and 300 ms, respectively. The determination of allergens in perfumes associated with chemometric tools, namely, parallel factor analysis (PARAFAC) was accomplished to demonstrate the validity of the proposed system. Evaluated allergens included already regulated ones in Brazil and Europe, such as geraniol and menthol, as well as potential allergens recommended in a 2011 opinion reported by the European Scientific Committee on Consumer Safety. Limits of detection were found to be suitable for current regulations, under 0.001% or 10 µL L−1 for leave-on products. Considerations on the required acquisition rates, the limitations imposed by current modulator technology, the modulation periods used, and how they affect all three dimensions are thoroughly discussed. A GC3 coupled with mass spectrometry (GC3-MS) approach is also covered, as well as a comparison of the signal resolution/deconvolution with the increase of data dimensionality. Lastly, the union of GC3 and chemometric tools allowed for a solution that may be used for both qualitative and quantitative analyses.

In this study, we describe the development of an analytical method to profile naphthenic acids (NAs) from produced water (PW). The NAs were isolated by hollow fiber liquid-phase microextraction (HF-LPME). A microwave-assisted methylation method was used to convert the free acids into its corresponding naphthenic methyl esters (NAMEs). The best reaction conditions were ascertained using central composite design. The optimized sample preparation method exhibited an improved analytical eco-scale value (80 vs. 61) compared to conventional liquid-liquid extraction. Although the primary goal was qualitative analysis of NAMEs (e.g., group-type separation) in produced water, the quantitative performance was also evaluated for future investigations. The instrumental detection and quantification limits were 0.10 ng mL−1 and 0.16 ng mL−1, respectively, using full spectrum data acquisition. The accuracy and precision of the proposed method ranged from 90.4 to 96.6 % and 3.3 to 13.1 %, respectively, using matrix-matched working solutions (0.1, 0.5, and 1.0 µg mL−1). The monoisotopic masses of the adduct ions ([M+H]+) and its corresponding fine isotopic patterns were used to determine the elemental composition of the NAMEs in the PW samples. Qualitative analysis indicated the O2 class as the predominant class in all samples with carbon numbers ranging from C5 to C19 and double bond equivalent (DBE) values of 1 to 8. Additional classes of polar compounds, i.e., O3, O4 and nitrogen-containing classes, are reported for the first time by gas chromatography coupled to Fourier transform Orbitrap mass spectrometry and chemical ionization.

Made available in DSpace on 2021-06-25T10:29:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-04-16 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Lippia alba (Mill.) N. E. Br. (Verbenaceae) is an aromatic shrub whose essential oils have stood out as a promising source for application in several industrial fields. In this study, the essential oils chemical characterization of eight new L. alba genotypes was performed. The selected materials were collected from the Active Germplasm Bank of the Agronomic Institute and the essential oils were extracted by hydrodistillation. Flow-modulated comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was employed for chemical characterization and evaluation of possible co-eluted compounds. In addition, the chemical analyses were submitted to multivariate statistical analyses. From this investigation, 73 metabolites were identified in the essential oils of the genotypes, from which α-pinene, β-myrcene, 1,8-cineole, linalool, neral, geranial, and caryophyllene oxide were the most abundant compounds among the accessions. This is the first report disclosing α-pinene in higher amounts in L. alba (19.69%). In addition, sabinene, trans-verbenol, myrtenol, (E)-caryophyllene, α-guaiene, germacrene D, and α-bulnesene were also found in relevant quantities in some of the genotypes, and myrtenal and myrtenol could be well separated through the second dimension. Such results contributed to the understanding of the chemical composition of those new genotypes, being important to drive a future industrial applicability and studies in genetic breeding. Vegetable Genetic Resources Center Agronomic Institute School of Agriculture São Paulo State University (Unesp) Institute of Chemistry University of Campinas (Unicamp) School of Agriculture São Paulo State University (Unesp) FAPESP: 2018/26066-1

This chapter discusses the fundamentals of gas chromatography (GC) to improve method development for metabolic profiling of complex biological samples. The selection of column geometry and phase ratio impacts analyte mass transfer, which must be carefully optimized for fast analysis. Stationary phase selection is critical to obtain baseline resolution of critical pairs, but such selection must consider important aspects of metabolomic protocols, such as derivatization and dependence of analyte identification on existing databases. Sample preparation methods are also addressed depending on the sample matrix, including liquid-liquid extraction and solid-phase microextraction.

Gas chromatography is the most important separation technique in petroleum industry. Application of conventional and multidimensional gas chromatography has led to significant improvements in the analysis of petroleum products. In this chapter, we present common practices and recent developments in gas chromatographic methods used in oil and gas industry, namely organic geochemical analyses and refinery analyses.

Nitrosamines are a concerning group of carcinogens, which have gained increasing attention over the last years, frequently found in drinking and recycled water systems. In this work, an analytical method was developed for the detection and quantification of seven nitrosamines (NDMA, NMEA, NPYR, NDEA, NPIP, NDPA, and NDBA) in drinking water. The method is based on gas chromatography coupled with a single quadrupole mass spectrometer (GC-MS) with electron ionization (EI) mode. Sample enrichment and matrix cleanup by solid-phase extraction (SPE) were performed using the US EPA Method 521 cartridge packed with coconut-based charcoal. The simple method allowed reliable identification and quantification of nitrosamines in the water at nanogram per liter levels. The optimized method was validated at three concentration levels (20, 100, and 200 ng L-1) in ultrapure and drinking water samples. Average recoveries were 63-87% for ultrapure water and 38-79% for drinking water with relative standard deviations (RSD) below 10% for both matrices. Method detection limits were 1.23-3.14 ng L-1. The described method was applied to eighteen drinking water samples collected from 13 cities of the Metropolitan Region of Campinas (Sao Paulo, Brazil). NDMA was the most frequent nitrosamine detected (89% of samples) and had the highest concentration level found (67 ng L-1). The levels of nitrosamines found in drinking water samples are of considerable relevance since the selected area is a major urban center that has amply expanded industrial and agricultural activities over the last few decades. To the best of our knowledge, this is the first evaluation of nitrosamines in drinking water conducted in Brazil to date.

In this paper is reported a proof of concept study to evaluate the usage of a public metadata base about beers to guide chemical interpretation of volatile organic compounds (VOC) profiling. 1,569,641 consumers’ evaluations were collected from Untappd® platform and used to define a property of interest according to beer preference. 14 brands of beers from lager family were divided in two groups, first one containing samples with low consumers’ ratings and the second with brands that exhibited high evaluations. VOC profiles were extracted by headspace solid phase microextraction (HS-SPME) and analyzed using comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS). To correlate the VOC profile and consumers’ preference, unfolded-partial least squares discriminant analysis (U-PLS-DA) with orthogonal signal correction (OSC) were employed. The mathematical model successfully classified all the beer samples. Furthermore, a template match protocol identified 31 compounds related to consumers’ preference. This proof of concept paper revealed the potential usage of public metadata bases for comprehensive chemical interpretation of VOC profiling in foodomics.

In the present work, comprehensive two-dimensional gas chromatography (GC × GC) was combined with chemometrics for qualitative analysis of petroleum. Method development included the evaluation of the main method parameters of flow modulation for the analysis of heavy hydrocarbons. The best performing GC × GC configuration was selected based on the elution pattern of hydrocarbons and the duty cycle of the method. The forward and reverse fill/flush configurations were carefully optimized and compared to ascertain the best conditions for the separation of high-boiling point constituents. It was found that the modulator’s flow path had a significant impact on the elution profile, wherein the flow restrictor geometry played an important role in fine tuning the elution temperatures of the analytes. The reverse fill/flush configuration equipped with a 2 m × 0.10 mm-di restrictor provided the best retention conditions for hydrocarbons in the C20+ range. The modulation parameters, namely 6 s modulation period and 400 ms flush time, ensured a duty cycle above 95%. Additionally, a case study was executed to showcase the potential of using chromatographic red-blue-green (RBG) images for multivariate data processing. The proposed method was based on independent components analysis (ICA), which suggests a gradual transformation of sample composition by evaporation in the simulated weathering experiments.

Comprehensive two-dimensional gas chromatography (GC × GC) has impacted the workflow of GC-based methods used in petroleum industry. Application of GC × GC to petroleomic investigations have guided sample preparation to faster, cleaner, and more reliable formats. For instance, solvent consumption has been drastically reduced when using miniaturized devices in routine applications. Furthermore, flow-modulation has enabled cost-effective analysis and powerful separations of gases, fuels, and crude oils. Hyphenation of GC × GC to conventional and high resolution mass spectrometry has enabled the detection and identification of hundreds of compounds in a single analysis. Pixel-based methods enabled efficient handling of big data generated by GC × GC in forensic and geochemical investigations. This review has highlighted an ever-growing demand for modern and powerful analytical methods for oil & gas industry. Here, we described the most recent advances to sample preparation, GC × GC instrumentation, and multivariate data analysis in petroleum industry, including microextractions, high temperature GC × GC, and pixel-based data analysis.

In this study, we report the combination of comprehensive two-dimensional gas chromatography (GC&times, GC) with multivariate pattern recognition through template matching for the assignment of the contribution of Brazilian Ale 02 yeast strain to the aroma profile of beer compared with the traditional Nottingham yeast. Volatile organic compounds (VOC) from two beer samples, which were fermented with these yeast strains were sampled using headspace solid-phase microextraction (HS-SPME). The aroma profiles from both beer samples were obtained using GC&times, GC coupled to a fast scanning quadrupole mass spectrometer. Data processing performed through multiway principal components analysis succeeded in separating both beer samples based on yeast strain. The execution of a simple and reliable procedure succeeded and identified 46 compounds as relevant for sample classification. Furthermore, the bottom-up approach spotted compounds found exclusively in the beer sample fermented with the Brazilian yeast, highlighting the bioaromatization properties introduced to the aroma profile by this yeast strain.

In this article we describe a method using flow-modulated comprehensive two-dimensional gas chromatography with electron capture detector (FM–GC×GC–ECD) for the determination of polychlorinated biphenyls (PCBs) in complex transformer oils bypassing the need for sample preparation. A two-fold improvement in method development was attained. First, the solvation parameter model (SPM) was used to guide column selection. A highly cohesive ionic liquid-based phase (low l system constant), namely 1,12-di(tripropylphosphonium)dodecane bis(trifluoromethanesulfonyl)imide, was used in the primary stage leading to negligible retention of interfering aliphatic hydrocarbons, which are eluted in the first upper quadrant of the chromatogram. The resulting separation space was used to resolve the critical class of compounds, namely, PCBs and polyaromatic hydrocarbons. Second, a unique combination of column geometries and phase ratios enabled highly efficient reverse fill/flush flow modulation using very low pressure for auxiliary gas flow. The proof of concept method described herein exhibited linearities ranging from 0.990 to 0.994, limits of quantitation (LOQ) from 2.23 and 6.85 µg mL−1, precision below 5% relative standard deviation (RSD), and accuracy from 84.2% to 108.9% showcasing the potential of FM–GC×GC for routine analysis.

This work has the purpose of optimizing the analysis of serum samples by comprehensive two-dimensional gas chromatography coupled to mass spectrometry, using derivatization for sample preparation, and aiming at further application in metabolomics studies of bipolar disorder.

In this review, we present the most significant developments since 2013 of sorbent-based headspace extractions that resonated with the latest guidelines of green analytical chemistry. The fundamentals of each technique are discussed, as well as representative applications in the environmental, food analysis, and bioanalysis fields. Such equilibrium-based extractions are currently embodied by solid-phase microextraction (SPME) devices (e.g., fiber-based, TF-SPME, SPME Arrow), needle-based devices (e.g., SPDE, ITEX, NTD), and coated stir bars (e.g., HSSE, SBSE). This review covers most aspects of sample preparation, including theory, fundamental developments, novel sorbent phases (e.g., metal organic frameworks (MOF), carbon-based nanostructured materials, and ionic liquids (IL)), derivatization for gas chromatography (GC), and method development (e.g., design of experiments). In particular, vacuum-assisted extractions have been highlighted due to their recent developments.

In this study, a metabolomic approach was used to investigate the effect of seasonality on the chemical composition and yield of anti-inflammatory active principle, &alpha, humulene, in the essential oil of three genotypes of Varronia curassavica Jacq. (Syn. Cordia verbenaceae). The essential oils were extracted by hydrodistillation and analyzed by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC&times, GC-MS). The GC&times, GC approach a three-fold improvement in qualitative analysis (48 compounds were identified by GC-MS versus 135 by GC&times, GC-MS). The improved resolving power of GC&times, GC resolved important coelutions and enabled the detection of unusual substances in V. curassavica essential oil. The chromatographic data was analyzed by using peak table-based chemometrics, namely, principal component analysis (PCA) and hierarchical cluster analysis (HCA). The metabolic study showed that seasonality has a significant effect on the chemical composition. The &alpha, humulene content was affected by genotype and season. Spring and summer were the best harvest seasons for the yield of the active ingredient, found in higher concentrations in the VC2 genotype. The proposed metabolomic workflow was successfully applied to terpene analysis found in V. curassavica essential oil, and such results have broadened our understanding of the influence of seasonal factors on the specialized metabolism of the species.

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.

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.

In this study, a series of ionic liquids (ILs) are evaluated as stationary phases in comprehensive two-dimensional gas chromatography (GC × GC) for the separation of aliphatic hydrocarbons from kerosene. IL-based stationary phases were carefully designed to evaluate the role of cavity formation/dispersive interaction on the chromatographic retention of nonpolar analytes by GC × GC. The maximum allowable operating temperature (MAOT) of the IL-based columns was compared to that of commercial IL-based columns. Evaluation of the solvation characteristics of GC columns guided the selection of the best performing IL-based stationary phases for the resolution of aliphatic hydrocarbons, namely, trihexyl(tetradecyl)phosphonium tetrachloroferrate ([P66614][FeCl4]) and trihexyl(tetradecyl)phosphonium tris(pentafluoroethyl)trifluorophosphate ([P66614][FAP]) ILs. The best performing [P66614][FeCl4] IL-based column exhibited a MAOT of 320 °C, higher than the commercial SUPELCOWAX 10 (MAOT of 280 °C) and commercial IL-based columns (MAOT up to 300 °C). The structurally tuned [P66614][FeCl4] IL stationary phase exhibited improved separation of aliphatic hydrocarbons by GC × GC compared to the commercial columns examined (e.g., OV-1701, SUPELCOWAX 10, SLB-IL60, SLB-IL100, and SLB-IL111).

Solid-phase microextraction (SPME) using cross-linked polymeric ionic liquid (PIL)-based sorbent coatings was used to extract volatile aroma-related compounds from coffee samples. Several PIL-based coatings were screened alongside a commercial poly(acrylate) (PA) SPME coating. The best performing PIL-based SPME fiber, poly(1-vinyl-3-hexadecylimidazolium bis[(trifluoromethyl)sulfonylimide]) with 50% (w/w) 1,12-di(3-vinylbenzylimidazolium)dodecane dibis[(trifluoromethyl)sulfonyl]imide incorporated cross-linker, was used to isolate the volatile fraction of Arabica coffee. To illustrate the importance of trace analyte isolation, a method for the detection and quantification of coffee adulteration is described. Chromatographic profiles obtained by gas chromatography/mass spectrometry (GC/MS) were used to create the chemometric model. Partial least squares (PLS) regression was employed to correlate the aroma-related chemical fingerprint to the degree of adulteration. The proposed method successfully detected fraud down to 1% (w/w) of adulterant and accurately determined the degree of coffee adulteration (i.e, root mean square error of calibration and prediction of 0.54% and 0.83% (w/w), respectively). Finally, important aroma-related compounds including furans, methoxyphenols, pyrazines, and ketones were identified.

In this paper is reported the use of the chromatographic profiles of volatiles to determine disease markers in plants – in this case, leaves of Eucalyptus globulus contaminated by the necrotroph fungus Teratosphaeria nubilosa . The volatile fraction was isolated by headspace solid phase microextraction (HS-SPME) and analyzed by comprehensive two-dimensional gas chromatography–fast quadrupole mass spectrometry (GC × GC–qMS). For the correlation between the metabolic profile described by the chromatograms and the presence of the infection, unfolded-partial least squares discriminant analysis (U-PLS-DA) with orthogonal signal correction (OSC) were employed. The proposed method was checked to be independent of factors such as the age of the harvested plants. The manipulation of the mathematical model obtained also resulted in graphic representations similar to real chromatograms, which allowed the tentative identification of more than 40 compounds potentially useful as disease biomarkers for this plant/pathogen pair. The proposed methodology can be considered as highly reliable, since the diagnosis is based on the whole chromatographic profile rather than in the detection of a single analyte.

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.