Leveraging the use of ionic liquid capillary columns and GC×GC-MS for fatty acid profiling in human colostrum samples.

Lipids in human colostrum provide the majority of energy intake and essential fatty acids for developing infants. The fatty acid composition of human colostrum is highly variable and influenced by multiple factors. Human colostrum is a complex sample bringing challenges to fatty acid profiling. This work aimed to optimize the use of ionic liquid (IL) columns and flow-modulated comprehensive two-dimensional gas chromatography coupled to mass spectrometry (FM-GC×GC-MS) for fatty acid profiling in human colostrum. Derivatization strategies were optimized and the elution behavior of fatty acid methyl esters (FAME) on various 1D column phases (Solgel-WAX, SLB-IL60i, SLB-IL76i, and SLB-IL111i). Derivatization with sodium methoxide yielded a satisfactory recovery rate (90%) at milder conditions and reduced time. The use of IL60 as the 1D column provided superior separation, good peak shape, and better utilization of elution space. As a proof of concept, the developed method was applied to access the effects of the mode of neonatal delivery (vaginal vs. C-section) on the fatty acid profile of human colostrum samples. The integrated multidimensional gas chromatography strategy improved FAME detection and separation and can be a useful tool for accessing the effects of different factors on the fatty acid profiling of complex samples.

Automated method using direct-immersion solid-phase microextraction and on-fiber derivatization coupled with comprehensive two-dimensional gas chromatography high-resolution mass spectrometry for profiling naphthenic acids in produced water.

Naphthenic acids (NAs) are naturally occurring organic acids in petroleum and are found in waste waters generated during oil production (produced water, PW). Profiling this class of compounds is important due to flow assurance during oil exploration. Compositional analysis of PW is also relevant for waste treatment to reduce negative impacts on the environment. Here, comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry (GC×GC-HRMS) was applied as an ideal platform for qualitative analysis of NAs by combining the high peak capacity of the composite system with automated scripts for group-type identification based on accurate mass measurements and fragmentation patterns. To achieve high-throughput profiling of NAs in PW samples, direct-immersion solid phase microextraction (DI-SPME) was selected for extraction, derivatization and preconcentration. A fully automated DI-SPME method was developed to combine extraction, fiber rinsing and drying, and on-fiber derivatization with N-methyl-N­tert-butyldimethylsilyltrifluoroacetamide (MTBSTFA). Data processing was based on filtering scripts using the Computer Language for Identifying Chemicals (CLIC). The method successfully identified up to 94 NAs comprising carbon numbers between 6 and 18 and hydrogen deficiency values ranging from 0 to -4. The proposed method demonstrated wider extraction coverage compared to traditional liquid-liquid extraction (LLE) - a critical factor for petroleomic investigations. The method developed also enabled quantitative analysis, exhibiting detection limits of 0.5 ng L-1 and relative standard deviation (RSD) at a concentration of NAs of 30 µg L-1 ranging from 4.5 to 25.0%.

Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry.

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3).

Miniaturized systems for gas chromatography: Developments in sample preparation and instrumentation.

An important field of research is the miniaturization of analytical systems for laboratory applications and on-field analysis. In particular, gas chromatography (GC) has benefited from the recent advances in enabling technologies like photolithography, micromachining, hot embossing, and 3D-printing to improve sampling and sample preparation, microcolumn technologies, and detection. In this article, the developments and applications reported since 2015 were reviewed and summarized. Important applications using benchtop instruments, portable GCs, and micro-GCs (µGCs) were showcased to illustrate the current challenges associated with each miniaturized interfaces and systems. For instance, portable instruments need to be energy-efficient and ideally depend on renewable sources for carrier gas generation. Lastly, multidimensional separations were addressed using miniaturized systems to effectively improve the peak capacity of portable systems.

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis.

GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.

Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis.

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

Practical Considerations in Method Development for Gas Chromatography-Based Metabolomic Profiling.

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.

Profiling naphthenic acids in produced water using hollow fiber liquid-phase microextraction combined with gas chromatography coupled to Fourier transform Orbitrap mass spectrometry.

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.

Essential Oils of New Lippia alba Genotypes Analyzed by Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography (GC×GC) and Chemometric Analysis.

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, ß-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.

Improving selective channel occlusion of complex hydrocarbons and fatty acid methyl esters in urea crystals by using an expendable 3D-printed microfluidic device for sample preparation in untargeted petroleomics.

In this study, we describe a proof-of-concept investigation of the potential and limitations of employing channel occlusion for sample preparation in untargeted analysis in petroleomics. A middle petroleum distillate composed of fatty acid methyl esters (FAME) and a complex mixture of linear, branched, and cyclic hydrocarbons were selected as the model samples for this investigation. A microfluidic device was engineered to overcome the limitations of channel occlusion, resulting in a quick and robust method for sample preparation. The 3D-printed device using fused deposition modelling (FDM) allowed the combination of a 13-h multi-step sample handling protocol into a 2-min single-step procedure, which is also automation-friendly. Such developments were also evaluated using the analytical eco-scale to guide the development of a green analytical method. The relative standard deviation decreased 2-fold with method miniaturization. The efficiency of n-alkane removal was extended from tridecane (n-C13) to heptadecane (n-C17), compared to original method (n-C16 to n-C17). The analytical performance of the method was investigated for untargeted analysis. The tool used to probe the intra- and inter-class variance was multi-way principal component analysis (MPCA). MPCA modelling revealed that both methods generated equivalent chemical information, highlighting the benefits of reliable and reproducible sample preparation methods, especially for untargeted analysis. Such awareness is critical to avoid the generation of misleading results in fields that heavily rely on untargeted analysis and fingerprinting, such as petroleomics.

Drinking water nitrosamines in a large metropolitan region in Brazil.

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 (São 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.

Exploring a public database to evaluate consumer preference and aroma profile of lager beers by comprehensive two-dimensional gas chromatography and partial least squares regression discriminant analysis.

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.

Microstructural characterization of nanocellulose foams prepared in the presence of cationic surfactants.

In this work, we explore the architecture of highly porous foams based on cellulose nanofibers (CNFs) prepared by using cationic surfactants (e.g., C12TAB, C14TAB, and C16TAB) as modifying agents. The addition of surfactants to CNF suspensions led the nanoparticles surface to be covered by these molecules, reducing the ζ-potential from -35 to -8 mV, which, together with hydrophobic interactions of adsorbed surfactants, causes CNF flocculation. After freeze-casting and lyophilization, mechanical properties and pores structure of the low-density foams obtained (15.6-30.9 mg cm-3) proved to be strongly dependent on nanofibers agglomeration degree. The primary causes of such dependence could not be observable by scanning electron microscopy but were found to be very significant on X-ray microtomography analysis. Total porosity, pore-size, and wall thickness of the foams were calculated and related to the surfactant chain size. Surface areas from 4.7 to 48.6 m2 g-1, obtained by n-decane adsorption, are reported.

Comprehensive two-dimensional gas chromatography combined to multivariate data analysis for detection of disease-resistant clones of Eucalyptus.

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.

Quantitative analysis of biodiesel in blends of biodiesel and conventional diesel by comprehensive two-dimensional gas chromatography and multivariate curve resolution.

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.

Detection of extraction artifacts in the analysis of honey volatiles using comprehensive two-dimensional gas chromatography.

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 ((1)D-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.

Determination of disease biomarkers in Eucalyptus by comprehensive two-dimensional gas chromatography and multivariate data analysis.

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.

Solid-phase microextraction combined with comprehensive two-dimensional gas chromatography for fatty acid profiling of cell wall phospholipids.

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.

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.