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This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m2) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00216-020-03008-6) contains supplementary material, which is available to authorized users.

Acrylamide (AA) is a product of food heating process that is widely present in cooked foods and known to be toxic to humans. Exposure data has revealed coffee to be one of the sources of this toxicant in adult diets. A great deal of effort has been invested into finding ways of reducing AA formation during coffee processing. However, despite the accumulated knowledge and mitigation strategies applied so far, AA reduction in coffee is still a challenge compared to other heat-processed foods in which the wider raw-material selection and progress in technological processes and/or changes in the recipes are possible at the industrial level. This review presents a critical analysis of the accumulated knowledge on the formation of AA in coffee as well as on the mitigation strategies that have been investigated to date, with a focus on current applicability in industry and little explored topics.

The research was supported by Progetto Ager.Fondazioni in rete per la ricerca agroalimentare. Project acronym Violin. Valorization of Italian olive products through innovative analytical tools; years 2016-2021., The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00., Progetto Ager.Fondazioni in rete per la ricerca agroalimentare

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

This chapter reports on the status of chiral recognition with gas chromatography (enantioselective-GC, Es-GC). The chapter consists of two main parts. The first one illustrates properties and performance of chiral selectors nowadays available for everyday enantiomer separation (α-amino acid derivatives, chelate chiral selectors, and derivatized cyclodextrin) and concisely describes the other recently introduced chiral selectors of a different chemical nature but not yet available for routine enantiomer recognition. The second part deals with possible approach(es) to chiral GC analysis of real-world samples and determination of peculiar parameters (enantiomeric excess or ratio). It includes enantiomer automatic identification by Es-GC or Es-GC-MS, fast-GC, multidimensional GC, and mass spectrometry in chiral recognition, micropreparative Es-GC, and routine methods with total analysis systems. All subjects are illustrated with examples on both standard and real-world samples.

Cocoa smoky off-flavour is generated from an inappropriate artificial drying applied on beans to speeding up the post-harvest process and it can affect the quality of the chocolate. The sensory tests are time-consuming, and at present, a fast analytical method to detect this defect in raw materials is not yet available. This study applies a HS-SPME-MS-enose in combination with chemometrics to obtain diagnostic mass-spectral patterns to detect smoked samples and/or as analytical decision maker. SIMCA models provide the best classification results, compared to PLS-DA, with sensitivities exceeding 90% and a high class specificity range of 89-100% depending on the matrix investigated (beans or liquors). Resulting diagnostic ions were related to phenolic derivatives. The discrimination ability of the method has been confirmed by a quantitative analysis through HS-SPME-GC-MS. HS-SPME-MS-enose turned out to be a fast, cost-effective and objective approach for high throughput analytical screening to discard defective cocoa samples.

The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) ƴ-nonalactone, ƴ-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and ƴ-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples.

Background Comprehensive two-dimensional gas chromatography (GC×GC) combined with time-of-flight (TOF) MS is the most informative analytical approach for chemical characterization of the complex food volatilome. Key analytical features include separation power and resolution enhancement, improved sensitivity, and structured separation patterns from chemically correlated analytes. Objective In this study, we explore the complex extra-virgin olive oil volatilome by combining headspace (HS) solid-phase microextraction (SPME), applied under HS linearity conditions to GC×GC-TOF MS and featuring hard and soft ionization in tandem. Method Multiple analytical dimensions are combined in a single run and evaluated in terms of chemical dimensionality, method absolute and relative sensitivity, identification reliability provided by spectral signatures acquired at 70 and 12 eV, and dynamic and linear range of response provided by soft ionization. Results Method effectiveness is validated on a sample set of oils from Picual olives at different ripening stages. Ripening markers [3,4-diethyl-1,5-hexadiene (RS/SR), 3,4-diethyl-1,5-hexadiene (meso), (5Z)-3-ethyl-1,5-octadiene, (5E)-3-ethyl-1,5-octadiene, (E, Z)-3,7-decadiene and (E, E)-3,7-decadiene, (Z)-2-hexenal, (Z)-3-hexenal and (Z)-3-hexenal, (E)-2-pentenal, (Z)-2-pentenal, 1-pentanol, 1-penten-3-ol, 3-pentanone, and 1-penten-3-one] and quality indexes [(Z)-3-hexenal/nonanal, (Z)-3-hexenal/octane, (E)-2-pentenal/nonanal, and (E)-2-pentenal/octane] are confirmed for their validity in HS linearity conditions. Conclusions For the complex olive oil volatilome, the proposed approach offers concrete advantages for the validation of the informative role of existing analytes while suggesting new potential markers to be studied in larger sample sets. Highlights The accurate fingerprinting of volatiles by HS-SPME operating in HS linearity conditions followed by GC×GC-TOF MS featuring tandem ionization gives the opportunity to improve the quality of analytical data and reliability of results.

Cocoa smoky off-flavor is due to inappropriate post-harvest processing and cannot be removed in the subsequent chocolate-manufacturing steps. To date, no reliable analytical method to detect key-analytes responsible for smoky off-flavor in incoming raw material is available. This study aims to develop an analytical method, suitable for quality control, to detect smoky markers. The cocoa volatilome was first profiled by headspace solid phase microextration combined with comprehensive two-dimensional gas chromatography-mass spectrometry from a set of representative smoky and non-smoky samples; advanced fingerprinting revealed the chemicals responsible for the off-flavor. The results served to develop a 1D-GC method suitable for routine application. Ten identified smoky markers were subjected to accurate quantification, thereby defining operative ranges to accept/reject incoming bean samples. On average, these markers are present in smoky samples at 7 to 125 fold concentrations vs. those in non-smoky beans, ranging from 32.5 ng/g for naphtalene to 721.8 ng/g for phenol.

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA), (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated, and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of &plusmn, 2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

Aroma is a primary hedonic aspect of a good coffee. Coffee aroma quality is generally defined by cup tasting, which however is time-consuming in terms of panel training and alignment and too subjective. It is challenging to define a relationship between chemical profile and aroma sensory impact, but it might provide an objective evaluation of industrial products. This study aimed to define the chemical signature of coffee sensory notes, to develop prediction models based on analytical measurements for use at the control level. In particular, the sensory profile was linked with the chemical composition defined by HS-SPME-GC-MS, using a chemometric-driven approach. The strategy was found to be discriminative and informative, identifying aroma compounds characteristic of the selected sensory notes. The predictive ability in defining the sensory scores of each aroma note was used as a validation tool for the chemical signatures characterized. The most reliable models were those obtained for woody, bitter, and acidic properties, whose selected volatiles reliably represented the sensory note fingerprints. Prediction models could be exploited in quality control, but compromises must be determined if they are to become complementary to panel tasting.

Fragrances and products deriving from essential oils are often formulated or diluted in aqueous media, usually ethanol/water. Gas chromatography (GC) is the technique of choice to analyze volatiles. However, when using columns coated with conventional stationary phases, its application to aqueous samples often requires time-consuming and/or discriminative sample preparation techniques to extract the target analytes from the aqueous medium, so as to avoid its direct injection. In GC with conventional columns, water produces peak asymmetry, poor sensitivity and efficiency, strong adsorption, stationary phase degradation, and, last but not least, it is not easy to detect reliably when present in high amounts. In 2012, Armstrong's group introduced new fully water-compatible ionic-liquid (IL)-based GC capillary columns based on phosphonium and imidazolium derivative cations combined with trifluoromethanesulphonate. These columns were recently made available commercially by Supelco, under the trade name Watercol™. These derivatives maintain IL's unique selectivity and chromatographic properties, and enable water to be used as injection solvent, thus avoiding the sample preparation procedures required by conventional columns. This study reports and critically discusses the results of commercially available water-compatible IL columns for direct analysis of aqueous samples in the fragrance and essential oil fields by GC with thermal conductivity (TCD) and/or flame ionization detectors (FID). The results showed that water-compatible IL-based stationary phases can successfully be adopted for qualitative and quantitative analysis of fragrances and essential oils directly diluted in aqueous solvents. On the other hand, the study also shows that their inertness needs to be further increased and (possibly) the range of operative temperature extended when water is the main solvent of the sample.

The quality control of essential oils (EO) principally aims at revealing the presence of adulterations and at quantifying compounds that are limited by law by evaluating EO chemical compositions, usually in terms of the normalised relative abundance of selected markers, for comparison to reference values reported in pharmacopoeias and/or international norms. Common adulterations of EO consist of the addition of cheaper EO or synthetic materials. This adulteration can be detected by calculating the percent normalised areas of selected markers or the enantiomeric composition of chiral components. The dilution of the EO with vegetable oils is another type of adulteration. This adulteration is quite devious, as it modifies neither the qualitative composition of the resulting EO nor the marker’s normalised percentage abundance, which is no longer diagnostic, and an absolute quantitative analysis is required. This study aims at verifying the application of the two above approaches (i.e., normalised relative abundance and absolute quantitation) to detect EO adulterations, with examples involving selected commercial EO (lavender, bergamot and tea tree) adulterated with synthetic components, EO of different origin and lower economical values and heavy vegetable oils. The results show that absolute quantitation is necessary to highlight adulteration with heavy vegetable oils, providing that a reference quantitative profile is available.

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

The volatile fraction of foods and of plant-origin materials provides functional information on sample-related variables, and gas-phase extractions are ideal approaches for its accurate chemical characterization. However, for gas-phase sampling, the usual procedures adopted to standardize results from solvent extraction methods are not appropriate: headspace (HS) composition depends on the intrinsic physicochemical analyte properties (volatility, polarity, partition coefficient(s)) and matrix effects. Method development, design, and expression of the results are therefore challenging. This review article focuses on volatile vapor-phase quantitation methods (internal standard normalization, standard addition, stable isotope dilution assay, multiple headspace extraction) and their suitability in different applications. Because of the analyte informative role, the different ways of expressing results (normalized chromatographic area, percent normalized chromatographic areas, and absolute concentrations) are discussed and critically evaluated with examples of quality markers in chamomile, process contaminants (furan and 2-methylfuran) in roasted coffee, and key-aroma compounds from high-quality cocoa.

A correct botanical identification and analytical quality control of volatile key-markers responsible for aroma and biological activities is necessary to monitor volatile compounds transferred from a plant to the related herbal tea and human intake to guarantee their safe use. This is mainly true for markers limited by regulations or by a recommended maximum amount of consumption per day. GC–MS is the elective technique to analyze volatiles, provided that for aqueous samples (herbal teas) an appropriate sample preparation procedure, and/or a water-compatible GC stationary phases are applied. Solid Phase Micro Extraction (SPME) on-line coupled to GC–MS in a fully automatic approach is here applied to sample and quantify key markers in plant material (headspace) and in the corresponding herbal tea (direct immersion). In parallel, a new generation of GC columns coated with ionic liquid based stationary phases compatible with aqueous samples (Watercol™) was applied to test direct injection of aqueous samples (DAI-GC-FID). The latter approach fully bypasses sample preparation thus speeding up quality control. This study deals with the quantitation of menthol, α- and β-thujone, estragole, and anethole contained in several plant species commonly used for herbal teas (i.e. peppermint, sage, wormwood, fennel, aniseed) and regulated by International Organizations. The two methods gave comparable results and are characterized by high repeatability, linearity and accuracy, although, as expected, their sensitivity was different because DAI-GC-FID implies injection of the sample as such without analyte concentration as for DI-SPME-GC–MS. For instance, LOD and LOQ of estragole were 0.03 and 0.1 mg L−1 with DI-SPME-GC–MS and 0.1 and 0.8 mg L−1 with DAI-GC-FID. The two methods are fully complementary and their adoption depends on the amount of marker(s) to be quantified.

The capture of volatile patterns from food is a fingerprinting that opens access to a high level of information related to functional variables (origin, processing, shelf-life etc.) and their impact on sample composition and quality. When the focus is on food volatilome, comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) is undoubtedly the most effective technique to obtain a highly representative fingerprinting. A recently patented ion source, featuring variable-energy EI, when operated at low energies (10 eV, 12 eV, 14 eV), claims enhanced intensity of structure-indicating ions while minimizing the inherent loss of sensitivity due to low EI energies. The spectral acquisition is done by multiplexing between two ionization energies and generates tandem data streams in a single run. This study explores the potentials of combined untargeted/targeted (UT) fingerprinting with tandem signals to study the complex volatile metabolome of high quality cocoa. The quality of the spectra at 70 eV is confirmed by similarity match factors above a fixed threshold (950) while spectral differences between hard (70 eV) and soft (12 eV, 14 eV) ionization are computed in terms of spectral similarity and signal-to-noise ratio (SNR). Tandem signals are then processed independently and after fusion in a single stream (summed signal) by the UT fingerprinting work-flow; signal characteristics (SNR, detectable 2D peaks, spectral peak intensities) are then computed and adopted to define the best strategy to discriminate and classify samples. Classification performance, on processed cocoa from four different origins, is validated by cross-comparing results between single ionization channels and fused data streams and considering both targeted and untargeted features. Classification results indicate the potential for superior performances of UT fingerprinting with fused data streams (summed signals), while signal characteristics at low ionization energies not only offer additional elements to better discriminate and/or identify isomeric analytes but also to achieve wider dynamic range of exploration.

Within the pattern of volatiles released by food products (volatilome), potent odorants are bio-active compounds that trigger aroma perception by activating a complex array of odor receptors (ORs) in the regio olfactoria. Their informative role is fundamental to select optimal post-harvest and storage conditions and preserve food sensory quality. This study addresses the volatile metabolome from high-quality hazelnuts (Corylus avellana L.) from the Ordu region (Turkey) and Tonda Romana from Italy, and investigates its evolution throughout the production chain (post-harvest, industrial storage, roasting) to find functional correlations between technological strategies and product quality. The volatile metabolome is analyzed by headspace solid-phase microextration combined with comprehensive two-dimensional gas chromatography and mass spectrometry. Dedicated pattern recognition, based on 2D data (targeted fingerprinting), is used to mine analytical outputs, while principal component analysis (PCA), Fisher ratio, hierarchical clustering, and analysis of variance are used to find decision makers among the most informative chemicals. Low-temperature drying (18–20 °C) has a decisive effect on quality; it correlates negatively with bacteria and mold metabolic activity, nut viability, and lipid oxidation products (2-methyl-1-propanol, 3-methyl-1-butanol, 2-ethyl-1-hexanol, 2-octanol, 1-octen-3-ol, hexanal, octanal and (E)-2-heptanal). Protective atmosphere storage (99% N2–1% O2) effectively limits lipid oxidation for 9–12 months after nut harvest. The combination of optimal drying and storage preserves the aroma potential; after roasting at different shelf-lives, key odorants responsible for malty and buttery (2- and 3-methylbutanal, 2,3-butanedione and 2,3-pentanedione), earthy (methylpyrazine, 2-ethyl-5-methyl pyrazine and 3-ethyl-2,5-dimethyl pyrazine) and caramel-like and musty notes (2,5-dimethyl-4-hydroxy-3(2H)-furanone - furaneol and acetyl pyrrole) show no significant variation.

In each sample run, comprehensive two-dimensional gas chromatography with dual secondary columns and detectors (GC × 2GC) provides complementary information in two chromatograms generated by its two detectors. For example, a flame ionization detector (FID) produces data that is especially effective for quantification and a mass spectrometer (MS) produces data that is especially useful for chemical-structure elucidation and compound identification. The greater information capacity of two detectors is most useful for difficult analyses, such as metabolomics, but using the joint information offered by the two complex two-dimensional chromatograms requires data fusion. In the case that the second columns are equivalent but flow conditions vary (e.g., related to the operative pressure of their different detectors), data fusion can be accomplished by aligning the chromatographic data and/or chromatographic features such as peaks and retention-time windows. Chromatographic alignment requires a mapping from the retention times of one chromatogram to the retention times of the other chromatogram. This paper considers general issues and experimental performance for global two-dimensional mapping functions to align pairs of GC × 2GC chromatograms. Experimental results for GC × 2GC with FID and MS for metabolomic analyses of human urine samples suggest that low-degree polynomial mapping functions out-perform affine transformation (as measured by root-mean-square residuals for matched peaks) and achieve performance near a lower-bound benchmark of inherent variability. Third-degree polynomials slightly out-performed second-degree polynomials in these results, but second-degree polynomials performed nearly as well and may be preferred for parametric and computational simplicity as well as robustness.

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with mass spectrometry (MS) is one of the most powerful analytical techniques now available for detailed analysis, identification and quantitation of medium-to-high complexity mixtures. However, the number of application methods that combine fingerprinting and/or profiling with quantitation of informative volatile analytes (targets) is still limited. Possible reasons are related to the huge amount of information to handle and to the availability of reference standards for calibration. Although quantitative analysis by GC×GC is complex it has important advantages: (i) to assess data/results over an extended time frame, varied instrumentation and different laboratories; (ii) to interpret the biological role of (potential) biomarkers; (iii) to evaluate the impact of potent odourants; and/or (iv) to define product quality, e.g. relative to a reference standard. In this study, a GC×2GC-MS/FID platform consisting of one primary column (1D) coupled to two parallel secondary columns (2D) having an identical inner diameter, stationary phase chemistry and film thickness, which, in turn, are connected to two detectors: a fast quadrupole MS and a FID, was adopted for quantitative profiling of essential oils (EOs). Two medium complexity EOs (i.e. Mentha and Lavandula species) that pose different quantitation challenges were taken as examples and a selection of quality markers subjected to an extensive method performance evaluation (e.g. method validation). Experimental results confirmed the platform's reliability in terms of: linearity, precision and quantitation accuracy. In addition, predicted FID Relative Response Factors (RRFs) based on combustion enthalpies were adopted to extend quantitation to all identified analytes. The experimental data demonstrated the accuracy of the predicted RRFs, supporting their adoption in quantitation of EO markers. This approach is of particular interest for those applications where reference standards are not (easily) available and/or regulated. Copyright © 2015 John Wiley & Sons, Ltd.

Herbs and spices are used worldwide as food flavoring, thus determination of their identity, origin, and quality is mandatory for safe human consumption. An analysis strategy based on separative (HS-SPME-GC-MS) and non-separative (HS-SPME-MS) approaches is proposed for the volatile fraction of herbs and spices, for quality control and to quantify the aromatic markers with a single analysis directly on the plant material as such. Eight-to-ten lots of each of the following herbs/spices were considered: cloves (Syzygium aromaticum (L.) Merr. & Perry), American peppertree (Schinus molle L.), black pepper and white pepper (Piper nigrum L.), rosemary (Rosmarinus officinalis L.), sage (Salvia officinalis L.) and thyme (Thymus vulgaris L.). Homogeneity, origin, and chemotypes of the investigated lots of each herb/spice were defined by fingerprinting, through statistical elaboration with principal component analysis (PCA). Characterizing aromatic markers were directly quantified on the solid matrix through multiple headspace extraction-HS-SPME (MHS-SPME). Reliable results were obtained with both separative and non-separative methods (where the latter were applicable); the two were in full agreement, RSD% ranging from 1.8 to 7.7% for eugenol in cloves, 2.2-18.4% for carvacrol+thymol in thyme, and 3.1-16.8% for thujones in sage.

Despite clinical findings suggesting that the form (liquid versus solid) of the sugars may significantly affect the development of metabolic diseases, no experimental data are available on the impact of their formulations on gut microbiota, integrity and hepatic outcomes. In the present sudy, C57Bl/6j mice were fed a standard diet plus water (SD), a standard diet plus 60% fructose syrup (L-Fr) or a 60% fructose solid diet plus water (S-Fr) for 12 weeks. Gut microbiota was characterized through 16S rRNA phylogenetic profiling and shotgun sequencing of microbial genes in ileum content and related volatilome profiling. Fructose feeding led to alterations of the gut microbiota depending on the fructose formulation, with increased colonization by Clostridium, Oscillospira and Clostridiales phyla in the S-Fr group and Bacteroides, Lactobacillus, Lachnospiraceae and Dorea in the L-Fr. S-Fr evoked the highest accumulation of advanced glycation end products and barrier injury in the ileum intestinal mucosa. These effects were associated to a stronger activation of the lipopolysaccharide-dependent proinflammatory TLR4/NLRP3 inflammasome pathway in the liver of S-Fr mice than of L-Fr mice. In contrast, L-Fr intake induced higher levels of hepatosteatosis and markers of fibrosis than S-Fr. Fructose-induced ex novo lipogenesis with production of SCFA and MCFA was confirmed by metagenomic analysis. These results suggest that consumption of fructose under different forms, liquid or solid, may differently affect gut microbiota, thus leading to impairment in intestinal mucosa integrity and liver homeostasis.

Recent technological advances in dynamic headspace sampling (D-HS) and the possibility to automate this sampling method have lead to a marked improvement in its the performance, a strong renewal of interest in it, and have extended its fields of application. The introduction of in-parallel and in-series automatic multi-sampling and of new trapping materials, plus the possibility to design an effective sampling process by correctly applying the breakthrough volume theory, have make profiling more representative, and have enhanced selectivity, and flexibility, also offering the possibility of fractionated enrichment in particular for high-volatility compounds. This study deals with fractionated D-HS ability to produce a sample representative of the volatile fraction of solid or liquid matrices. Experiments were carried out on a model equimolar (0.5 mM) EtOH/water solution, comprising 16 compounds with different polarities and volatilities, structures ranging from C5 to C15 and vapor pressures from 4.15 kPa (2,3-pentandione) to 0.004 kPa (t-β-caryophyllene), and on an Arabica roasted coffee powder. Three trapping materials were considered: Tenax TA™ (TX), Polydimethylsiloxane foam (PDMS), and a three-carbon cartridge Carbopack B/Carbopack C/Carbosieve S-III™ (CBS). The influence of several parameters on the design of successful fractionated D-HS sampling. Including the physical and chemical characteristics of analytes and matrix, trapping material, analyte breakthrough, purge gas volumes, and sampling temperature, were investigated. The results show that, by appropriately choosing sampling conditions, fractionated D-HS sampling, based on component volatility, can produce a fast and representative profile of the matrix volatile fraction, with total recoveries comparable to those obtained by full evaporation D-HS for liquid samples, and very high concentration factors for solid samples.

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

This study is part of a wider project aiming to correlate the chemical composition of the coffee volatile fraction to its sensory properties with the end-goal of developing an instrumental analysis approach complementary to human sensory profiling. The proposed investigation strategy compares the chemical information concerning coffee aroma and flavor obtained with HS-SPME of the ground coffee and in-solution SBSE/SPME sampling combined with GC-MS to evaluate their compatibility with the cupping evaluation for quality control purposes. Roasted coffee samples with specific sensory properties were analyzed. The chemical results obtained by the three samplings were compared through multivariate analysis, and related to the samples' sensory attributes. Despite the differences between the three sampling approaches, data processing showed that the three methods provide the same kind of chemical information useful for sample discrimination, and that they could be used interchangeably to sample the coffee aroma and flavor.

Tea prepared by infusion of dried leaves of Camellia sinensis (L.) Kuntze, is the second world's most popular beverage, after water. Its consumption is associated with its chemical composition: it influences its sensory and nutritional quality addressing consumer preferences, and potential health benefits. This study aims to obtain an informative chemical signature of the volatile fraction of black tea samples from Ceylon by applying the principles of sensomics. In particular, several high concentration capacity (HCC) sample preparation techniques were tested in combination with GC×GC-MS to investigate chemical signatures of black tea volatiles. This platform, using headspace solid phase microextraction (HS-SPME) with multicomponent fiber as sampling technique, recovers 95% of the key-odorants in a fully automated work-flow. A group 123 components, including key-odorants, technological and botanical tracers, were mapped. The resulting 2D fingerprints were interpreted by pattern recognition tools (i.e. template matching fingerprinting and scripting) providing highly informative chemical signatures for quality assessment.

In the fields of essential oils and fragrances, samples often consist of mixtures of compounds with similar structural and physical characteristics (e.g. mono- and sesquiterpenoids), whose correct identification closely depends on the synergic combination of chromatographic and mass spectral data. This sample complexity means that new GC stationary phases with different selectivities are continually being investigated. Ionic liquids (ILs) are of great interest as GC stationary phases in this field because of their selectivity (significantly different than that of currently phases) and their high temperature stability. A first generation of IL GC columns was found to be competitive when applied to these field, in terms of selectivity and efficiency, compared to conventional columns (polydimethylsiloxane, (e.g. OV-1), methyl-polysiloxane 5%-phenyl (e.g. SE-52), 7%-cyanopropyl, 7%-phenyl polysiloxane (e.g. OV-1701), and polyethylen glycol (e.g. PEG–20 M). However, these columns showed significant activity towards polar or active analytes, which primarily affected their quantitative performance. A new generation of highly-inactive columns coated with three of the most widely-used ionic liquid GC stationary phases has recently been introduced; these phases are SLB-IL60i (1,12-di(tripropylphosphonium) dodecane bis(trifluoromethylsulfonyl) imide [NTf2], SLB-IL76i (tri-(tripropylphosphonium-hexanamido)-triethylamine [NTf2]), and SLB-IL111i (1,5-di (2,3-dimethyllimidazolium) pentane [NTf2]). This study carefully tested the new inert IL columns, in view of their routine application in the fragrance and essential oil fields. They were found to have unusually high selectivity, comparable to that of first-generation IL columns, while their inertness and efficiency were competitive with those of currently-used conventional columns. The IL column performance of first and second generations was compared, through the quali-quantitative analysis of components in a group of different complexity samples; these included the Grob test, a standard mixture of “suspected” skin allergens, and the essential oils of chamomile and sandalwood.

a b s t r a c t This study investigates the potential of a parallel dual secondary column-dual detection two-dimensional comprehensive GC platform (GC × 2GC-MS/FID) for metabolic profiling and fingerprinting of mouse urine. Samples were obtained from a murine model that mimics a typical unhealthy Western diet featu- ring both high fat and sugar (HFHS) intake, which induces obesity, dyslipidemia, and insulin resistance. Urines collected at different steps of the study were used to obtain pivotal and comparative data on the presence and relative distributions of early markers of metabolic disease. The data elaboration and interpretation work-flow includes an advanced untargeted fingerprinting approach, with peak-region features to locate relevant features to be quantified by external standard calibration. The reliability of untargeted fingerprinting is confirmed by quantitative results on selected relevant features that showed percentages of variations consistent with those observed by comparing raw data quantitative descriptors (2D peak-region volumes and percent of response). Analytes that were up-regulated with % of variation ranging from 30 to 1000, included pyruvic acid, glycerol, fructose, galactose, glucose, lactic acid, mannitol and valine. Down-regulation was evidenced for malonic acid, succinic acid, alanine, glycine, and creati- nine. Advanced fingerprinting also is demonstrated for effectively evaluating individual variations during experiments, thus representing a promising tool for personalized intervention studies. In this context, it is interesting to observe that informative features that were not discriminant for the entire population may be relevant for individuals. © 2014 Elsevier B.V. All rights reserved.

Comprehensive two-dimensional gas chromatography (GC×GC) coupled with Mass Spectrometry (MS) is one of today's most powerful analytical platforms for detailed analysis of medium-to-high complexity samples. The column set usually consists of a long, conventional-inner-diameter first dimension ((1)D) (typically 15-30m long, 0.32-0.25mm dc), and a short, narrow-bore second dimension ((2)D) column (typically 0.5-2m, 0.1mm dc) where separation is run in a few seconds. However, when thermal modulation is used, since the columns of a set are coupled in series, a flow mismatch occurs between the two dimensions, making it impossible to operate simultaneously at optimized flow conditions. Further, short narrow-bore capillaries can easily be overloaded, because of their lower loadability, limiting the effectiveness of (2)D separation. In this study, improved gas linear velocities in both chromatographic dimensions were achieved by coupling the (1)D column with two parallel (2)D columns, having identical inner diameter, stationary phase chemistry, and film thickness. In turn, these were connected to two detectors: a fast quadrupole Mass Spectrometer (MS) and a Flame Ionization Detector (FID). Different configurations were tested and performances compared to a conventional set-up; experimental results on two model mixtures (n-alkanes and fourteen medium-to-high polarity volatiles of interest in the flavor and fragrance field) and on the essential oil of Artemisia umbelliformis Lam., show the system provides consistent results, in terms of analyte identification (reliability of spectra and MS matching) and quantitation, also affording an internal cross-validation of quantitation accuracy.

The possibility to transfer methods from thermal to differential-flow modulated comprehensive two-dimensional gas chromatographic (GC×GC) platforms opens interesting perspectives for routine analysis of complex samples. Flow modulated platforms avoid the use of cryogenics, thereby simplifying laboratory operations and analyst supervision during intensive analytical sessions. This study evaluates the feasibility of transferring a fingerprinting method capable of classifying and discriminating cocoa samples based on the volatiles fraction composition according to their origin and processing steps. Previously developed principles of GC×GC method translation are applied to an original fingerprinting method, developed for a loop-type thermal modulated GC×GC-MS system, to engineer a method for a reverse-injection differential flow modulated platform (GC×2GC-MS/FID) with a dual-parallel secondary column and dual detection. Effective method translation preserves analytes elution order, 1D resolution, and 2D pattern coherence. The experimental results confirm the feasibility of translating fingerprinting method conditions while preserving the informative power of 2D peak patterns for sample classification and discrimination. Correct translation enables effective transfer of metadata (e.g., compound names and MS fragmentation patterns) by automatic template transformation and matching from the original/reference method to its translated counterpart. Although the adoption of a narrow bore (i.e. 0.1mm dc) column in the first-dimension enabled operation under close-to-optimal conditions with the differential-flow modulation platform, due to the dual-parallel columns in the second-dimension, it resulted in lower overall method sensitivity. Nevertheless, fingerprinting accuracy was preserved and most of the key-aroma compounds and technological markers were effectively mapped, thus limiting the loss of fingerprinting information.

This article deals with enantioselective gas chromatography (Es-GC) utilizing cyclodextrin derivatives as chiral stationary phases (CSPs) for chiral recognition in the flavour and fragrance field. It consists of two main parts. The first one concerns cyclodextrins and their use in Es-GC, and it deals with their introduction and evolution as CSPs in GC, together with some theoretical aspects involved with enantiomeric separation and their consequent influence on routine chiral recognition. The second part reports on the strategy of chiral recognition in routine analysis with cyclodextrin derivatives as CSP, illustrated with examples from the authors’ everyday experiences. This part describes how to identify enantiomers or to measure their excess or ratio determination in complex mixtures; in particular, it discusses the opportunities offered by multidimensional and fast GC techniques, the role of mass spectrometry, and the application of total analysis systems to chiral recognition.

Scope The human volatilome has gained high interest for the discovery of potential biomarkers of diseases. However, knowledge about the diet as a crucial factor affecting the volatilome is scarce. Therefore, the search for disease biomarkers, as well as the potential use of volatiles as dietary markers is so far limited. The aim of this study is to investigate the association of the diet with the urinary volatilome with the special task to find potential markers of coffee consumption in 24 h urine samples from the Karlsruhe Metabolomics and Nutrition (KarMeN) study. Methods and results Acidified urine samples are analyzed using an approach combining headspace solid phase microextraction (HS-SPME) sampling with untargeted GC×GC-MS. Overall, 138 reliably occurring volatiles are detected. To account for the unequally concentrated urine samples, results of six different commonly used normalization methods are compared. Statistical analysis evidences six potential markers of coffee consumption, the most promising being 3,4-dimethyl-2,5-furandione. A correlation analysis between the 24 h dietary recall data and the urinary volatilome reveals further promising associations. Conclusion The human urinary volatilome is highly affected by the diet, enabling access to a high level of information about potential diet-related biomarkers. Therefore, it is a very promising source for further investigations on dietary markers.

This article deals with enantioselective gas chromatography (Es-GC) utilizing cyclodextrin derivatives as chiral stationary phases (CSPs) for chiral recognition in the flavour and fragrance field. It consists of two main parts. The first one concerns cyclodextrins and their use in Es-GC, and it deals with their introduction and evolution as CSPs in GC, together with some theoretical aspects involved with enantiomeric separation and their consequent influence on routine chiral recognition. The second part reports on the strategy of chiral recognition in routine analysis with cyclodextrin derivatives as CSP, illustrated with examples from the authors’ everyday experiences. This part describes how to identify enantiomers or to measure their excess or ratio determination in complex mixtures; in particular, it discusses the opportunities offered by multidimensional and fast GC techniques, the role of mass spectrometry, and the application of total analysis systems to chiral recognition.

This study aimed to characterise and discriminate 44 authenticated commercial samples of citrus essential oils (EO) from seven species (bergamot, lemon, bigarade, orange, mandarin, grapefruit, lime) by analysing the non-volatile oxygenated heterocyclic compounds (OHC) by UHPLC/TOF-HRMS, multivariate data analysis (PCA, PLS-DA) and metabolomic strategies; the OHC fraction includes coumarins, furocoumarins, and polymethoxylated flavonoids. Two different approaches were adopted: (i) targeted profiling based on quantifying 18 furocoumarins and coumarins, some of which are regulated by law, and (ii) targeted fingerprinting based on 140 OHCs reported in citrus essential oils, from which 38 discriminant markers were defined. This approach correctly discriminated the Citrus species; its "sensitivity" to relatively low adulteration rate (10%) was highly satisfactory. The proposed method is complementary to that of analysing the citrus EO volatile part by GC techniques.