Composition and nutritional values of fatty acids in marine organisms by one-step microwave-assisted extraction/derivatization and comprehensive two-dimensional gas chromatography -flame ionization detector.

This work reports the characterization of the lipidic fraction of seven species of marine organisms gathered along the shoreline of the Po Delta Park of Emilia-Romagna Region (Italy) and of the north Adriatic Sea. Two species of oysters (Crassostrea gigas and Ostrea edulis), two species of clams (Chamelea gallina and Ruditapes philippinarum), one species of mussel (Mytilus galloprovincialis), one species of macroalgae (Ulva rigida), and one species of spiny dogfish (Squalus acanthias) were analyzed to characterize their fatty acids profile and related nutritional value. The lipid fraction was simultaneously extracted and transesterified into fatty acid methyl esters (FAMEs) by using a recently developed one-step microwave-assisted extraction/derivatization (MAED) method. The obtained FAMEs extract was analyzed by a rapid comprehensive multidimensional gas chromatography (GC × GC) method (30 min). The system was equipped with a reverse set of columns (polar × non-polar) connected through a reversed fill/flush flow modulator. The GC × GC system was coupled with a flame-ionization detector (FID) for both qualitative and quantitative purposes. The MAED- GC × GC-FID methodology was suitable in the context of samples containing high percentages of omega-3 PUFA. A total of 82 FAMEs were tentatively identified using standards, literature data, and the two-dimensional plot location. FAME profiles obtained with the proposed approach were comparable with reference methods (AOCS Ce 2b-11), showing no significant differences. Moreover, to determine the food nutritional value of the samples investigated, the most common nutritional indices (index of atherogenicity, index thrombogenicity, hypocholesterolemic/hypercholesterolemic ratio, health-promoting index, unsaturation index, and the fish lipid quality index) were calculated from FAME profiles. Among the samples investigated, Squalus acanthias presented the best nutritional score, while Ruditapes philippinarum had the worst score in 3 out of 6 indices.

Artificial Intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment.

Effective investigation of food volatilome by comprehensive two-dimensional gas chromatography with parallel detection by mass spectrometry and flame ionization detector (GC×GC-MS/FID) gives access to valuable information related to industrial quality. However, without accurate quantitative data, results transferability over time and across laboratories is prevented. The study applies quantitative volatilomics by multiple headspace solid phase microextraction (MHS-SPME) to a large selection of hazelnut samples (Corylus avellana L. n = 207) representing the top-quality selection of interest for the confectionery industry. By untargeted and targeted fingerprinting, performant classification models validate the role of chemical patterns strongly correlated to quality parameters (i.e., botanical/geographical origin, post-harvest practices, storage time and conditions). By quantification of marker analytes, Artificial Intelligence (AI) tools are derived: the augmented smelling based on sensomics with blueprint related to key-aroma compounds and spoilage odorant; decision-makers for rancidity level and storage quality; origin tracers. By reliable quantification AI can be applied with confidence and could be the driver for industrial strategies.

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome.

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Delineating the extra-virgin olive oil aroma blueprint by multiple headspace solid phase microextraction and differential-flow modulated comprehensive two-dimensional gas chromatography.

Comprehensive two-dimensional gas chromatography with parallel mass spectrometry and flame ionization detection (GC × GC-MS/FID) enables effective chromatographic fingerprinting of complex samples by comprehensively mapping untargeted and targeted components. Moreover, the complementary characteristics of MS and FID open the possibility of performing multi-target quantitative profiling with great accuracy. If this synergy is applied to the complex volatile fraction of food, sample preparation is crucial and requires appropriate methodologies capable of providing true quantitative results. In this study, untargeted/targeted (UT) fingerprinting of extra-virgin olive oil volatile fractions is combined with accurate quantitative profiling by multiple headspace solid phase microextraction (MHS-SPME). External calibration on fifteen pre-selected analytes and FID predicted relative response factors (RRFs) enable the accurate quantification of forty-two analytes in total, including key-aroma compounds, potent odorants, and olive oil geographical markers. Results confirm good performances of comprehensive UT fingerprinting in developing classification models for geographical origin discrimination, while quantification by MHS-SPME provides accurate results and guarantees data referability and results transferability over years. Moreover, by this approach the extent of internal standardization procedure inaccuracy, largely adopted in food volatiles profiling, is measured. Internal standardization yielded an average relative error of 208 % for the fifteen calibrated compounds, with an overestimation of + 538% for (E)-2-hexenal, the most abundant yet informative volatile of olive oil, and a -89% and -80% for (E)-2-octenal and (E)-2-nonenal respectively, analytes with a lower HS distribution constant. Compared to existing methods based on 1D-GC, the current procedure offers better separation power and chromatographic resolution that greatly improve method specificity and selectivity and results in lower LODs and LOQs, high calibration performances (i.e., R2 and residual distribution), and wider linear range of responses. As an artificial intelligence smelling machine, the MHS-SPME-GC × GC-MS/FID method is here adopted to delineate extra-virgin olive oil aroma blueprints; an objective tool with great flexibility and reliability that can improve the quality and information power of each analytical run.

Exploring the Extra-Virgin Olive Oil Volatilome by Adding Extra Dimensions to Comprehensive Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry Featuring Tandem Ionization: Validation of Ripening Markers in Headspace Linearity Conditions.

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.

Chemical fingerprinting strategies based on comprehensive two-dimensional gas chromatography combined with gas chromatography-olfactometry to capture the unique signature of Piemonte peppermint essential oil (Mentha x piperita var Italo-Mitcham).

Accurate, reliable, and informative mapping of untargeted and targeted components across many samples is here performed by combining off-line GC-Olfactometry (GC-O) and comprehensive two-dimensional gas chromatography (GC×GC) coupled to time-of-flight mass spectrometry with variable ionization energy (TOF MS featuring Tandem Ionization™). In particular, untargeted and targeted (UT) features patterns are processed by chromatographic fingerprinting, giving differential priority to potent odorants' retention-times regions. Distinguishing peppermint essential oil (EO) from Piedmont (Italy - Mentha × piperita L. var. Italo-Mitcham - Menta di Pancalieri EO), with its unique sensory fingerprint (i.e., freshness and long-lasting sweetness), from high-quality peppermint EOs produced in other areas poses a great challenge. Chromatographic UT fingerprinting provided a great chemical dimensionality by mapping more than 350 peak-regions at 70 eV and 135 at 12 eV. From them, 95 components were identified and responses compared to available literature. Then, potent odorants, detected by GC-O using the aroma extraction dilution analysis (AEDA), were tracked over the chromatographic space and tentatively identified. With the highest flavor dilution (FD), 1,8-cineole (eucalyptus, fresh, camphoraceous); menthone (minty, herbaceous); and menthofuran (minty, musty, petroleum-like) were highlighted. Responsible for creamy and coumarinic notes were the diasteroisomers of (3,6)-dimethyl-4,5,6,7-tetrahydrobenzo[b]-furan-2(3H)-one (i.e., menthofurolactones), detected in higher relative abundance in Pancalieri EOs. By prioritizing the investigation of volatiles on higher LogFD retention regions, including 131 untargeted/targeted features, Pancalieri EOs were separately clustered from United States samples. Besides pre-targeted analytes, additional untargeted features were post-processed for identification within marker chemicals. Myrtenyl methyl ether, ethyl 3-methyl butanoate, propyl-2-methylbutanoate, and (E)-2-hexenal were putatively identified. Of the "unknown - knowns" with diagnostic roles, all metadata were collected including low energy spectra at 12 eV, which were found to be highly complementary to 70 eV spectra.

Comprehensive two-dimensional gas chromatography as a boosting technology in food-omic investigations.

This review focuses on the role that comprehensive two-dimensional gas chromatography can play within the investigation workflows of food-omics and related disciplines and subdisciplines, including food metabolomics, nutrimetabolomics, sensomics, and food safety. After a short introductory survey, discussing the intriguing context of system biology and integrationist approaches of investigation, the concepts of analytical dimensions and the key characteristics of comprehensive two-dimensional gas chromatography are introduced. Through a selection of relevant examples, the boosting role of comprehensive two-dimensional gas chromatography within food-omics is described, providing to the reader evidence of how comprehensive multidimensional separations based platforms have introduced new concepts and tools in the analytical measurement of complex biological samples.

An effective chromatographic fingerprinting workflow based on comprehensive two-dimensional gas chromatography - Mass spectrometry to establish volatiles patterns discriminative of spoiled hazelnuts (Corylus avellana L.).

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) Æ´-nonalactone, Æ´-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and Æ´-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples.

Exploring extra dimensions to capture saliva metabolite fingerprints from metabolically healthy and unhealthy obese patients by comprehensive two-dimensional gas chromatography featuring Tandem Ionization mass spectrometry.

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.

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography.

Data processing and evaluation are critical steps of comprehensive two-dimensional gas chromatography (GCxGC), particularly when coupled to mass spectrometry. The rich information encrypted in the data may be highly valuable but difficult to access efficiently. Data density and complexity can lead to long elaboration times and require laborious, analyst-dependent procedures. Effective yet accessible data processing tools, therefore, are key to enabling the spread and acceptance of this advanced multidimensional technique in laboratories for daily use. The data analysis protocol presented in this work uses chromatographic fingerprinting and template matching to achieve the goal of highly automated deconstruction of complex two-dimensional chromatograms into individual chemical features for advanced recognition of informative patterns within individual chromatograms and across sets of chromatograms. The protocol delivers high consistency and reliability with little intervention. At the same time, analyst supervision is possible in a variety of settings and constraint functions that can be customized to provide flexibility and capacity to adapt to different needs and goals. Template matching is shown here to be a powerful approach to explore extra-virgin olive oil volatilome. Cross-alignment of peaks is performed not only for known targets, but also for untargeted compounds, which significantly increases the characterization power for a wide range of applications. Examples are presented to evidence the performance for the classification and comparison of chromatographic patterns from sample sets analyzed under similar conditions.

A step forward in the equivalence between thermal and differential-flow modulated comprehensive two-dimensional gas chromatography methods.

Comprehensive two-dimensional gas chromatography (GC×GC) based on flow-modulation (FM) is gaining increasing attention as an alternative to thermal modulation (TM), the recognized GC×GC benchmark, thanks to its lower operational cost and rugged performance. An accessible, rational procedure to perform method translation between the two platforms would be highly valuable to facilitate compatibility and consequently extend the flexibility and applicability of GC×GC. To enable an effective transfer, the methodology needs to ensure preservation of the elution pattern, separation power, and sensitivity. Here, a loop-type thermal modulation system with dual detection (TM-GC×GC-MS/FID) used for the targeted analysis of allergens in fragrances is selected as reference method. Initially, six different columns configurations are systematically evaluated for the flow-modulated counterpart. The set-up providing the most consistent chromatographic separation (20 m x 0.18 mm dc x 0.18 µm df + 1.8 m x 0.18 mm dc x 0.18 µm df) is further evaluated to assess its overall performance in terms of sensitivity, linearity, accuracy, and pattern reliability. The experimental results convincingly show that the method translation procedure is effective and allows successful transfer of the target template metadata. Additionally, the FM-GC×GC-MS/FID system is suitable for challenging applications such as the quantitative profiling of complex fragrance materials.

Climate and Processing Effects on Tea (Camellia sinensis L. Kuntze) Metabolome: Accurate Profiling and Fingerprinting by Comprehensive Two-Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry.

This study applied an untargeted-targeted (UT) fingerprinting approach, based on comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS), to assess the effects of rainfall and temperature (both seasonal and elevational) on the tea metabolome. By this strategy, the same compound found in multiple samples need only to be identified once, since chromatograms and mass spectral features are aligned in the data analysis process. Primary and specialized metabolites of leaves from two Chinese provinces, Yunnan (pu'erh) and Fujian (oolong), and a farm in South Carolina (USA, black tea) were studied. UT fingerprinting provided insight into plant metabolism activation/inhibition, taste and trigeminal sensations, and antioxidant properties, not easily attained by other analytical approaches. For example, pu'erh and oolong contained higher relative amounts of amino acids, organic acids, and sugars. Conversely, black tea contained less of all targeted compounds except fructose and glucose, which were more similar to oolong tea. Findings revealed compounds statistically different between spring (pre-monsoon) and summer (monsoon) in pu'erh and oolong teas as well as compounds that exhibited the greatest variability due to seasonal and elevational differences. The UT fingerprinting approach offered unique insights into how differences in growing conditions and commercial processing affect the nutritional benefits and sensory characteristics of tea beverages.

Adding extra-dimensions to hazelnuts primary metabolome fingerprinting by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry featuring tandem ionization: Insights on the aroma potential.

The information potential of comprehensive two-dimensional gas chromatography combined with time of flight mass spectrometry (GC × GC-TOFMS) featuring tandem hard (70 eV) and soft (12 eV) electron ionization is here applied to accurately delineate high-quality hazelnuts (Corylus avellana L.) primary metabolome fingerprints. The information provided by tandem signals for untargeted and targeted 2D-peaks is examined and exploited with pattern recognition based on template matching algorithms. EI-MS fragmentation pattern similarity, base-peak m/z values at the two examined energies (i.e., 12 and 70 eV) and response relative sensitivity are adopted to evaluate the complementary nature of signals. As challenging bench test, the hazelnut primary metabolome has a large chemical dimensionality that includes various chemical classes such as mono- and disaccharides, amino acids, low-molecular weight acids, and amines, further complicated by oximation/silylation to obtain volatile derivatives. Tandem ionization provides notable benefits including larger relative ratio of structural informing ions due to limited fragmentation at low energies (12 eV), meaningful spectral dissimilarity between 12 and 70 eV (direct match factor values range 222-783) and, for several analytes, enhanced relative sensitivity at lower energies. The complementary information provided by tandem ionization is exploited by untargeted/targeted (UT) fingerprinting on samples from different cultivars and geographical origins. The responses of 138 UT-peak-regions are explored to delineate informative patterns by univariate and multivariate statistics, providing insights on correlations between known precursors and (key)-aroma compounds and potent odorants. Strong positive correlations between non-volatile precursors and odorants are highlighted with some interesting linear trends for: 3-methylbutanal with isoleucine (R2 0.9284); 2,3-butanedione/2,3-pentanedione with monosaccharides (fructose/glucose derivatives) (R2 0.8543 and 0.8860); 2,5-dimethylpyrazine with alanine (R2 0.8822); and pyrroles (1H-pyrrole, 3-methyl-1H-pyrrole, and 1H-pyrrole-2-carboxaldehyde) with ornithine and alanine derivatives (R2 0.8604). The analytical work-flow provides a solid foundation for a new strategy for hazelnuts quality assessment because aroma potential could be derived from precursors' chemical fingerprints.

Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome.

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena.

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies.

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.

Comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry featuring tandem ionization: Challenges and opportunities for accurate fingerprinting studies.

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.

Development and validation of a pneumatic model for the reversed-flow differential flow modulator for comprehensive two-dimensional gas chromatography.

The development of the reversed fill/flush modulator represents a significant advancement in flow-modulated, comprehensive two-dimensional gas chromatography (GC × GC). Compared to the forward flush/fill modulator, the reversed-flow modulator is less susceptible to baseline anomalies and peak tailing as a result of modulator channel overfilling or insufficient purging of high concentration analytes. Flow reversal requires the addition of a bleed capillary not present in the forward-flow modulator. Selecting the appropriate restriction of the bleed capillary is critical. If the bleed capillary is too restrictive, eluate from the first-dimension column can split between the modulator channel and second-dimension column, which also results in baseline artifacts. To gain a better understanding of the reversed-flow modulator, a comprehensive pneumatic model was developed. The model was validated by comparing calculated and measured hold-up times. The errors in calculated hold-up times were less than 1% of the measured values. The model can be used to predict first-dimension eluate splitting and determine the optimal bleed capillary dimensions to prevent its occurrence. Calculation of the modulator hold-up time can be used to determine the maximum collection time to ensure comprehensive analysis and optimal flush times for partial fill operation.

Combined untargeted and targeted fingerprinting by comprehensive two-dimensional gas chromatography: revealing fructose-induced changes in mice urinary metabolic signatures.

This study exploits the information potential of comprehensive two-dimensional gas chromatography configured with a parallel dual secondary column-dual detection by mass spectrometry and flame ionization (GC×2GC-MS/FID) to study changes in urinary metabolic signatures of mice subjected to high-fructose diets. Samples are taken from mice fed with normal or fructose-enriched diets provided either in aqueous solution or in solid form and analyzed at three stages of the dietary intervention (1, 6, and 12 weeks). Automated Untargeted and Targeted fingerprinting for 2D data elaboration is adopted for the most inclusive data mining of GC×GC patterns. The UT fingerprinting strategy performs a fully automated peak-region features fingerprinting and combines results from pre-targeted compounds and unknowns across the sample-set. The most informative metabolites, with statistically relevant differences between sample groups, are obtained by unsupervised multivariate analysis (MVA) and cross-validated by multi-factor analysis (MFA) with external standard quantitation by GC-MS. Results indicate coherent clustering of mice urine signatures according to dietary manipulation. Notably, the metabolite fingerprints of mice fed with liquid fructose exhibited greater derangement in fructose, glucose, citric, pyruvic, malic, malonic, gluconic, cis-aconitic, succinic and 2-keto glutaric acids, glycine acyl derivatives (N-carboxy glycine, N-butyrylglycine, N-isovaleroylglycine, N-phenylacetylglycine), and hippuric acid. Untargeted fingerprinting indicates some analytes which were not a priori pre-targeted which provide additional insights: N-acetyl glucosamine, N-acetyl glutamine, malonyl glycine, methyl malonyl glycine, and glutaric acid. Visual features fingerprinting is used to track individual variations during experiments, thereby extending the panorama of possible data elaboration tools. Graphical abstract ᅟ.