Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

Acrylamide in coffee: What is known and what still needs to be explored. A review.

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.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils.

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.

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.

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits.

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 ±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.

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.

Robust Markers of Coffee Consumption Identified Among the Volatile Organic Compounds in Human Urine.

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.

Evaluation of volatile bioactive secondary metabolites transfer from medicinal and aromatic plants to herbal teas: Comparison of different methods for the determination of transfer rate and human intake.

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 ß-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.

Strategies for Accurate Quantitation of Volatiles from Foods and Plant-Origin Materials: A Challenging Task.

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.

Chemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control.

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.

Analysis of essential oils and fragrances with a new generation of highly inert gas chromatographic columns coated with ionic liquids.

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-20M). 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.

Fractionated dynamic headspace sampling in the analysis of matrices of vegetable origin in the food field.

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 (0.5mM) EtOH/water solution, comprising 16 compounds with different polarities and volatilities, structures ranging from C5 to C15 and vapor pressures from 4.15kPa (2,3-pentandione) to 0.004kPa (t-ß-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.

Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography - Mass spectrometry combined with high concentration capacity sample preparation techniques: Toward a fully automated sensomic assessment.

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.

Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC-MS to describe the sensory properties in cup.

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.

Toward a definition of blueprint of virgin olive oil by comprehensive two-dimensional gas chromatography.

This study investigates the applicability of an iterative approach aimed at defining a chemical blueprint of virgin olive oil volatiles to be correlated to the product sensory quality. The investigation strategy proposed allows to fully exploit the informative content of a comprehensive multidimensional gas chromatography (GC×GC) coupled to a mass spectrometry (MS) data set. Olive oil samples (19), including 5 reference standards, obtained from the International Olive Oil Council, and commercial samples, were submitted to a sensory evaluation by a Panel test, before being analyzed in two laboratories using different instrumentation, column set, and software elaboration packages in view of a cross-validation of the entire methodology. A first classification of samples based on untargeted peak features information, was obtained on raw data from two different column combinations (apolar×polar and polar×apolar) by applying unsupervised multivariate analysis (i.e., principal component analysis-PCA). However, to improve effectiveness and specificity of this classification, peak features were reliably identified (261 compounds), on the basis of the MS spectrum and linear retention index matching, and subjected to successive pair-wise comparisons based on 2D patterns, which revealed peculiar distribution of chemicals correlated with samples sensory classification. The most informative compounds were thus identified and collected in a "blueprint" of specific defects (or combination of defects) successively adopted to discriminate Extra Virgin from defected oils (i.e., lampante oil) with the aid of a supervised approach, i.e., partial least squares-discriminant analysis (PLS-DA). In this last step, the principles of sensomics, which assigns higher information potential to analytes with lower odor threshold proved to be successful, and a much more powerful discrimination of samples was obtained in view of a sensory quality assessment.

A further tool to monitor the coffee roasting process: aroma composition and chemical indices.

Coffee quality is strictly related to its flavor and aroma developed during the roasting process, that, in their turn, depend on variety and origin, harvest and postharvest practices, and the time, temperature, and degree of roasting. This study investigates the possibility of combining chemical (aroma components) and physical (color) parameters through chemometric approaches to monitor the roasting process, degree of roasting, and aroma formation by analyzing a suitable number of coffee samples from different varieties and blends. In particular, a correlation between the aroma composition of roasted coffee obtained by HS-SPME-GC-MS and degree of roasting, defined by the color, has been researched. The results showed that aroma components are linearly correlated to coffee color with a correlation factor of 0.9387. The study continued looking for chemical indices: 11 indices were found to be linearly correlated to the color resulting from the roasting process, the most effective of them being the 5-methylfurfural/2-acetylfuran ratio (index).

Quantitative analysis of volatiles from solid matrices of vegetable origin by high concentration capacity headspace techniques: determination of furan in roasted coffee.

The study compares standard addition (SA), stable isotope dilution assay (SIDA) and multiple headspace extraction (MHE) as methods to quantify furan and 2-methyl-furan in roasted coffee with HS-SPME-GC-MS, using CAR-PDMS as fibre coating, d(4)-furan as internal standard and in-fibre internal standardization with n-undecane to check the fibre reliability. The results on about 150 samples calculated with the three quantitation approaches were all very satisfactory, with coefficient of variation (CV) versus the U.S. Food and Drug Administration (FDA) method, taken as reference, almost always below the arbitrarily-fixed limit of 15%. Furan was detected in the 1-5 ppm range, 2-methyl-furan in the 4-20 ppm range. Moreover, experimental exponential slopes (Q) and linearity (r) of both furan and 2-methyl-furan MHE regression equation on 50 samples were very similar thus making possible to use the same average Q value for all samples of the investigated set and their quantitation with a single determination. This makes this approach very rapid and competitive in-time with SA and SIDA. A non-separative method (HS-SPME-MS) was also developed in view of possible application on-line monitoring of furan and 2-methyl-furan in a pilot-plant with the aim of optimizing the roasting process to reduce these compounds to a minimum. Sampling times of 20 and 5 min were tested, the latter enabling total analysis time to be reduced to about 9 min. The results on 105 samples with both SIDA and MHE approaches were again highly satisfactory most of the samples giving a CV% versus the conventional methods below 20%. In this case too average Q values for both furan and 2-methyl-furan were used for MHE. The separative method presented very good repeatability (RSD% always below 10%) and intermediate precision over three months (RSD% always below 15%); performance were similar for the non-separative method, with repeatability (RSD%) always below 12% and intermediate precision over three months (RSD%) always below 15%. The sensitivity of both separative and non-separative methods was also very good, LOD and LOQ being in the ppb range for both furan and 2-methyl-furan, i.e. well below the amounts present in the roasted coffee samples.

Solvent-enhanced headspace sorptive extraction in the analysis of the volatile fraction of matrices of vegetable origin.

The solvent-enhanced headspace sorptive extraction technique aims at modifying PDMS polarity using a solvent to increase its concentration capability. In solvent-enhanced headspace sorptive extraction, a PDMS tubing closed at both ends by small glass stoppers and filled with an organic solvent is suspended in the sample headspace for a fixed time. After sampling, the sampled analytes are recovered from the PDMS tubing by thermal desorption and online transferred to a GC-flame ionization detector or GC-MS system for analysis. Cyclohexane, iso-octane, ethyl acetate, acetone, acetonitrile and methanol were tested as PDMS modifiers to sample the volatile fractions of sage (Salvia lavandulifolia Vahl.), thyme (Thymus vulgaris L.) and roasted coffee. Ethyl acetate was found to be the most effective PDMS modifier for all matrices investigated; although to a lesser extent, cyclohexane also increased component recoveries with sage and thyme. Acetone, acetonitrile and methanol did not increase PDMS recovery, while isooctane was excluded because of its interaction with the polymer. The results show that solvent-modified PDMS extends the range of sampled headspace components with different polarities, increases the recovery of many of them, improves sensitivity in trace analysis, speeds up recovery and gives repeatability comparable with that of unmodified PDMS.

Targeted and non-targeted approaches for complex natural sample profiling by GCxGC-qMS.

The present study examines the ability of targeted and non-targeted methods to provide specific and complementary information on groups of samples on the basis of their component distribution on the two-dimensional gas chromatography (GCxGC) plane. The volatile fraction of Arabica green and roasted coffee samples differing in geographical origins and roasting treatments and the volatile fraction from juniper needles, sampled by headspace-solid phase microextraction, were analyzed by GCxGC-qMS and sample profiles processed by different approaches. In the target analysis profiling, samples submitted to different roasting cycles and/or differing in origin and post-harvest treatment are characterized on the basis of known constituents (botanical, technological, and/or aromatic markers). This approach provides highly reliable results on quali-quantitative compositional differences because of the authentic standard confirmation, extending and improving the specificity of the comparative procedure to trace and minor components. On the other hand, non-targeted data-processing methods (e.g., direct image comparison and template-based fingerprinting) include in the sample comparisons and correlations all detected sample components, offering an increased discrimination potential by identifying compounds that are comparatively significant but not known targets. Results demonstrate the ability of GCxGC to explore in depth the complexity of samples and emphasize the advantages of a comprehensive and multidisciplinary approach to improve the level of information provided by GCxGC separation.