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Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory system, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-based expert system (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory system. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory system, that is, by "artificial intelligence smelling".

A large set of volatiles (a metabolome) was isolated by SAFE distillation from 25 high priced rums prepared from sugar cane juice (SCJ) and 26 high priced rums manufactured from sugar cane molasses (SCM). The volatile fractions were first analyzed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF-MS), and the "comprehensive template matching fingerprinting" was used to extract the entire features present in the respective set of volatile compounds. After raw data pretreatment, chemometrics was used to locate marker compounds. Following, a sparse-partial-least-squares discriminant analysis ( sPLS-DA) and a partial-least-squares discriminant analysis (PLS-DA) were applied to a training data set for creating a model. The model was validated using leave-one-out cross validation and tested over an independent data set to evaluate its predictive power. The characteristic fingerprint resulted in a 100% correct classification of sugar cane juice rums, thus achieving the first aim of locating markers for these higher quality rums. Then, past-processing identification within the discriminant features was done to characterize 12 significant marker compounds as 1-decanol, γ-dodecalactone, ethyl 3-methylbutanoate, ethyl nonanoate, 3-furancarboxaldehyde, 1-hexanol, β-ionone, 2- and 3-methylbutanol, methyl decanoate, 3-octanol, and 2-undecanone. Quantitation of eight selected markers by stable isotope dilution assays confirmed higher concentrations in SCJ compared to SCM and served as the final proof to differentiate both types of spirits.

The volatile fraction of murici, bacuri and sapodilla are here studied because of their increasing interest for consumers, abundance of production in Brazil, and the general demand for new flavors and aromas. Their volatile profiles were studied by two High Concentration Capacity Headspace techniques (HCC-HS), Headspace Solid Phase Microextraction (HS-SPME) and Headspace Sorptive Extraction (HSSE), in combination with GC–MS. Murici volatile fraction mainly contains esters (38%), carboxylic acids (19%), aldehydes (11%), alcohols (14%), others (13%) and sulfur compounds; bacuri is characterized by terpenes (41%), non-terpenic alcohols (24%), esters (15%), aldehydes (6%), and others (12%); sapodilla consists of esters (33%), alcohols (27%), terpenes (18%) and others (21%). The GC–MS component co-elution was overcome by GC × GC-qMS. The adoption of modern analysis technologies afforded to achieve a better knowledge of the volatile fraction composition of these fruit pulps by increasing substantially the number of compounds identified.

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 study proposes an investigation strategy to identify sensitive, robust and reliable chemical markers of hazelnut roasting. A fully-automated and validated analytical method, based on Headspace Solid Phase Microextraction (HS-SPME) coupled with Gas Chromatography–Mass Spectrometric detection (GC–MS), for effective off-line monitoring of changes in the volatile profile of high-quality hazelnuts was developed. Samples from two different harvests were submitted to roasting, following different time/temperature protocols and different technologies, enabling chemical changes to be correlated with technological processing and sensory quality. Chemical indices, expressed as analyte response ratio, were defined and their trend observed across roasting profiles. Reliability and robustness of chemical indices were also evaluated, in view of their application to on-line monitoring with Mass Spectrometry-based electronic nose technology (MS-nose). Experiments, simulating on-line chemical characterisation of the volatile fraction, were performed through a fully-automated system. The results confirmed: (a) the effectiveness of single process indicators of roasting selected by the separative method (5-methylfurfural, 1(H)-pyrrole, furfuryl alcohol, 1(H)-pyrrole-2-carboxaldehyde, 1-hydroxy-2-propanone, dihydro-2(3H)-furanone, 5-methyl-(E)-2-hepten-4-one, acetic acid, pyridine, furfural, pyrazine, and several alkyl-pyrazines); and, (b) the reliability of proposed chemical indices: 5-methylfurfural/2,5-dimethylpyrazine, 5-methylfurfural/2-methylpyrazine, 2,5-dimethylpyrazine/2,3-dimethylpyrazine; these maintained a consistent trend versus harvest and sampling/analysis technology.

This study reports and critically discusses the results of a systematic investigation on the effectiveness of different and complementary sampling approaches, based on either sorption and adsorption, treated as a further dimension of a two-dimensional comprehensive gas chromatography-mass spectrometry analytical platform for sensomics. The focus is on the potentials of a group of high concentration capacity (HCC) sample preparation (Solid Phase Microextraction, SPME, Stir Bar Sorptive Extraction, SBSE and Headspace Sorptive Extraction, HSSE) and Dynamic Headspace (D-HS) techniques investigated to provide information useful for fingerprinting and profiling studies of food aroma. Volatiles and semi-volatiles contributing to define whole and nonfat dry milk aroma have been successfully characterized thanks to the combination of effective and selective sampling by HCC and D-HS techniques, high separation and detection power of GC×GC-MS and suitable data elaboration (i.e., Comprehensive Template Matching Fingerprinting - CTMF). Out of the sample preparation techniques investigated, HSSE and SBSE have shown to be really effective for sensomics studies because of their high concentration factors, providing highly representative profiles as well as analyte recovery suitable for GC-Olfactometry even with high odor threshold (OT) markers or potent odorants in sub-trace amounts.

a b s t r a c t The continuous interest in non-targeted profiling induced the development of tools for automated cross- sample analysis. Such tools were found to be selective or not comprehensive thus delivering a biased view on the qualitative/quantitative peak distribution across 2D sample chromatograms. Therefore, the performance of non-targeted approaches needs to be critically evaluated. This study focused on the development of a validation procedure for non-targeted, peak-based, GC×GC-MS data profiling. The procedure introduced performance parameters such as specificity, precision, accuracy, and uncertainty for a profiling method known as Comprehensive Template Matching. The performance was assessed by applying a three-week validation protocol based on CITAC/EURACHEM guidelines. Optimized 1D and 2D retention times search windows, MS match factor threshold, detection threshold, and template threshold were evolved from two training sets by a semi-automated learning process. The effectiveness of proposed settings to consistently match 2D peak patterns was established by evaluating the rate of mismatched peaks and was expressed in terms of results accuracy. The study utilized 23 different 2D peak patterns providing the chemical fingerprints of raw and roasted hazelnuts (Corylus avellana L.) from different geographical origins, of diverse varieties and different roasting degrees. The validation results show that non-targeted peak-based profiling can be reliable with error rates lower than 10% independent of the degree of analytical variance. The optimized Comprehensive Template Matching procedure was employed to study hazelnut roasting profiles and in particular to find marker compounds strongly dependent on the thermal treatment, and to establish the correlation of potential marker compounds to geographical origin and variety/cultivar and finally to reveal the characteristic release of aroma active compounds. © 2012 Elsevier B.V. All rights reserved.