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.

Effectiveness of modified atmosphere and vacuum packaging in preserving the volatilome of Stelvio PDO cheese over time.

We aimed to assay the effectiveness of vacuum or modified atmosphere packaging in preserving the organoleptic characteristics of already ripened slices of Stelvio Protected Designation of Origin cheese during 3 months of storage. A multi-omics panel, including metagenomic and metabolomic analyses, was implemented together with physicochemical and sensory analyses. Among the 177 volatiles identified, 30 out of the 50 potent odorants were found to be prevalent, regardless of packaging. Isovaleric acid showed the highest relative intensity in all samples. Caproic and caprylic acids always increased during storage, while metabolites such as dodecane and 2,3-butanediol always decreased. Slow proteolysis occurred during storage, but did not differentiate cheese samples. The type of packaging differentiated the microbiota and volatile profile, with modified atmosphere packaging keeping the volatilome more stable. Out of the 50 potent odorants, 9 were relevant to sample discrimination, with 8-nonen-2-one, 2-nonanone, and caproic acid being more abundant in stored samples.

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.

Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation.

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

Microwave-assisted extraction in closed vessel in food analysis.

Microwave-assisted extraction (MAE) is an important technique in analytical chemistry. It offers several advantages over traditional extraction methods, such as improved extraction efficiency, shorter extraction times, reduced solvent consumption, and enhanced analyte recovery. Using microwaves, heat is directly applied to the sample, leading to rapid and efficient extraction of target compounds by enhancing the solubility and diffusion of the target compounds, thus requiring lower solvent volume. Therefore, MAE can be considered a more environmentally friendly and cost-effective option facilitating the transition toward greener and more sustainable analytical chemistry workflows. This contribution systematically reviews the application of MAE to a selection of target compounds/compounds classes of relevance for food quality and safety assessment. As inclusion criteria, MAE active temperature control and molecularly-resolved characterization of the extracts were considered. Contents include a brief introduction of the principles of operation, available systems characteristics, and key parameters influencing extraction efficiency and selectivity. The application section covers functional food components (e.g., phenols, diterpenes, and carotenoids), lipids, contaminants (e.g., polycyclic aromatic hydrocarbons and mineral oil hydrocarbons), pesticides, veterinary drug residues, and a selection of process contaminants and xenobiotics of relevance for food safety.

Lactiplantibacillus plantarum, lactiplantibacillus pentosus and inulin meal inclusion boost the metagenomic function of broiler chickens.

BACKGROUND: The inclusion of alternative ingredients in poultry feed is foreseen to impact poultry gut microbiota. New feeding strategies (probiotics/prebiotics) must be adopted to allow sustainable productions. Therefore, the current study aimed to use metagenomics approaches to determine how dietary inclusion of prebiotic (inulin) plus a multi-strain probiotic mixture of Lactiplantibacillus plantarum and Lactiplantibacillus pentosus affected microbiota composition and functions of the gastro-intestinal tract of the broilers during production. Fecal samples were collected at the beginning of the trial and after 5, 11 and 32 days for metataxonomic analysis. At the end of the trial, broilers were submitted to anatomo-pathological investigations and caecal content was subjected to volatilome analysis and DNAseq. RESULTS: Probiotic plus prebiotic inclusion did not significantly influence bird performance and did not produce histopathological alterations or changes in blood measurements, which indicates that the probiotic did not impair the overall health status of the birds. The multi-strain probiotic plus inulin inclusion in broilers increased the abundance of Blautia, Faecalibacterium and Lachnospiraceae and as a consequence an increased level of butyric acid was observed. In addition, the administration of probiotics plus inulin modified the gut microbiota composition also at strain level since probiotics alone or in combination with inulin select specific Faecalibacterium prausnitzi strain populations. The metagenomic analysis showed in probiotic plus prebiotic fed broilers a higher number of genes required for branched-chain amino acid biosynthesis belonging to selected F. prausnitzi strains, which are crucial in increasing immune function resistance to pathogens. In the presence of the probiotic/prebiotic a reduction in the occurrence of antibiotic resistance genes belonging to aminoglycoside, beta-lactamase and lincosamide family was observed. CONCLUSIONS: The positive microbiome modulation observed is particularly relevant, since the use of these alternative ingredients could promote a healthier status of the broiler's gut.

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.

Integrated Strategy for Informative Profiling and Accurate Quantification of Key-Volatiles in Dried Fruits and Nuts: An Industrial Quality Control Perspective.

Edible nuts and dried fruits, usually traded together in the global market, are one of the cornerstones of the Mediterranean diet representing a source of essential nutrients and bioactives. The food industry has an interest in the selection of high-quality materials for new product development while also matching consumers' expectations in terms of sensory quality. In this study, walnuts (Juglans regia), almonds (Prunus dulcis), and dried pineapples (Ananas comosus) are selected as food models to develop an integrated analytical strategy for the informative volatile organic compounds (VOCs) quali- and quantitative profiling. The study deals with VOCs monitoring over time (12 months) and in the function of storage conditions (temperature and atmosphere).VOCs are targeted within those: (i) with a role in the product's aroma blueprint (i.e., key-aromas and potent odorants); (ii) responsible for sensory degradation (i.e., rancidity); and/or (iii) formed by lipid autoxidation process. By accurate quantitative determination of volatile lipid oxidation markers (i.e., hexanal, heptanal, octanal, nonanal, decanal, (E)-2-heptenal, (E)-2-octenal, (E)-2-nonenal) product quality benchmarking is achieved. The combination of detailed VOCs profiling by headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) and accurate quantification of rancidity markers by multiple headspace-SPME (MHS-SPME) answers many different questions about shelf-life (i.e., aroma, storage stability, impact of temperature and storage atmosphere, rancidity level), while providing reliable and robust data for long-range studies and quality controls. The quantification associated with HS-SPME profiling is demonstrated and critically commented on to help the industrial research in a better understanding of the most suitable analytical strategies for supporting primary materials selection and new product development.

Effect of Inoculation with Lentilactobacillus buchneri and Lacticaseibacillus paracasei on the Maize Silage Volatilome: The Advantages of Advanced 2D-Chromatographic Fingerprinting Approaches.

In this study, the complex volatilome of maize silage samples conserved for 229 d, inoculated with Lentilactobacillus buchneri (Lbuc) and Lacticaseibacillus paracasei (Lpar), is explored by means of advanced fingerprinting methodologies based on comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry. The combined untargeted and targeted (UT) fingerprinting strategy covers 452 features, 269 of which were putatively identified and assigned within their characteristic classes. The high amounts of short-chain free fatty acids and alcohols were produced by fermentation and led to a large number of esters. The impact of Lbuc fermentation was not clearly distinguishable from the control samples; however, Lpar had a strong and distinctive signature that was dominated by propionic acid and 1-propanol characteristic volatiles. The approach provides a better understanding of silage stabilization mechanisms against the degradative action of yeasts and molds during the exposure of silage to air.

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.

Corylus avellana L. Natural Signature: Chiral Recognition of Selected Informative Components in the Volatilome of High-Quality Hazelnuts.

The volatile fraction of plant-based foods provides useful functional information concerning sample-related variables such as plant genotype and phenotype expression, pedoclimatic and harvest conditions, transformation/processing technologies, and can be informative about the sensory quality. In this respect, the enantiomeric recognition of the chiral compounds increases the level of information in profiling studies, being the biosynthesis of native compounds often stereo-guided. Chiral native volatiles mostly show an enantiomeric excess that enables origin authentication or support correlation studies between chemical patterns and sensory profiles. This study focuses, for the first time, on the enantiomeric composition of a large set of chiral compounds within the complex volatilome of Corylus avellana L. belonging to different cultivars (Tonda Gentile Romana, Tonda Gentile Trilobata, Anakliuri) and harvested in different geographical areas (Italian and Georgian). Besides native components profiled in raw kernels, volatiles formed after technological treatment (i.e., roasting) are also considered. Headspace solid-phase microextraction combined with enantioselective gas chromatography-mass spectrometry enables the accurate tracking and annotation of about 150 compounds across many samples. The results show that chiral compounds have diagnostic distribution patterns within hazelnut volatilome with cultivar and harvest region playing the major role. Moreover, being some of these chiral molecules also key-aromas, their distribution has a decisive impact on the sensory properties of the product. In particular, the enantiomeric composition of (E)-5-methyl-2-hepten-4-one (filbertone) resulted to be discriminant for origin authentication. The enantiomeric distribution showed, according to literature, an excess of the (S)-enantiomer in both raw and roasted samples volatilome with larger differences in raw samples. The amount of both (R) and (S)-filbertone increases during roasting; the most marked increase for (R)-enantiomer is observed in Italian samples, thus supporting evidence of better hedonic properties and more pleasant odor and aroma.

Corylus avellana L. Aroma Blueprint: Potent Odorants Signatures in the Volatilome of High Quality Hazelnuts.

The volatilome of hazelnuts (Corylus avellana L.) encrypts information about phenotype expression as a function of cultivar/origin, post-harvest practices, and their impact on primary metabolome, storage conditions and shelf-life, spoilage, and quality deterioration. Moreover, within the bulk of detectable volatiles, just a few of them play a key role in defining distinctive aroma (i.e., aroma blueprint) and conferring characteristic hedonic profile. In particular, in raw hazelnuts, key-odorants as defined by sensomics are: 2,3-diethyl-5-methylpyrazine (musty and nutty); 2-acetyl-1,4,5,6-tetrahydropyridine (caramel); 2-acetyl-1-pyrroline (popcorn-like); 2-acetyl-3,4,5,6-tetrahydropyridine (roasted, caramel); 3-(methylthio)-propanal (cooked potato); 3-(methylthio)propionaldehyde (musty, earthy); 3,7-dimethylocta-1,6-dien-3-ol/linalool (citrus, floral); 3-methyl-4-heptanone (fruity, nutty); and 5-methyl-(E)-2-hepten-4-one (nutty, fruity). Dry-roasting on hazelnut kernels triggers the formation of additional potent odorants, likely contributing to the pleasant aroma of roasted nuts. Whiting the newly formed aromas, 2,3-pentanedione (buttery); 2-propionyl-1-pyrroline (popcorn-like); 3-methylbutanal; (malty); 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel); dimethyl trisulfide (sulfurous, cabbage) are worthy to be mentioned. The review focuses on high-quality hazelnuts adopted as premium primary material by the confectionery industry. Information on primary and secondary/specialized metabolites distribution introduces more specialized sections focused on volatilome chemical dimensions and their correlation to cultivar/origin, post-harvest practices and storage, and spoilage phenomena. Sensory-driven studies, based on sensomic principles, provide insights on the aroma blueprint of raw and roasted hazelnuts while robust correlations between non-volatile precursors and key-aroma compounds pose solid foundations to the conceptualization of aroma potential.

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.

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.