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.

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.

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.

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.

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.

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.

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.

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.

Ionic liquids as water-compatible GC stationary phases for the analysis of fragrances and essential oils.

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

Evolution of potent odorants within the volatile metabolome of high-quality hazelnuts (Corylus avellana L.): evaluation by comprehensive two-dimensional gas chromatography coupled with mass spectrometry.

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

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.

Cultivable gut bacteria provide a pathway for adaptation of Chrysolina herbacea to Mentha aquatica volatiles.

BACKGROUND: A chemical cross-talk between plants and insects is required in order to achieve a successful co-adaptation. In response to herbivory, plants produce specific compounds, and feeding insects respond adequately7 to molecules produced by plants. Here we show the role of the gut microbial community of the mint beetle Chrysolina herbacea in the chemical cross-talk with Mentha aquatica (or watermint). RESULTS: By using two-dimensional gas chromatography-mass spectrometry we first evaluated the chemical patterns of both M. aquatica leaf and frass volatiles extracted by C. herbacea males and females feeding on plants, and observed marked differences between males and females volatiles. The sex-specific chemical pattern of the frass paralleled with sex-specific distribution of cultivable gut bacteria. Indeed, all isolated gut bacteria from females belonged to either α- or γ-Proteobacteria, whilst those from males were γ-Proteobacteria or Firmicutes. We then demonstrated that five Serratia marcescens strains from females possessed antibacterial activity against bacteria from males belonging to Firmicutes suggesting competition by production of antimicrobial compounds. By in vitro experiments, we lastly showed that the microbial communities from the two sexes were associated to specific metabolic patterns with respect to their ability to biotransform M. aquatica terpenoids, and metabolize them into an array of compounds with possible pheromone activity. CONCLUSIONS: Our data suggest that cultivable gut bacteria of Chrysolina herbacea males and females influence the volatile blend of herbivory induced Mentha aquatica volatiles in a sex-specific way.

Effectiveness of Global, Low-Degree Polynomial Transformations for GCxGC Data Alignment.

As columns age and differ between systems, retention times for comprehensive two-dimensional gas chromatography (GCxGC) may vary between runs. To properly analyze GCxGC chromatograms, it often is desirable to align the retention times of chromatographic features, such as analyte peaks, between chromatograms. Previous work by the authors has shown that global, low-degree polynomial transformation functions, namely affine, second-degree polynomial, and third-degree polynomial, are effective for aligning pairs of two-dimensional chromatograms acquired with dual second columns and detectors (GC×2GC). This work assesses the experimental performance of these global methods on more general GCxGC chromatogram pairs and compares their performance to that of a recent, robust, local alignment algorithm for GCxGC data [ Gros Anal. Chem. 2012 , 84 , 9033 ]. Measuring performance with the root-mean-square (RMS) residual differences in retention times for matched peaks suggests that global, low-degree polynomial transformations outperform the local algorithm given a sufficiently large set of alignment points, and are able to improve misalignment by over 95% based on a lower-bound benchmark of inherent variability. However, with small sets of alignment points, the local method demonstrated lower error rates (although with greater computational overhead). For GCxGC chromatogram pairs with only slight initial misalignment, none of the global or local methods performed well. In some cases with initial misalignment near the inherent variability of the system, these methods worsened alignment, suggesting that it may be better not to perform alignment in such cases.

Alignment for comprehensive two-dimensional gas chromatography with dual secondary columns and detectors.

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

Comprehensive two-dimensional gas chromatography and food sensory properties: potential and challenges.

Modern omics disciplines dealing with food flavor focus the analytical efforts on the elucidation of sensory-active compounds, including all possible stimuli of multimodal perception (aroma, taste, texture, etc.) by means of a comprehensive, integrated treatment of sample constituents, such as physicochemical properties, concentration in the matrix, and sensory properties (odor/taste quality, perception threshold). Such analyses require detailed profiling of known bioactive components as well as advanced fingerprinting techniques to catalog sample constituents comprehensively, quantitatively, and comparably across samples. Multidimensional analytical platforms support comprehensive investigations required for flavor analysis by combining information on analytes' identities, physicochemical behaviors (volatility, polarity, partition coefficient, and solubility), concentration, and odor quality. Unlike other omics, flavor metabolomics and sensomics include the final output of the biological phenomenon (i.e., sensory perceptions) as an additional analytical dimension, which is specifically and exclusively triggered by the chemicals analyzed. However, advanced omics platforms, which are multidimensional by definition, pose challenging issues not only in terms of coupling with detection systems and sample preparation, but also in terms of data elaboration and processing. The large number of variables collected during each analytical run provides a high level of information, but requires appropriate strategies to exploit fully this potential. This review focuses on advances in comprehensive two-dimensional gas chromatography and analytical platforms combining two-dimensional gas chromatography with olfactometry, chemometrics, and quantitative assays for food sensory analysis to assess the quality of a given product. We review instrumental advances and couplings, automation in sample preparation, data elaboration, and a selection of applications.

Translating 2D-Chromatographic Fingerprinting to Quantitative Volatilomics: Unrevealing Compositional Changes in Maize Silage Volatilome for Robust Marker Discovery.

This study examines the complex volatilome of maize silage, both with and without commercial heterolactic strain inoculation, conserved for 100 days, using quantitative volatilomics. Chemical classes linked to microbial metabolism were analyzed across a concentration range from 10 µg g-1 to 1 ng g-1. A reference method using comprehensive two-dimensional gas chromatography (GC × GC) and time-of-flight mass spectrometry (TOF MS) with loop-type thermal modulation (TM) was translated to a differential-flow modulation (FM) platform with parallel MS and flame ionization detector (FID) detection. With translation, the original method's analyte elution order and resolution are preserved. The new method allowed for accurate quantification using multiple headspace solid-phase microextraction (MHS-SPME) and FID-predicted relative response factors (RRFs). Both methods showed comparable discriminatory power with FM GC × GC-MS/FID achieving satisfactory quantification accuracy without external calibration. Analysis of 98 volatiles provided insights into silage fermentation, supporting marker discovery and correlations with silage quality and stability.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction.

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

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.