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.

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.

Diterpenes stability of commercial blends of roasted and ground coffees packed in copolymer coupled with aluminium and eco-friendly capsules.

Diterpenes are group of compounds of the terpenic fraction of roasted coffee and account for about 7-20 % (w/w) of the lipid fraction. Several parameters can influence their occurrence in coffee beans and beverages including species and post-harvest processing. Diterpenes in coffee have been studied extensively, but to the best of the authors' knowledge, there is no information in the literature on their stability over time. Coffee is a relatively stable product under optimal temperature, humidity and oxygen conditions. However, during storage it can undergo a series of chemical and physical reactions that alter its flavour and lead to rancidity, mainly due to the oxidative reactions that take place on the lipid fraction. In this study, the effect of long-term storage on the diterpene content of different commercial coffee blends and packaging is analysed and critically discussed. The Results show that the storage influences the internal environment of the capsules with an increase in moisture and a decrease in pH favouring more reactive conditions, especially for Eco capsules. Relative stability over time is observed for cafestol and kahweol. dehydro derivatives show a degradation up to T60 independently on the blends and packaging, which is not related to their precursors. The permeability of packaging and blends affect the modification of these components: while a drastic oxidation process takes place in Arabica eco compatible capsules (PC) when acidity and moisture increase, in Arabica/Robusta eco compatible capsules (IC) as well as in Arabica/Robusta and Arabica standard capsules (IS and PS) the peroxides tend to increase resulting in an autocatalytic propagation.

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.

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.

Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning.

This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.

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.

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. 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.

Adulteration of Essential Oils: A Multitask Issue for Quality Control. Three Case Studies: Lavandula angustifolia Mill., Citrus limon (L.) Osbeck and Melaleuca alternifolia (Maiden & Betche) Cheel.

The quality control of essential oils (EO) principally aims at revealing the presence of adulterations and at quantifying compounds that are limited by law by evaluating EO chemical compositions, usually in terms of the normalised relative abundance of selected markers, for comparison to reference values reported in pharmacopoeias and/or international norms. Common adulterations of EO consist of the addition of cheaper EO or synthetic materials. This adulteration can be detected by calculating the percent normalised areas of selected markers or the enantiomeric composition of chiral components. The dilution of the EO with vegetable oils is another type of adulteration. This adulteration is quite devious, as it modifies neither the qualitative composition of the resulting EO nor the marker's normalised percentage abundance, which is no longer diagnostic, and an absolute quantitative analysis is required. This study aims at verifying the application of the two above approaches (i.e., normalised relative abundance and absolute quantitation) to detect EO adulterations, with examples involving selected commercial EO (lavender, bergamot and tea tree) adulterated with synthetic components, EO of different origin and lower economical values and heavy vegetable oils. The results show that absolute quantitation is necessary to highlight adulteration with heavy vegetable oils, providing that a reference quantitative profile is available.

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.

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.

Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls.

Cocoa smoky off-flavour is generated from an inappropriate artificial drying applied on beans to speeding up the post-harvest process and it can affect the quality of the chocolate. The sensory tests are time-consuming, and at present, a fast analytical method to detect this defect in raw materials is not yet available. This study applies a HS-SPME-MS-enose in combination with chemometrics to obtain diagnostic mass-spectral patterns to detect smoked samples and/or as analytical decision maker. SIMCA models provide the best classification results, compared to PLS-DA, with sensitivities exceeding 90% and a high class specificity range of 89-100% depending on the matrix investigated (beans or liquors). Resulting diagnostic ions were related to phenolic derivatives. The discrimination ability of the method has been confirmed by a quantitative analysis through HS-SPME-GC-MS. HS-SPME-MS-enose turned out to be a fast, cost-effective and objective approach for high throughput analytical screening to discard defective cocoa samples.

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

The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) Æ´-nonalactone, Æ´-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and Æ´-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples.

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

This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m2) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO.Graphical abstract.

Sedentariness and Urinary Metabolite Profile in Type 2 Diabetic Patients, a Cross-Sectional Study.

Recent findings indicate a significant association between sedentary (SED)-time and type 2 diabetes mellitus(T2DM). The aim of this study was to investigate whether different levels of SED-time could impact on biochemical and physiological processes occurring in sedentary and physically inactive T2DM patients. In particular, patients from the "Italian Diabetes and Exercise Study (IDES)_2 trial belonging to the first and fourth quartile of SED-time were compared. Urine samples were analyzed by comprehensive two-dimensional gas chromatography(GC×GC) with parallel detection by mass spectrometry and flame ionization detection(GC×2GC-MS/FID). This platform enables accurate profiling and fingerprinting of urinary metabolites while maximizing the overall information capacity, quantitation reliability, and response linearity. Moreover, using advanced pattern recognition, the fingerprinting process was extended to untargeted and targeted features, revealing diagnostic urinary fingerprints between groups. Quantitative metabolomics was then applied to analytes of relevance for robust comparisons. Increased levels of glycine, L-valine,L-threonine, L-phenylalanine, L-leucine, L-alanine, succinic acid, 2-ketoglutaric acid, xylitol, and ribitol were revealed in samples from less sedentary women. In conclusion, SED-time is associated with changes in urine metabolome signatures. These preliminary results suggest that reducing SED-time could be a strategy to improve the health status of a large proportion of diabetic patients.

Cocoa smoky off-flavor: Chemical characterization and objective evaluation for quality control.

Cocoa smoky off-flavor is due to inappropriate post-harvest processing and cannot be removed in the subsequent chocolate-manufacturing steps. To date, no reliable analytical method to detect key-analytes responsible for smoky off-flavor in incoming raw material is available. This study aims to develop an analytical method, suitable for quality control, to detect smoky markers. The cocoa volatilome was first profiled by headspace solid phase microextration combined with comprehensive two-dimensional gas chromatography-mass spectrometry from a set of representative smoky and non-smoky samples; advanced fingerprinting revealed the chemicals responsible for the off-flavor. The results served to develop a 1D-GC method suitable for routine application. Ten identified smoky markers were subjected to accurate quantification, thereby defining operative ranges to accept/reject incoming bean samples. On average, these markers are present in smoky samples at 7 to 125 fold concentrations vs. those in non-smoky beans, ranging from 32.5 ng/g for naphtalene to 721.8 ng/g for phenol.