Search Results (203)

Fermented vinegars have been highlighted globally for their health benefits. The benefits can differ according to the type of vinegar; therefore, we investigated the differences of 15 grain (GV), 10 persimmon (PV), and 14 apple vinegars (AV) using integrated non-targeted and targeted metabolome analyses. We profiled non-volatile and volatile metabolites using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), ultra-high-performance liquid chromatography–orbitrap–tandem mass spectrometry, and headspace–solid-phase microextraction–GC-TOF-MS. Among the 132 identified metabolites, 73 non-volatile and 40 volatile metabolites showed significant differences across the three vinegar types. Amino acids, hydroxy fatty acids, phenolic compounds, aldehydes, pyrazines, and sulfides were abundant in GV. Some phenolic compounds, alcohols, and esters were abundant in PV, whereas carbohydrates, flavonoids, and terpenoids were abundant in AV, contributing to nutrients, tastes, and flavors. Bioactivity assays revealed that GV showed notable antioxidant activity, whereas PV and AV had the highest total phenolic and flavonoid contents, respectively. Through quantitative analysis, we revealed that acetic acid, propionic acid, butanoic acid, lactic acid, and alanine were major components in the three types of vinegar, although their composition was different in each vinegar. Our comprehensive qualitative and quantitative metabolite comparison provides insights into the differences among the three vinegar types, classified according to their raw materials. Full article

Base Mine Lake (BML) is the first full-scale demonstration of water-capped tailing technology in a pit lake to reclaim lands impacted by surface mining in the Alberta Oil Sands Region (AOSR). Biogeochemical cycling and/or exchange near the fluid water interface (FWI) of the organic-rich fluid fine tailings (FFT) can hinder the reclamation process. To monitor this activity, sedimentary depth profiles were collected from three platforms (P1 to P3) at BML. Seventy-four chromatographically well-resolved petroleum hydrocarbon (PHC) isomers were quantified at each depth interval using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC/TOFMS). The range of total concentrations of all isomers examined across the FFT was the highest at P1 (range = 3.6 × 10⁰–5.5 × 10³ ng/g TOC), second highest at P2 (range = 3.8 × 10⁰–1.9 × 10³ ng/g TOC), and lowest at P3 (range = 5.6 × 10⁰–7.1 × 10² ng/g TOC). The elevated levels of the same isomers across platforms suggest a consistent source fingerprint. While the source fingerprint was mostly consistent across the platforms and depths, Principal Component Analysis (PCA) identified small differences between geospatial locations caused by variations in specific isomer concentrations. Hierarchical Clustering Analysis (HCA) identified the isomers responsible for the PCA separation, showing that the concentrations of low-molecular-weight n-alkanes (C11–C13) and drimane varied compared to the heavier PHCs with depth. These alkanes are the most biodegradable of the compounds identified in this study, and their variations may reflect biogeochemical cycling within the FFT. Combining these statistical tools provided deeper insight into how isomer concentrations vary with depth, helping to identify possible influences like changing inputs, biogeochemical cycling, and species exchange with the water column. Full article

Cooked-rice aroma strongly affects consumer choice, yet the chemical traits distinguishing glutinous rice from normal-amylose japonica rice remain underexplored because earlier studies targeted only a few dozen volatiles using one-dimensional gas chromatography–mass spectrometry (GC-MS). In this study, four glutinous and seven normal Japanese cultivars were cooked under identical conditions, their headspace volatiles trapped with MonoTrap and qualitatively profiled by comprehensive GC × GC-TOFMS. The two-dimensional platform resolved 1924 peaks—about ten-fold previous coverage—and, together with hierarchical clustering, PCA, heatmap visualization and volcano plots, cleanly separated the starch classes (78.3% cumulative PCA variance; Euclidean distance > 140). Volcano plots highlighted 277 compounds enriched in the glutinous cultivars and 295 in Koshihikari, including 270 compounds that were not previously documented in rice. Normal cultivars were dominated by ethers, aldehydes, amines and other nitrogenous volatiles associated with grainy, grassy and toasty notes. Glutinous cultivars showed abundant ketones, furans, carboxylic acids, thiols, steroids, nitro compounds, pyrroles and diverse hydrocarbons and aromatics, yielding sweeter, fruitier and floral accents. These results expand the volatile library for japonica rice, provide molecular markers for flavor-oriented breeding and demonstrate the power of GC × GC-TOFMS coupled with chemometrics for grain aroma research. Full article

Oxidative stress is a biological imbalance that contributes to cellular damage and is a major driver of aging and age-related disorders, prompting the search for natural antioxidant agents. Our study is a phytochemical, electrochemical, and biological characterization of the antioxidant potential of aqueous extracts from aerial parts of A. occidentale—leaves, bark, fruit, and cashew nuts—traditionally used in folklore medicine. Extracts were analyzed using FT-IR spectroscopy, GC × GC-TOFMS, polyphenol quantification, and antioxidant capacity assays (ABTS, FRAP, DPPH). Biological activity was tested in different mice and human cell lines (SH-SY5Y, MEF, ARPE-19, and HLECs). Aqueous extracts from the leaves and bark of A. occidentale exhibited significantly higher antioxidant activity compared to those from the fruit and cashew nut. These extracts showed elevated polyphenol content and strong performance in antioxidant capacity assays. In vitro, leaf and bark extracts enhanced cell viability under H₂O₂-induced oxidative stress, preserved mitochondrial membrane potential, and upregulated cytoprotective genes (HMOX1, NQO1, GCLC, and GCLM) in multiple cell lines. In contrast, fruit and nut extracts showed minimal antioxidant activity and no significant gene modulation. Our findings underscore the therapeutic potential of A. occidentale leaf and bark extracts as effective natural antioxidants and support their further development as candidates for phytotherapeutic interventions. Full article

Aroma is an important indicator for evaluating the quality of baijiu. In this study, we determined the aroma-active compounds in four representative brands of strong-aroma baijiu from Sichuan and Jianghuai regions through GC-MS/O, and GC-TOF-MS quantification. In addition, the non-volatile composition of four baijiu samples was quantified by BSTFA derivatization and GC-MS. By constructing a full recombination model containing both volatile and non-volatile components, the effect of different groups of non-volatile compounds on the aroma of strong-aroma baijiu was evaluated through recombination-omission tests. A total of 72 aroma-active compounds and 59 non-volatile compounds were identified and quantified. The results indicated that pyrazines, furfural, and furan derivatives displayed higher aroma intensities in strong-aroma baijiu produced in Sichuan compared to that produced in Jianghuai. The recombination model that included both aroma-active and non-volatile compounds showed a closer resemblance to the original baijiu samples, underscoring the critical role these compounds play in shaping the dominant aroma profile of strong-aroma baijiu. Non-volatile compounds significantly influenced six aroma attributes: fruity, sweet, sauce, pit, acidic, and alcoholic notes. Omission tests revealed that among posorly volatile organic acids, monobasic acids had distinct effects on the aroma profile, while dibasic acids did not show any noticeable influence on the sensory characteristics. Full article

The need to maximize aquaculture production while addressing environmental and food security challenges posed by climate change has driven research towards the development of functional aquafeeds that enhance performance and immunity in farmed species. However, exposure to dietary and environmental stressors affects marine organisms, altering key metabolic pathways best understood through high-throughput “omics” tools. This study assessed the effects of Asparagopsis taxiformis supplementation on central metabolic pathways by analyzing changes in primary metabolite levels in the liver of farmed Diplodus sargus under optimal and suboptimal temperature conditions. Results showed that seaweed supplementation had a beneficial effect on the fish’s primary metabolome; however, inclusion levels and rearing conditions played a crucial role in determining outcomes. While 1.5% supplementation maintained a balanced primary metabolome under optimal temperature conditions, 3.0% supplementation most effectively mitigated the adverse effects of acute thermal stress during a marine heatwave. These findings highlight the nutritive and functional potential of A. taxiformis supplementation in aquafeeds for marine omnivorous fish species and emphasize the importance of evaluating functional aquafeeds under suboptimal rearing conditions. Overall, our results demonstrate the value of metabolomics in elucidating the molecular basis underlying biological pathways in farmed marine fish and optimizing production through climate-smart dietary strategies. Full article

Background and Objectives: Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 lack sensitivity and specificity, particularly for early-stage disease. Advances in metabolomics and machine learning, particularly explainable artificial intelligence (XAI), offer new opportunities for identifying robust biomarkers and improving diagnostic accuracy. This study aimed to identify and validate serum-based metabolic biomarkers for breast cancer using advanced metabolomic profiling techniques and a Light Gradient Boosting Machine (LightGBM) model. Additionally, SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability and biological insight. Materials and Methods: The study included 103 breast cancer patients and 31 healthy controls. Serum samples underwent liquid and gas chromatography–time-of-flight mass spectrometry (LC-TOFMS and GC-TOFMS). Mutual Information (MI), Sparse Partial Least Squares (sPLS), Boruta, and Multi-Objective Feature Selection (MOFS) approaches were applied to the data for biomarker discovery. LightGBM, AdaBoost, and Random Forest were employed for classification and to identify class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). SHAP analysis ranked metabolites based on their contribution to model predictions. Results: Compared to other feature selection approaches, the MOFS approach was more robust in terms of predictive performance, and metabolites identified by this method were used in subsequent analyses for biomarker discovery. LightGBM outperformed the AdaBoost and Random Forest models, achieving 86.6% accuracy, 89.1% sensitivity, 84.2% specificity, and an F1-score of 87.0%. SHAP analysis identified 2-Aminobutyric acid, choline, and coproporphyrin as the most influential metabolites, with dysregulation of these markers associated with breast cancer risk. Conclusions: This study is among the first to integrate SHAP explainability with metabolomic profiling, bridging computational predictions and biological insights for improved clinical adoption. This study demonstrates the effectiveness of combining metabolomics with XAI-driven machine learning for breast cancer diagnostics. The identified biomarkers not only improve diagnostic accuracy but also reveal critical metabolic dysregulations associated with disease progression. Full article

In northern China’s arid and semi-arid regions, evergreen trees demonstrate significant cold tolerance to natural low-temperature stress during winter. However, the metabolic strategies and their associated properties underlying their overwintering adaptation remain incompletely elucidated. This study aims to reveal the metabolic properties of natural low-temperature adaptation strategies in five evergreen trees through metabolomic analysis and to identify key metabolites and their dynamic variation patterns. The GC-TOF-MS platform was used to investigate seasonal differential metabolites in five evergreen trees across January, April, July, and October and further explore core differentially expressed metabolites responsive to low-temperature stress. The results demonstrated that the seasonal changes in the chlorophyll content of five evergreens exhibited distinct patterns, that significant differences were observed between Juniperus sabina L. and Picea meyeri R., Ammopiptanthus mongolicus M., Buxus sinica var. parvifolia M.Cheng, and Pinus tabuliformis C., and that no significant differences were found among the other tree species. A total of 427 metabolites were detected in the metabolome; when assessing seasonal dynamics, it was found that the types of differentially expressed metabolites in the five evergreens underwent significant changes. In spring, the differentially expressed metabolites included some carbohydrates, alcohols, organic acids, and lipids. During summer and autumn, the largest number of differentially expressed metabolites accumulated, mainly including carbohydrates, organic acids, and amino acid compounds. In winter, while Picea meyeri primarily accumulated carbohydrates, the remaining four species mainly accumulated organic acids, along with a small number of alcohols, phenylpropanoids, and polyketides. Three shared carbohydrate metabolites, L-threose, galactinol, and gluconic lactone, were commonly downregulated across all species. Additionally, coniferous trees collectively accumulated 3,6-anhydro-D-galactose, showing downregulation. The KEGG enrichment analysis of winter-accumulated metabolites revealed significant associations with the pentose phosphate pathway, amino acid metabolism, phenylpropanoid biosynthesis, the tricarboxylic acid cycle, and ascorbate–aldarate metabolism pathways. Through comparative analysis with the summer growth season, we ultimately identified the core differentially expressed metabolites of the five evergreens, providing potential metabolic markers for the breeding of cold-tolerant species. In summary, these findings provide critical metabolomic insights into how plants adapt to low temperatures, significantly enhancing our understanding of the metabolic foundations of cold tolerance in evergreen species. Full article

Vitamin E deficiency (VED) represents a common micronutrient deficiency in dairy cows (DCs), leading to severe degenerative diseases, oxidative stress, immune dysfunction, and various health issues, ultimately causing significant economic losses for the global dairy sector. Accordingly, our objective was to explore the metabolic features of VED-afflicted cows by combining the untargeted gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) and targeted liquid chromatography-mass spectrometry (LC-MS) to identify effective serum VED biomarkers. Untargeted GC-TOF-MS analysis identified 31 differential metabolites (DMs): 20 were overexpressed and 11 were suppressed in the VED group compared to the healthy control group. These DMs were enriched in six major metabolic pathways: glycine, serine, and threonine; alanine, aspartate, and glutamate; cysteine and methionine; tyrosine; primary bile acid biosynthesis; and nitrogen metabolisms. These outcomes show that VED significantly disrupts amino acid/lipid/energy metabolism pathways in DCs. Further targeted LC-MS quantification revealed significant alterations in key metabolites, including increased levels of norepinephrine, glycine, cysteine, and L-glutamine, as well as a significant reduction in cholesterol concentrations. Binary logistic regression analysis identified norepinephrine and cholesterol as strong candidate biomarkers for VED. Receiver operating characteristic curve analysis established outstanding diagnostic accuracy for norepinephrine and cholesterol (for both p < 0.001, area under the curve = 0.980 and 0.990, correspondingly), with sensitivities and specificities of 90% and 100%, respectively. In conclusion, this study integrates untargeted and targeted metabolomics approaches to reveal VED-caused metabolic disruptions in DCs, particularly in amino acid/lipid/energy metabolism pathways. Norepinephrine and cholesterol were identified as highly accurate serum VED biomarkers with excellent diagnostic performance. Early detection and timely intervention using these biomarkers could promote disease treatment and cow health, as well as productivity, and decrease economic losses. Full article

Bio-sniffers represent a novel detection technology that demonstrates significant potential in medical diagnostics. Specifically, they assess disease conditions and metabolic status through the detection of volatile organic compounds (VOCs) in exhaled breath. Unlike conventional methods such as gas chromatography-mass spectrometry (GC-MS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), bio-sniffers provide rapid, sensitive, and portable detection capabilities. In this review, we examine the metabolic pathways and detection methods of specific VOCs in the human body, and their roles as disease biomarkers, and focus on the detection principles, performance characteristics, and medical applications of two bio-sniffer types: electrical and optical sensors. Finally, we systematically discuss the current challenges facing bio-sniffers in VOC monitoring, outline future development directions, and provide suggestions for improving sensitivity and reducing environmental interference. Full article

Aim: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatments are available for BC, most cancer-related deaths are due to incurable metastases. Therefore, the early diagnosis and treatment of BC are crucial before metastasis. Mammography and ultrasonography are primarily used in the clinic for the initial identification and staging of BC; these methods are useful for general screening but have limitations in terms of sensitivity and specificity. Omics-based biomarkers, like metabolomics, can make early diagnosis much more accurate, make tracking the disease’s progression more accurate, and help make personalized treatment plans that are tailored to each tumor’s specific molecular profile. Metabolomics technology is a feasible and comprehensive method for early disease detection and biomarker identification at the molecular level. This research aimed to establish an interpretable predictive artificial intelligence (AI) model using plasma-based metabolomics panel data to identify potential biomarkers that distinguish BC individuals from healthy controls. Methods: A cohort of 138 BC patients and 76 healthy controls were studied. Plasma metabolites were examined using LC-TOFMS and GC-TOFMS techniques. Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were evaluated using performance metrics such as Receiver Operating Characteristic-Area Under the Curve (ROC AUC), accuracy, sensitivity, specificity, and F1 score. ROC and Precision-Recall (PR) curves were generated for comparative analysis. The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. Results: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). RF also achieved the best Precision-Recall Area under the Curve (PR AUC) at 0.989. SHAP search found glycerophosphocholine and pentosidine as the most significant discriminatory metabolites. Uracil, glutamine, and butyrylcarnitine were also among the significant metabolites. Conclusions: Metabolomics biomarkers and an explainable AI (XAI)-based prediction model showed significant diagnostic accuracy and sensitivity in the detection of BC. The proposed XAI system using interpretable metabolite data can serve as a clinical decision support tool to improve early diagnosis processes. Full article

This publication reports the controlled cultivation of Zanthoxylum chiloperone var. angustifolium Engl. (Rutaceae) in several growth substrates under controlled greenhouse conditions. This plant is well-known for its anti-Chagas (trypanocidal) activity, related to the presence of several β-carboline alkaloids. The metabolomic study of Z. chiloperone seedlings over two years of growth (2018–2020) was performed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). The canthin-6-one alkaloids, canthin-6-one and 5-methoxy-canthin-6-one, were putatively identified in Z. chiloperone extracts. Finally, in vitro and in silico studies of trypanocidal activity were performed, suggesting that canthin-6-one alkaloids could interact with the main pharmacological targets against Trypanosoma cruzi, cruzain protease, dihydroorotate dehydrogenase, lanosterol 14-alpha-demethylase, farnesyl diphosphate, and squalene synthases. Full article

To develop and utilize Folium Artemisia argyi (FAA) tea resources, UPLC-MS/MS, HS-GC-IMS, and HS-SPME/GC×GC-TOFMS were adopted to analyze its volatile and non-volatile compounds, when harvested from March to June, in combination with its antioxidant activity. Here, 1742 volatile compounds and 8726 non-volatile compounds were identified, with 75 differential volatile metabolites and 36 key flavor compounds screened. Notably, 1-octen-3-one, (E)-2-octenal, (E)-2-undecenal, and heptanal were identified as major contributors to the sweet, fruity, green, and herbal aromas, and the concentration of them was highest in June-harvest FAA tea. Furthermore, metabolomics revealed that there were 154 non-volatile differential metabolites in FAA tea at four harvest times, which were mainly related to amino acid biosynthetic pathways. Samples harvested in June also showed the strongest antioxidant capacity, which was positively correlated with D-xylitol, L-glutamic acid, honokiol, and costunolide. These findings highlight June as the optimal harvest time, providing FAA tea with superior flavor and enhanced antioxidant properties, underscoring its potential as a valuable resource for functional food development. Full article

The physicochemical and sensory properties of wines are influenced by several factors, starting in the vineyard and evolving during the winemaking stages. After bottling, variables such as bottle position, closure type, storage temperature, and storage time shape wine characteristics. In this study, red wines stored for approximately 0.5 and 3 years with natural cork, micro-agglomerated cork stoppers, and screw cap closures were analyzed. Various techniques were employed to investigate changes during bottle storage, including the determination of volatile components by comprehensive gas chromatography-mass spectrometry with time-of-flight analyzer (GC × GC-ToFMS), phenolic profile by ultra-high-performance liquid chromatography, coupled with tandem mass spectrometry (UHPLC-DAD-MSⁿ), general physicochemical parameters, the oxygen transfer rate of cork stoppers, and sensorial analysis performed by a trained panel. The results revealed that the type of closure created distinct environments within the bottles, slightly influencing both sensory attributes and chemical evolution of the red wines. These findings highlight the value of combining diverse analytical techniques to reveal closure-driven differences, with volatile compound profiling emerging as the most sensitive methodology. Additionally, this study emphasizes that differences modulated by the wine–closure pairing, which become more pronounced during storage, can serve as an oenological tool in the construction of a wine’s identity. Full article

Despite its promising potential, the capabilities of Saccharomyces paradoxus in commercial winemaking are still unutilized and require further investigation. In this study, the effects of fermentation by a S. paradoxus strain P01-161 on the composition of Malvazija istarska white wine in two harvest years were investigated. A range of complementary standard and metabolomics analysis approaches were applied, including OIV methods for basic parameters; HPLC-DAD-RI for organic acids, glycerol, and proteins; UPLC/MS/MS for phenolic compounds; and GC/FID, GC/MS, and GC × GC/TOF-MS for volatile compounds. The harvest year exhibited a significant impact, but many distinctive traits of S. paradoxus versus S. cerevisiae control wines were consistent across the seasons. These included reductions in malic acid and certain phenols and pathogenesis-related proteins. Saccharomyces paradoxus fermentation yielded higher levels of glycerol, volatile acidity, and specific thaumatin-like proteins. Among a total of 474 identified volatile compounds, S. paradoxus exhibited lower concentrations of several odoriferous alcohols, acids, and esters, as well as higher concentrations of β-damascenone, acetaldehyde, isobutyric acid, ethyl 2-methylbutyrate, ethyl acetate, isobutyl acetate, various esters of succinic and lactic acids, accompanied by numerous minor compounds, when compared to S. cerevisiae. These differences suggest the potential for distinct sensory profiles produced by the two yeasts, indicating that S. paradoxus could be a promising alternative for white wine production. Full article