Search Results (573)

To assess the impact of developmental stages on the postharvest quality of loquat, fruits at five distinct maturity phases (T1–T5) were systematically analyzed. This study employed a range of analytical techniques to conduct a comprehensive examination of the variations in quality, nutritional content, and volatile compounds of loquat across different developmental stages. Sensory evaluation indicated that the T4 stage achieved the highest score (93.21 ± 1.13), with significant differences observed in a* (6.56–13.08) and b* values (16.91–22.16). Both moisture content (45.45–86.94 g/100 g) and fruit firmness (8.65–3.31 N) exhibited a decline with delayed harvest (p < 0.05). Scanning electron microscopy (SEM) analysis revealed that, as the developmental period progressed, the pores in the loquat cell walls enlarged, and the cellular structure became increasingly relaxed. GC–MS and GC–IMS detected 48 and 47 volatile compounds, respectively. Using orthogonal partial least squares discriminant analysis (OPLS–DA), we identified 14 key compounds that distinguish the five developmental stages. E-nose and E-tongue analyses showed significant changes in the smell and taste profiles of loquats over time, with the T1 stage notably different from later stages. This study offers important insights and guidance on the postharvest quality of loquats at various developmental stages. Full article

Drought stress severely compromises the physiological integrity and secondary metabolism of medicinal plants. This study integrated physiological, biochemical, and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analyses to investigate the effects of exogenous methyl jasmonate (MeJA) on drought-stressed Ilex rotunda seedlings. Drought reduced relative water content by 29% and chlorophyll by >50%, while elevating H₂O₂ (76%) and malondialdehyde (120%). MeJA application mitigated these impairments, reducing oxidative markers by 25% and enhancing non-enzymatic antioxidant capacity, as shown by a 74% increase in DPPH radical scavenging activity and a 141% rise in total phenolic content. Hierarchical clustering analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA), and pathway mapping confirmed a significant reprogramming of the phenolic metabolome, particularly within phenylpropanoid and benzoate biosynthesis pathways. Drought + MeJA-treated plants exhibited a distinct and enriched profile compared to both well-watered control and drought-stressed groups. This reprogramming specifically elevated key hydroxycinnamates, including verbascoside and neochlorogenic acid (increased by 50% and 52%, respectively), while suppressing alternative phenolic branches. These findings demonstrate that MeJA orchestrates a shift from enzymatic scavenging to a potent metabolite-based antioxidant system, positioning it as an effective elicitor for enhancing drought resilience and enriching the high-value phytochemicals in I. rotunda. Full article

Black pepper is the most widely used spice crop globally and has significant economic value, making it a target for economically motivated adulteration. A wide range of organic and inorganic bulking materials has been used as adulterants in black pepper. Development of rapid non-targeted screening methods for use at different stages of the black pepper supply chain is extremely important for the identification and prevention of evolving fraudulent practices. This study has assessed the potential of benchtop Fourier Transform infrared with attenuated total reflectance (FTIR-ATR), benchtop Fourier Transform near-infrared (FT-NIR), and two handheld NIR spectrometers, coupled with chemometrics, for the discrimination of black pepper (Piper nigrum), pepper from other species and genera (non-Piper nigrum) and a broad range (n = 27) of endogenous and exogenous adulterants. Spiked samples were prepared to imitate pepper adulteration with seven different adulterants at five levels of adulteration (5%, 25%, 50%, 75%, 95% w/w). Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved 100% total prediction accuracy for both FTIR-ATR and FT-NIR in differentiating authentic Piper nigrum and adulterant samples. The handheld microNIR 1700ES resulted in a 91.30% correct classification rate, while the SCiO model achieved 86.96% prediction accuracy. Detection of black pepper adulteration with multiple adulterants was performed using data-driven soft independent modelling of class analogy (DD-SIMCA). The highest performance of the DD-SIMCA model was achieved by FTIR-ATR (100% sensitivity and 100% specificity) followed by FT-NIR (98% sensitivity and 99% specificity). The handheld microNIR 1700ES resulted in 95% sensitivity and 90% specificity. This study demonstrated that FTIR-ATR and FT-NIR, coupled with DD-SIMCA, can effectively detect black pepper adulteration with multiple endogenous and exogenous adulterants. The handheld NIR (microNIR1700ES) clearly demonstrated the potential for rapid and effective verification of Piper nigrum authenticity outside the laboratory. Full article

Flavor-relevant trace volatiles and microbial communities were examined in six sauce-aroma high-temperature Daqu samples. Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) quantified 210 trace volatile compounds across 14 chemical classes. Orthogonal partial least squares discriminant analysis (OPLS-DA) with variable importance in projection (VIP) screening was integrated with sensory scoring, correlation analysis, and molecular docking to an olfactory receptor model. Volatile profiles showed clear stratification in total abundance. Pyrazines dominated the high-total group. Tetramethylpyrazine served as a major driver. Sensory evaluation indicated that aroma explained overall quality best. (E)-2-pentenal and dimethyl trisulfide showed significant positive associations with aroma and overall scores. In the olfactory receptor, the polar residue module that provides directional constraints for Daqu odor activation was formed by Ser75, Ser92, Ser152, Ser258, Thr74, Thr76, Thr98, Thr200, Gln99, and Glu94. The hydrogen-bond or charge network was further reinforced by Arg150, Arg262, Asn194, His180, His261, Asp182, and Gln181. The core discriminant set comprised acetic acid, hexanoic acid, (E)-2-pentenal, nonanal, decanal, dimethyl trisulfide, trans-3-methyl-2-n-propylthiophane, 2-hexanone oxime, ethyl linoleate, propylene glycol, 2-ethenyl-6-methylpyrazine, 4-methylquinazoline, 5-methyl-2-phenyl-2-hexenal, and 1,2,3,4-tetramethoxybenzene. Sequencing revealed higher bacterial diversity than fungal. Bacillus and Kroppenstedtia were dominant bacterial genera. Aspergillus, Paecilomyces, Monascus, and Penicillium were major fungal genera. Correlation patterns suggested that Bacillus and Monascus were positively linked to acetic acid and 1,2,3,4-tetramethoxybenzene. Together, these results connected chemical fingerprints, sensory performance, receptor-level plausibility, and microbial ecology. Concrete targets are provided for quality control of high-temperature Daqu. Full article

Rheumatoid arthritis involves chronic synovitis and immune-metabolic dysregulation, highlighting a need for multi-target therapies that jointly modulate metabolism and inflammation. We developed glycyrrhiza protein–paeoniflorin self-assembled nanoparticles (GP-PF NPs) and investigated their anti-arthritic mechanism in adjuvant-induced arthritis (AIA) mice, using UHPLC-Orbitrap-MS-based metabolomics. Male C57BL/6 mice (n = 42) were assigned to the control, model, GP-PF NPs, paeoniflorin, glycyrrhiza protein, physical mixture, and celecoxib groups. All groups except controls received complete Freund’s adjuvant, and treatments were given intraperitoneally for 10 days. GP-PF NPs produced the greatest reduction in paw thickness versus the model (p < 0.0001) and outperformed all other active treatments, which was consistent with the improved histopathology. UHPLC-Orbitrap-MS detected 473 serum metabolites, and the model group showed 59 significant changes versus the control. GP-PF NPs significantly modulated 108 metabolites and yielded robust OPLS-DA separation from the model (R²Y = 0.98; Q² = 0.742). Venn and pathway analyses identified 43 NP-specific metabolites enriched in glycerophospholipid metabolism, including glycerophosphocholine, 1-oleylglycerophosphocholine, PE (16:0/16:0), phosphocholine, and sphingosine-1-phosphate. These metabolites were selectively normalized toward control levels by GP-PF NPs. qPCR further showed that GP-PF NPs significantly reduced synovial PI3K, AKT, mTOR, NLRP3, Caspase-1, and GSDMD mRNA overexpression (all p < 0.001 vs. model). Correlation analysis indicated significant associations between key serum lipids and synovial genes (e.g., PI3K positively correlated with several metabolites, r = 0.71–0.82; mTOR negatively correlated with sphinganine 1-phosphate and glycerophosphocholine, r = −0.65 and −0.54). These data suggest that GP-PF NPs ameliorate AIA and are associated with the normalization of glycerophospholipid-related metabolic perturbations and reduced synovial mRNA expression of the PI3K/AKT/mTOR-NLRP3 pathway, supporting their potential as a metabolism-inflammation preclinical oriented anti-arthritic nanomedicine. Full article

Purpose: The purpose of this study is to investigate the bioactive constituents of Atractylodes Rhizome (AR) and to explore its mechanism of action in the treatment of rheumatoid arthritis (RA). Methods: The research mainly adopts the methods of tissue metabolomics and network pharmacology. Firstly, we employed a metabolomics strategy to obtain the metabolite profile and utilized PCA/OPLS-DA analyses to identify the differential metabolites involved in the treatment of RA by AR. Subsequently, we determined the key target metabolic pathways of AR in RA treatment. Next, a network pharmacology approach was employed to identify active compounds, potential targets, and signaling pathways for AR in RA treatment, with a PPI network constructed. These predictions were then validated through molecular docking simulations, followed by in vivo verification using a CFA-induced RA rat model. The anti-RA efficacy was evaluated through synovial histopathology and cytokine assays, with the key mechanistic insights being confirmed at the molecular level by RT-qPCR and WB. Results: The results of the metabolomics study showed that AR regulated 28 differential metabolites linked to glycerophospholipid, linoleic acid, and alpha-linolenic acid metabolism. Network pharmacology identified Wogonin, Atractyloyne, and Atractylenolide II as key active compounds, acting through pathways such as Pathways in cancer and PI3K-Akt signaling, combined with the metabolites to jointly analyze the metabolic pathways, and were verified by correlation analysis. Molecular docking confirmed the main active ingredients’ strong binding to core targets. In AIA rats, AR treatment reduced synovial inflammation and lowered serum levels of IL-6 and MMP-9. At the molecular level, AR up-regulated Bcl-2, down-regulated Bax, and inhibited the SRC/JAK2-STAT3 pathway by decreasing EGFR, SRC, JAK2, and p-STAT3 expression. Conclusion: These findings may illuminate the mechanism by which Atractylodes Rhizome exerts its effects via the JAK2/SRC-STAT3 axis, thereby revealing its potential mechanism of action against rheumatoid arthritis. Full article

Cynomorium songaricum is an important medicinal holoparasitic plant in the arid regions of northwestern China. Its extremely low seed germination rate relies on chemical signals released from the rhizosphere of specific host plants. This study aimed to elucidate the chemical basis of host recognition by C. songaricum by characterizing and comparing the rhizosphere volatile metabolomes of five host plant species. Gas chromatography–mass spectrometry (GC–MS) was used to analyze the rhizosphere volatiles of three Nitraria species (N. roborowskii, N. sibirica, N. tangutorum), Peganum multisectum, and Zygophyllum xanthoxylum. Multivariate statistical analyses, including PCA, HCA, and OPLS-DA, were employed to identify shared and differential metabolites. A total of 116 volatile compounds were identified. Alkanes were the predominant metabolite class (accounting for 46.27–76.47% in each host), and 11 C₁₁–C₁₆ alkanes were shared by all species. Notably, Z. xanthoxylon (belonging to a different family) exhibited a distinct metabolic profile, with a significantly higher proportion of benzene derivatives (35.82%) compared to the other hosts. PCA and cluster analysis revealed family/genus-specific clustering patterns, with Z. xanthoxylon forming a separate cluster. Several differential metabolites unique to Z. xanthoxylon possessed antimicrobial and stress-resistant activities. This study reveals the chemical signatures of C. songaricum host roots. The shared alkanes are proposed as potential background signals for general recognition, though this hypothetical role requires experimental validation. Family/genus-specific compounds might be involved in host selection. The unique chemical profile of Z. xanthoxylon suggests that C. songaricum may employ a flexible recognition strategy, enabling it to parasitize plants across different families. These findings provide fundamental data for understanding the chemical basis of host–parasite interactions in this species. Full article

Tea is produced from the fresh leaves of the tea plant (Camellia sinensis), and the quality of tea is directly dictated by its raw material. Although factors such as tea cultivar, fertilization, and cultivation practices are known to affect fresh leaf quality, the specific influence of altitude remains poorly understood. In this present study, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to investigate the non-volatile metabolites in fresh tea leaves grown at two different altitudes (350 m and 600 m). A total of 2323 metabolites were identified, with flavonoids and phenolic acids representing the dominant classes. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further revealed 116 differential metabolites between groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that several key pathways were differentially activated, including those related to the biosynthesis of kaempferol, luteolin, and flavones, as well as nucleotides and jasmonic acid metabolism. In addition, marked differences were observed in the accumulation patterns of lipids, phenolic acids, and flavonoids between leaves grown at the two altitudes. These findings provide valuable insights into the role of altitude in shaping the metabolic composition and flavor formation of tea. Full article

Anguilla japonica is a catadromous fish, and the Yangtze River Estuary serves as a crucial passage for A. japonica migrating downstream to the sea. A large number of adult A. japonica appear on the market during the peak migration period. Due to the lack of effective discrimination basis, it is difficult to distinguish the source of samples in market supervision. Therefore, there is an urgent need to trace the origin of A. japonica from different water bodies. This study analyzed muscle elemental fingerprints of 21 elements to determine the geographical origin of A. japonica. The results showed that A. japonica from different habitats had distinct elemental compositions in their muscles. Specifically, A. japonica from estuary waters (EW) was characterized by significantly higher levels of V and Hg compared to other water bodies. Na was identified as a key discriminant element among different habitats, with its content significantly increasing in river waters (RW), EW, and offshore waters (OW), respectively. Discriminant analysis selected four discriminant elements (V, Hg, Na and Cu) from 21 elemental compositions, among which V, Hg, and Na were the three key distinguishing elements. Based on the composition of these four discriminant elements in the muscles of A. japonica from different habitats, hierarchical cluster analysis (HCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA) were applied and validated, successfully achieving rapid and accurate origin tracing and verification for new samples, achieving 100% classification accuracy. Therefore, the application of muscle EFA can achieve the geographical traceability of A. japonica from different habitats. The analytical method and verification process for origin tracing established in this study can be successfully applied to market supervision for tracing the origin of samples with unknown sources. Full article

This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources—coriander, orange blossom, astragalus, rosemary, and chehelgiah. While HS-SPME-GC-MS combined with traditional chemometrics (e.g., PCA, LDA, OPLS-DA) is well-established for honey discrimination, the application and direct comparison of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models represent a significant advancement in multiclass prediction accuracy and model robustness. A total of 57 honey samples were analyzed to generate detailed volatile organic compound (VOC) profiles. Key chemotaxonomic markers were identified: anethole in coriander and chehelgiah, thymoquinone in astragalus, p-menth-8-en-1-ol in orange blossom, and dill ester (3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran) in rosemary. Principal component analysis (PCA) revealed clear separation across botanical classes (PC1: 49.8%; PC2: 22.6%). Three classification models—RF, XGBoost, and NN—were trained on standardized, stratified data. The NN model achieved the highest accuracy (90.32%), followed by XGBoost (86.69%) and RF (83.47%), with superior per-class F1-scores and near-perfect specificity (>0.95). Confusion matrices confirmed minimal misclassification, particularly in the NN model. This work establishes HS-SPME-GC-MS coupled with deep learning as a rapid, sensitive, and reliable tool for multiclass honey botanical authentication, offering strong potential for real-time quality control, fraud detection, and premium market certification. Full article

Background: Post-exercise recovery involves coordinated metabolic restoration and redox rebalancing. Although dietary polyphenols have been proposed to facilitate recovery, the metabolic mechanisms underlying their effects—particularly during the recovery phase—remain insufficiently characterized. This study aimed to investigate how polyphenol supplementation modulates post-exercise metabolic recovery using an integrative metabolomics approach. Methods: We conducted a secondary analysis of publicly available longitudinal human LC–MS metabolomics datasets from exercise intervention studies with polyphenol supplementation. Datasets were obtained from the NIH Metabolomics Workbench and MetaboLights repositories; study-level metadata were used as provided by the original investigators. Global metabolic trajectories were assessed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Targeted analyses focused on purine degradation intermediates and redox-related metabolites. Correlation-based network and pathway enrichment analyses were applied to characterize recovery-phase metabolic reorganization. Results: Exercise induced a pronounced global metabolic perturbation in both placebo and polyphenol groups. During recovery, polyphenol supplementation was associated with a partial reversion of the metabolome toward the pre-exercise state, whereas placebo samples remained metabolically displaced. Discriminant metabolite analyses identified purine degradation intermediates and oxidative stress-related lipid species as key contributors to group separation during recovery. Polyphenol supplementation attenuated recovery-phase accumulation of hypoxanthine, xanthine, and uric acid and was associated with a sustained suppression of the uric acid-to-hypoxanthine ratio. Network analyses revealed weakened correlations between purine metabolites and oxidative stress markers, along with reduced network centrality of stress-responsive metabolic hubs. Conclusions: These findings indicate that polyphenol supplementation is associated with accelerated metabolic normalization during post-exercise recovery, potentially through modulation of purine-associated oxidative pathways and system-level metabolic network reorganization. Full article

Gentiana species are widely used in traditional and modern medicine, and Gentiana siphonantha is an important medicinal representative. To evaluate the quality characteristics of cultivated G. siphonantha roots, the accumulation patterns of iridoid glycosides and antioxidant activities across different cultivation ages and harvest months were investigated. Five major iridoid glycosides were quantified, and antioxidant capacity was assessed through DPPH, ABTS, and FRAP assays. Quality was subsequently multidimensionally evaluated using principal component analysis (PCA), orthogonal partial least squares–discriminant analysis (OPLS-DA), membership function analysis, and entropy weight–TOPSIS analysis, and the relationship between iridoid glycoside content and antioxidant activity was analyzed. Results showed that 3-year-old cultivated roots had the highest total iridoid glycoside content (134.60 mg·g⁻¹ DW), indicating the optimal cultivation age. Peak glycoside accumulation occurred in 4-year-old plants harvested in June–July, identifying this period as the optimal harvest time, as supported by multivariate statistical and comprehensive evaluation. Antioxidant activity increased with cultivation age, with samples collected in June or August showing higher capacities, and it was positively correlated with total iridoid glycoside content, particularly with FRAP (p < 0.05). In conclusion, cultivated G. siphonantha exhibits stable quality and favorable antioxidant activity, providing a basis for standardized cultivation, quality evaluation, and rational utilization. Full article

Background: Early molecular changes on the facial skin surface during early adulthood remain insufficiently characterized. We integrated biophysical readouts with untargeted skin surface lipid (SSL) profiling to identify region-dependent, age-associated features in women with combination skin. Methods: Eighty healthy Chinese women were stratified into 22–28 years (n = 40) and 29–35 years (n = 40). Sebum was measured on the cheek and forehead; cheek elasticity, hydration (CM), transepidermal water loss (TEWL), pH, and tone indices were assessed under standardized conditions. SSLs from both regions were profiled by UPLC–QTOF–MS. Differential features were prioritized using OPLS-DA (VIP > 1) with univariate screening (p < 0.05; fold change > 2 or <0.5). Results: TEWL, CM, and pH were comparable between age groups, whereas the older group showed lower cheek elasticity and reduced sebum. Lipidomics revealed clearer remodeling on the cheek than the forehead: 30 and 59 differential SSL features were identified in the cheek and forehead, respectively. Cheek changes in the older group were characterized by lower ceramides (including acylceramides), TG/DG and long-chain fatty acids, alongside relatively higher cholesteryl esters. Conclusions: Conventional barrier indices remained largely stable across this age window, while cheek SSL profiles captured earlier molecular shifts, providing candidates for targeted validation and longitudinal follow-up. Full article

Volatile fingerprint analysis using Gas Chromatography–Ion Mobility Spectrometry (GC-IMS) was applied to differentiate cowhide (22 samples), sheepskin (6 samples), and pigskin (6 samples). A total of 126 signal peaks were detected from the whole GC-IMS dataset, with 96 volatile compounds identified. Principal Component Analysis (PCA) revealed distinct clustering: cowhide exhibited unique volatile profiles, separating itself clearly from sheepskin and pigskin, which showed significant similarity. This was confirmed by Hierarchical clustering, K-means clustering (optimal k = 2), and Partial Least Squares Discriminant Analysis (PLS-DA) (R² = 0.9836, Q² = 0.9040). Cowhide was characterized by exclusive compounds (2-Hexanone, alpha-Thujene, Butyl acetate, 3-Methyl-2-butanol, 2-Heptanone, Hexyl methyl ether-monomer, Diethyl disulfide). Sheepskin and pigskin shared exclusive compounds (2-Methyl propanol, Isobutyl acetate, 2-Pentyl acetate, 3-Penten-2-one, 2,5-Dimethylfuran). Orthogonal PLS-DA (OPLS-DA) further differentiated sheepskin (Ethyl isobutanoate-dimer, Pentyl acetate-dimer, 3-Methyl-2-butanol, 2-Pentanone, 2-Methylbutanol-dimer, 3-Methyl-1-butanol, 2,5-Dimethylfuran, Propan-2-ol, Ethanol-dimer, and alpha-Thujene) and pigskin (Butan-2-one, Pentanal-dimer, 1-Pentanal-monomer, Ethyl vinyl ether, Z-2-Heptene, and Butyronitrile), identifying alpha-Thujene, 3-Methyl-2-butanol, and 2,5-Dimethylfuran as universal discriminatory markers. GC-IMS coupled with chemometric analysis provides a robust approach for leather authentication. Full article

The flavor and aroma of kiwifruit are largely influenced by the concentration of Volatile Organic Compounds (VOCs). To analyze the volatile profiles and identify characteristic aroma compounds, this study utilized Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) to analyze the aromatic compounds sourced from seven major production regions in China and New Zealand, covering red-, green-, and yellow-fleshed varieties. A total of 77 VOCs were identified, with esters, aldehydes, and ketones as the dominant classes. Significant regional and varietal differences were observed: red-fleshed kiwifruits from Yunnan exhibited high levels of 2-Vinyl-5-methylfuran, Ethyl formate, and 1-Penten-3-one; green-fleshed fruits from Shaanxi were rich in Limonene and Methyl hexanoate, and those from Yunnan were rich in 1-Propanol and 1-Hexanol; and yellow-fleshed fruits from Henan were characterized by Methyl salicylate and 3-Hydroxy-2-butanone. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully classified kiwifruits by origin and variety, confirming the stability and predictive power of the model (Q2Y > 0.97). This study also elucidated the key metabolic pathways—including lipid oxidation, amino acid degradation, and terpenoid metabolism—underlying the formation of these characteristic VOCs. These findings provide a theoretical foundation for the biochemical regulation of kiwifruit flavor and support the development of origin-tracing and quality-assessment tools based on VOC fingerprints. Full article