Search Results (1,177)

This study evaluated the potential of electronic nose (E-nose) technology to discriminate Spanish-style green table olives spoiled by different bacterial strains. Microbial growth, physicochemical properties, sensory attributes, and volatile organic compounds (VOCs) profiles were analyzed to assess spoilage patterns. The results indicated strain-dependent microbial survival during incubation, with Bacillus cereus and Enterobacter cloacae showing the highest tolerance. Inoculated olives exhibited significant changes in color, texture, pH, phenolic content, and antioxidant activity compared to the Control. Sensory evaluation revealed a reduction in positive attributes and the emergence of defects such as cooked, rancid, and woody aromas, particularly in olives inoculated with B. cereus and Escherichia coli. VOC analysis confirmed these alterations, showing strain-specific increases in aldehydes, phenols, and esters, along with reductions in alcohols and acids. Principal component analysis (PCA) of E-nose data successfully distinguished two groups—spoiled and non-spoiled samples—explaining 84.8% of variance, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a classification accuracy of 90.4%. These findings highlight the E-nose as a rapid, non-destructive, and reliable tool for detecting bacterial spoilage in table olives, with potential applications in quality control and early spoilage detection. Full article

The functional genomic mechanisms contributing to aging-associated decline in fertility in men remain insufficiently elucidated. This study investigated age-related alterations in the sperm metabolome of healthy fertile Arab men across three groups: young adult (21–30 years, n = 6), late adult (31–40 years, n = 7), and advanced age (41–51 years, n = 5). Metabolomics was performed using LC-MS/MS. Statistical/functional analyses were performed using MetaboAnalyst-Pro. A total of 380 metabolites were identified, of which 164 showed significant differences (p < 0.05) across age groups. Principal component analysis, partial least squares-discriminate (PLS-DA), and sparse PLS-DA consistently demonstrated distinct metabolomic clustering between young adult and advanced age groups. Notably, in the advanced-age spermatozoa, L-homocysteine was undetectable, while methyloctadecanoyl-CoA was uniquely abundant. Biomarker analysis identified 137 potential aging-sperm biomarkers (AUC = 1), including upregulated (e.g., pentadecanoyl-CoA, (3S)-3-hydroxylinoleoyl-CoA, CDP-DG(LTE4/20:4(8Z11Z14Z17Z)), uracil) and downregulated (e.g., (S)-hydroxyoctanoyl-CoA, DG(22:6/18:4), L-homocysteine, N-myristoyl serine) metabolites. These biomarkers participate in key sperm domains, including motility, energy metabolism, membrane remodeling, oxidative-stress regulation, and fertilization. In conclusion, advancing age disrupts sperm “metabolostasis” (metabolite homeostasis essential for normal function), compromising their physiological integrity and fertilization competence. The identified biomarkers offer promising targets for interventions to preserve sperm health and mitigate age-related fertility decline. Full article

The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive approach to determine the maturity of the Hass avocado crop based on machine learning techniques. The approach consists of a low-cost, non-invasive bioimpedance spectroscopy system operating in the 1–10 kHz range, featuring a custom Analog Front End (AFE) and a tetrapolar surface probe to mitigate skin contact resistance, which collects data for predictive models of avocado maturity. To evaluate the quality of the approach, a longitudinal field study (n = 100) was conducted in a commercial orchard in Cundinamarca, Colombia, tracking complex impedance features—Magnitude, Phase Angle, Resistance, and Reactance—of tagged fruits over 8 weeks across four measurement timepoints. The predictive performance of a classical chemometric model (PLS-DA), non-linear classifiers (SVM, Random Forest), and a temporal Deep Learning (LSTM) architecture was compared using a Stratified Group K-Fold Cross-Validation scheme to prevent data leakage across fruits from the same tree. The 4-electrode configuration successfully isolated mesocarp impedance, identifying the 5–7.2 kHz band as the most sensitive to physiological maturation. In turn, the LSTM model achieved a mean accuracy of 92.0% and an AUC of 0.94, outperforming the other models by 4.0% in mean accuracy. The results demonstrate that modeling the temporal trajectory of impedance, rather than single-point measurements, improves harvest maturity classification in Hass avocados, providing a scalable, low-cost alternative to destructive testing. Full article

Advanced sensing technologies increasingly support monitoring and decision-making processes in modern agriculture. This study investigates the feasibility of developing a harvest timing monitoring workflow based on a portable hyperspectral imaging (HSI) system in the visible–near-infrared (VIS-NIR: 400–1000 nm) range, coupled with machine learning. A hierarchical Partial Least Squares–Discriminant Analysis (Hi-PLS-DA) model was developed and tested to discriminate harvestable from non-harvestable plants of Brassica rapa subsp. sylvestris through the identification of open flowers within otherwise closed flower buds in the raceme. The classification included four target plant classes, i.e., green inflorescences, green leaves, yellow flowers, and yellow leaves, along with two non-target classes, background and not-classified (NC), which were included to support the classification process. The predicted hyperspectral images demonstrated a clear distinction between closed and open flowers, supported by satisfactory classification performance (sensitivity, specificity, precision, and F1-score: 0.78–1.00). This workflow proved effective in handling intrinsic outdoor hyperspectral variability, mitigating illumination and canopy texture, and offers useful methodological insights for the possible future integration of HSI-based approaches into automated field applications, paving the way for rapid, real-time harvest decision support. Full article

Glass is a highly recyclable material that provides substantial environmental benefits, including savings in raw materials and energy as well as a reduction in CO₂ emissions. To ensure the production of high-quality secondary raw materials, container glass from municipal waste separate collection must be accurately separated by color in recycling plants, where only minimal color mixing is tolerated. Color sorting is therefore a key step in glass recycling, as it directly affects both the quality and the market value of recycled cullet. Given the increasingly stringent color quality requirements for recycled glass and the high fraction of cullet used in container glass, advanced technological solutions are needed to improve sorting accuracy. In this study, a visible–near-infrared (VIS-NIR: 400–1000 nm) hyperspectral imaging (HSI) approach integrated with machine learning (ML) is proposed for the automated classification of post-consumer glass fragments from bottles and jars into five color categories: brown, dark green, light green, half-white and white. A hierarchical Partial Least Squares-Discriminant Analysis (PLS-DA) model combined with an object-based analysis strategy was developed to optimize color recognition. The proposed system achieved sensitivity and specificity values between 0.910 and 1.000, demonstrating excellent robustness and predictive capability. Validation on independent datasets confirmed the model’s reliability, with all color glass fragments correctly classified at the object level. The results highlight the potential of HSI-ML systems to enhance color sorting accuracy and process efficiency in recycling plants, contributing to improved material recovery and the advancement of sustainable, circular glass production. Full article

Crete’s rich heritage of indigenous wine grapes remains underexplored in terms of chemical composition, with many cultivars yet to be fully characterized. This study presents a comprehensive analysis of the phenolic profile of 67 monovarietal Cretan wines produced by 10 wineries (42 white, 25 red) from 12 varieties—eight white (Assyrtiko, Dafni, Malvazia, Melissaki, Moschato Spinas, Plito, Vidiano, and Vilana) and four red (Kotsifali, Liatiko, Mandilaria, and Romeiko). A targeted LC–QTOF–MS workflow covering 45 phenolic compounds (flavonoids and non-flavonoids) was applied. Varietal differences were assessed using heteroscedasticity-robust univariate statistics (Welch’s ANOVA with Games–Howell post hoc comparisons and effect-size estimation) and explored by multivariate analyses (PCA and HCA); cross-validated PLS-DA was used for descriptive classification, and MFA integrated the targeted phenolic matrix with classical indices (e.g., total phenolics, tannins, and color metrics). Red wines exhibited stronger variety-linked phenolic structuring than white wines, whereas white-wine differentiation was driven by a limited subset of marker phenolics. Given the central role of phenolic composition in overall wine quality, this study provides the first detailed phenolic characterization of 12 key indigenous Cretan grape varieties. Full article

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide, yet only a fraction of smokers develops the disease, suggesting protective mechanisms in resilient individuals. Identifying airway-localized molecular signatures may improve our understanding of disease pathomechanisms and support hypothesis generation for biomarker research. In this pilot study, induced sputum from smokers with COPD (n = 28) and smokers without COPD (n = 16; Global Initiative for Chronic Obstructive Lung Disease (GOLD)-defined pre-COPD) was analyzed by untargeted proteomics, metabolomics, and lipidomics. After quality control, 1180 proteins, 187 metabolites, and 1234 lipids were retained. Analyses included univariate models with false discovery rate adjustment and multivariate analyses (PCA, PLS-DA), followed by pathway enrichment and protein interaction network analysis. While few features remained significant after FDR correction, consistent cross-omics patterns were observed. COPD was characterized by ↑ glutathione, creatine, and L-arginine; ↓ CCDC88A and ↑ STAT3 and SYDE2; and broad lipid remodeling involving phosphatidylcholines, sphingolipids, and eicosanoids. Network analysis highlighted STAT3 as a highly connected node linking COPD-related genes. These findings suggest that the multi-omic profiling of induced sputum can capture coherent airway-localized molecular signatures such as oxidative stress, cytoskeletal remodeling, and Rho-family GTPase signaling. However, the results should be interpreted as exploratory and require validation in functional studies. Full article

Large Language Models (LLMs) have successfully performed calculation-based tasks, generated diverse data visualizations, and executed chemometric analyses. This study systematically evaluated the performance of Microsoft M365 Copilot (GPT-5) across 35 representative questions spanning five domains: (1) chemical equilibrium, pH, titration, and buffer calculations; (2) data visualization, including histograms, box plots, correlation plots, and heatmaps; (3) analysis of periodic table properties using principal component analysis (PCA); (4) image interpretation and generation in classroom contexts; and (5) machine learning applications using Partial Least Squares Discriminant Analysis (PLS-DA). All questions were assessed without the use of additional prompting. Across two independent user accounts, identical question sets were administered twice per month between October and December 2025. Copilot consistently produced accurate, step-by-step solutions for equilibrium and acid–base problems, generated high-quality visualizations directly from uploaded datasets, and correctly constructed PCA score and loading plots with appropriate data standardization. Collectively, these findings demonstrate that Copilot offers substantial value for both research-oriented tasks and chemistry education. Full article

Background/Objectives: The COVID-19 pandemic has emphasized the urgent need for non-invasive diagnostic strategies. While breath analysis has been widely investigated, sweat and sebum remain largely unexplored, despite being abundant, chemically diverse, and easily collected. This exploratory study presents a proof-of-concept workflow to evaluate their potential for infection biomarker discovery. Methods: Samples from 51 subjects were analyzed by headspace solid-phase microextraction coupled with gas chromatography and time-of-flight mass spectrometry (HS-SPME-GC/ToF-MS). Over 8000 untargeted volatile compounds were detected, reflecting the high complexity of these matrices. Results: Data refinement and chemometric modelling using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) revealed robust separation between SARS-CoV-2-positive Patients and Controls. Classification accuracies consistently exceeded 95%, demonstrating the robust discriminative performance of the approach. Among the detected volatiles, 2-methylbenzenemethanol acetate emerged as the most informative compound, representing a potential biomarker candidate. Conclusions: This work shows that the sweat and sebum volatilome can be exploited for clinical applications. The workflow integrates non-invasive sampling, comprehensive chromatographic profiling, and advanced statistical modelling, representing a methodological contribution to analytical chemistry. Beyond COVID-19, the strategy provides a potential framework for volatile organic compound (VOC)-based diagnostics across different diseases and supports future development of sensor technologies for translation into healthcare practice. Full article

Nicotine exposure from e-cigarette use remains a growing public health concern, necessitating reliable biomarkers and analytical approaches for long-term exposure assessment. This study aimed to investigate the feasibility of detecting and classifying cotinine, the primary metabolite of nicotine, in fingernails of e-cigarette users using Fourier transform infrared (FTIR) spectroscopy coupled with chemometric analysis. Fingernail samples were collected and extracted from 30 e-cigarette users and 30 non-smokers. Infrared spectra were acquired in attenuated total reflectance mode and analysed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for classification and prediction. Distinct spectral features associated with cotinine were observed in smoker samples, particularly an absorption band near 1277 cm⁻¹ corresponding to C–N stretching vibrations. Quantitative analysis revealed significantly higher cotinine concentrations in smokers compared with non-smokers (p < 0.05, Mann–Whitney U test). Chemometric modelling achieved complete discrimination between groups, with the PLS-DA model demonstrating excellent predictive performance and an area under the receiver operating characteristic (ROC) curve of 1.0. These findings indicate that FTIR spectroscopy combined with chemometric tools provides a rapid and effective approach for cotinine detection in fingernails, supporting its potential application in nicotine exposure assessment. Full article

Rice seed vigor is one of the critical factors determining rice yield and quality. Identifying substances related to seed vigor and rapidly assessing seed vigor by non-destructive methods are of great significance for increasing rice production. This study employed near-infrared diffuse reflectance spectroscopy (NIR-DRS) and transmission spectroscopy (NIR-TS) to evaluate the vigor of naturally aged rice seeds. The NIR-DRS failed to establish a reliable relationship between spectral data and seed vigor, proving ineffective in distinguishing seed vigor. After enhancing the spectral differences between viable and non-viable seeds, the NIR-TS successfully identified high-vigor and non-viable seeds, with a partial least squares discriminant analysis (PLS-DA) model achieving accuracy and germination rates of 84.52% and 88.57% on the test set, respectively. Furthermore, three algorithms, including interval partial least squares (iPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS), were applied to extract characteristic spectral wavelengths associated with seed vigor. Among these, the CARS algorithm performed the best, identifying 38 characteristic wavelengths. Wavelength analysis indicated that rice seed vigor is primarily influenced by molecules such as starch, protein, moisture, and lipids. Using the characteristic wavelengths selected by the CARS algorithm, a PLS-DA prediction model for rice seed vigor was constructed, achieving high accuracy and germination rates of 90.47% and 95.38% on the test set, respectively. This study demonstrates that NIR-TS outperforms NIR-DRS in assessing rice seed vigor. Moreover, wavelength selection techniques can effectively identify characteristic spectral features related to seed vigor and significantly enhance the prediction accuracy of the model. Full article

Rhodiola species are traditionally used to mitigate hypoxia-related symptoms, but comparative evidence on their chemical bases and active constituents is limited. We implemented an integrated “qualitative analysis–pharmacological exploration–correlation analysis–molecular validation” workflow to compare Rhodiola crenulata, R. kirilowii, and R. rosea. Ultra-high-performance liquid chromatography–Q Exactive mass spectrometry (UPLC-QE-MS) profiling identified 175 metabolites across the three species, of which 161 were shared; multivariate analyses (principal component analysis, PCA; partial least squares–discriminant analysis, PLS-DA) revealed 30 differential compounds. In a normobaric hypoxia mouse model using herbal powder solutions, all three species significantly increased survival time versus control (p < 0.05), with mean survival times of 48.16 min (RR), 47.07 min (RC), and 44.82 min (RK) compared with 44.34 min for the positive control. Chemometric correlation (partial least squares regression, PLSR) combined with grey relational analysis (GRA) prioritized 14 compounds consistently associated with anti-hypoxia efficacy; six representative metabolites—epicatechin, 3-O-galloylquinic acid, salidroside, p-coumaric acid-4-O-glucoside, citric acid, and geraniol—were selected for in silico assessment. Molecular docking against hypoxia-inducible factor-1α (HIF-1α) yielded favorable binding poses (docking scores < −4.0), providing preliminary molecular-level plausibility without claiming mechanistic proof. This multi-level approach clarifies chemical–pharmacological relationships among Rhodiola species and provides prioritized candidate compounds for targeted isolation and mechanistic validation. Full article

Background: Early diagnosis of rheumatoid arthritis (RA) remains challenging due to the limited performance of existing serum biomarkers. This exploratory study aimed to identify novel serum metabolite and lipoprotein biomarkers for RA and to develop interpretable machine learning models for screening. Methods: This study employed ¹H-NMR metabolomics to analyze serum from 77 RA patients and 70 healthy controls, quantifying 38 endogenous metabolites and 112 lipoprotein parameters. Seven key biomarkers were identified using multiple criteria and Least Absolute Shrinkage and Selection Operator (LASSO) regression. The dataset was split into training and testing sets (7:3 ratio), and four machine learning models were constructed. The Random Forest (RF) model was further interpreted using the SHapley Additive exPlanations (SHAP) method. Results: The selected biomarkers, including formic acid and High-density lipoprotein 4 phospholipids (H4PL), showed significant associations with RA. In the internal test set, the RF model demonstrated promising discriminatory ability. Additionally, a proof-of-concept regression model for predicting the Disease Activity Score in 28 joints (DAS-28) score was developed, explaining a portion of its variance (R² = 0.548) in this cohort. Conclusions: This exploratory, single-center study identifies a novel panel of potential biomarkers for RA and provides a preliminary, interpretable predictive tool. The findings, particularly the internally validated high performance of certain markers, are hypothesis-generating and underscore the need for validation in larger, multi-center cohorts. The DAS-28 prediction model also warrants further investigation. Full article

Herein, we present a comprehensive single-cell investigation of the biochemical and metabolic responses of normal human colon fibroblasts (CCD-18Co) and colorectal adenocarcinoma cells (Caco-2) to supplementation with the amino acids leucine, threonine, and arginine, employing State-of-the-Art Raman spectroscopy and Raman imaging. This fully label-free and noninvasive methodology enabled high-spatial-resolution mapping of intracellular components, providing unprecedented insight into subcellular biochemical organization and metabolic remodeling associated with colorectal carcinogenesis. By synergistically integrating Raman spectroscopic data with advanced chemometric methods, we demonstrate robust, reproducible discrimination between normal and malignant colon cells, both in their native state and after amino acid treatment, based solely on their intrinsic vibrational fingerprints. Partial Least Squares Discriminant Analysis (PLS-DA) and one-way ANOVA revealed that perturbations in lipid metabolism and protein composition constitute key molecular determinants underlying the observed phenotypic divergence between control and amino acid–supplemented cells. Notably, detailed analysis of diagnostic Raman band intensity ratios (2845/3015, 2845/2930, 3015/2888, and 1444/1256) uncovered pronounced amino acid–driven alterations in metabolic pathways at the single-cell level. Raman imaging further enabled spatially resolved visualization of these biochemical shifts and changes in Raman band intensities, highlighting distinct lipid- and protein-rich subcellular domains that respond differentially to amino acid exposure in normal versus cancerous cells. Collectively, our findings establish Raman spectroscopy combined with chemometric analysis as a powerful and sensitive platform for decoding amino acid–induced metabolic reprogramming in colorectal cells. This approach deepens the mechanistic understanding of nutrient–cancer cell interactions and opens new avenues for the development of Raman-based strategies in cancer diagnostics and therapeutic response assessment. Full article

The transition period in dairy cows involves profound metabolic adaptations that challenge energy balance and liver function. This study evaluated the effects of rumen-protected methionine (RPM) and choline (RPC) on hepatic and skeletal muscle metabolism. Twenty-one multiparous Holstein cows from a 2 × 2 factorial design (CON, RPM, RPC) underwent liver and semitendinosus biopsies at −10, +7, and +20 d relative to parturition. Untargeted LC-MS metabolomics detected 2288 and 1454 molecular features in liver and muscle. Data were analyzed using mixed-model ANOVA (FDR ≤ 0.05), complemented by multivariate approaches including sparse PLS-DA and PERMANOVA to assess global metabolic variation. Metabolite annotation was performed using HMDB (±0.005 Da). Dietary supplementation significantly affected 105 hepatic metabolites, whereas time influenced 552 metabolites, generally reflecting increases or decreases in concentration from the prepartum to early postpartum periods. Network analysis identified nine hepatic co-expression modules associated with RPM and RPC. Hub metabolites included glucose-6-phosphate, mannose-6-phosphate, and sphingomyelins, indicating modulation of carbohydrate and lipid metabolism. In muscle, treatment effects were modest, with PERMANOVA and PLS-DA confirming limited discrimination among groups and a predominant temporal effect. Overall, RPM and, to a lesser extent, RPC modulated key hepatic metabolic pathways, supporting energy and redox homeostasis during early lactation. These findings highlight the potential of methyl-donor supplementation to enhance metabolic resilience at the tissue level in transition cows. Full article