Search Results (249)

Background/Objectives: Dietary fibers from cereals promote the production of short-chain fatty acids (SCFA), which have been linked to improved glucose and lipid metabolism, reduced inflammation, and decreased oxidative stress. Wheat aleurone, a bran fraction enriched in fermentable fibers and bioactive compounds, may enhance SCFA production, but clinical evidence remains limited. This study investigated whether a wheat aleurone-rich diet, compared with a refined wheat diet, modulates circulating SCFA concentrations and their relationship with oxidative stress in individuals at elevated cardio-metabolic risk. Methods: In a randomized, cross-over trial, 23 adults with abdominal obesity and at least one additional metabolic syndrome feature consumed isoenergetic diets enriched with wheat aleurone or refined wheat for 8 weeks. Fasting and postprandial serum SCFA concentrations were measured over 3 h following standardized test meals using the gas chromatography method. Urinary 8-isoprostane excretion was assessed as a biomarker of oxidative stress using the ELISA method. SCFA values are reported as changes (increase/decrease) from fasting values, calculated by subtracting the fasting value from that of each time point. Results: Compared with refined wheat, the wheat aleurone diet significantly increased postprandial butyrate response (p = 0.005, time × meal interaction), with higher values observed at 150 min (p = 0.027) and 180 min (p = 0.001). The mean change in postprandial butyrate was also greater after the wheat aleurone diet (+0.95 ± 1.92 vs. −0.32 ± 2.01 µmol/L; p = 0.040). Importantly, butyrate at 180 min was inversely correlated with urinary 8-isoprostane (r = −0.618, p = 0.019). No significant differences were found for acetate or propionate. Conclusions: A wheat aleurone-rich diet enhances postprandial butyrate production and is associated with lower oxidative stress, suggesting a role of butyrate in mediating the antioxidant benefits of wheat aleurone in individuals with cardio-metabolic risk. This study is registered under ClinicalTrials.gov Identifier no. NCT02150356. Full article

Lavandin essential oil (LEO) (Lavandula × intermedia) is a high-yielding aromatic product with broad bioactive potential, but its direct application is hindered by its volatility, rapid oxidation, and environmental sensitivity. In this study, the microencapsulation of LEO by spray drying using different wall materials was investigated: Maltodextrin (MD), Gum Arabic (GA), Whey Protein Concentrate (WPC), Inulin (IN), and Modified Starch (Hi-Cap). The resulting formulations exhibited encapsulation efficiencies (EEs) of 55.35–83.29%, oil retention (RE) of 49.07–76.65%, and yields of 41.39–71.47%. The MD/GA blend with Tween 80 performed best, as it offered high EE and RE, low residual moisture, fast reconstitution, and strong protection of the encapsulated oil against thermal and moisture stress. Gas chromatography–mass spectrometry (GC–MS) identified 38 volatile components, with linalyl acetate (30.38%) and linalool (24.65%) being the major components. Biological tests confirmed that the antimicrobial and antifungal activity of lavandin against some pathogens was maintained even when a much lower concentration of the oil (1–5%) was used in encapsulated form. Antioxidant activity decreased after encapsulation, while tyrosinase inhibition increased, indicating cosmetic potential. These results show that spray drying is an effective strategy for stabilizing LEO and expanding its applications in various industries. Full article

The thermal processing of walnut shells was investigated through pyrolysis within the range of 100–650 °C, highlighting the influence of thermal engineering parameters on biomass conversion. The resulting biochar was subjected to chemical activation with phosphoric acid, and its physicochemical properties were evaluated to determine how thermal processing enhances its performance as a cathode material for lithium–sulfur (Li–S) batteries. This approach underscores the role of thermal engineering in bridging biomass valorization with energy storage technologies. In parallel, the gaseous fraction generated during walnut shell fast pyrolysis was collected, and for the first time, volatile organic compounds (VOCs) under atmospheric conditions were identified using solid-phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC–MS). The composition of the VOCs was characterized, quantifying aromatic compounds, hydrocarbons, furans, and oxygenated species. This study further linked the thermal decomposition pathways of these compounds to their atmospheric implications by estimating tropospheric lifetimes and evaluating their potential contributions to air quality degradation at the local, regional, and global scales. Full article

Background: Excessive alcohol consumption constitutes a serious cause of death worldwide. Fatty acid ethyl esters, as metabolites of the non-oxidative elimination pathway of ethanol, have been recognized as mediators of alcohol-induced organ damage. These metabolites serve as potential biomarkers for the assessment of ethanol intake and might be also used in post-mortem studies. Methods: In this study, the development and optimization of a simple, fast, precise, accurate, and cost-effective method with the use of gas chromatography coupled with tandem mass spectrometry for quantitative analysis of six fatty acid ethyl esters, namely ethyl laurate, myristate, palmitate, linoleate, oleate, and stearate, were conducted. Results: The optimized method was fully validated according to ICH guidelines. Additionally, identification of critical method parameters was possible by using the quality by design approach. By carrying out analyses according to the Plackett–Burman plan (design of experiments methodology), the robustness of the analytical method developed was confirmed for four (ethyl palmitate, linoleate, oleate, and stearate) ethyl esters. In the case of ethyl myristate, the variable significantly affecting the results appeared to be the temperature of solvent evaporation after the deproteinization step. Conclusions: Biochemical interpretation of the obtained results with available medical records suggests that plasma concentrations of selected fatty acid ethyl esters are valuable indicators of pre-mortem alcohol consumption and may be one of the key factors helpful in determining the cause and mechanism of death. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection. Patients and methods: Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles. Results: GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%). Conclusions: Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis. Full article

Background/Objectives: This study explores the bioactive potential of Bovistella utriformis biomass and its polysaccharides (PsBu) through comprehensive biochemical and bioactivity analyses, focusing on their antioxidant, cytotoxic, and antihyperglycemic properties. Methods: Elemental analysis determined the biomass’s chemical composition. Antioxidant activity was assessed using ABTS and DPPH assays. Monosaccharide composition was analyzed via gas chromatography-mass spectrometry (GC-MS). In vitro cytotoxicity assays were conducted on cancer and normal cell lines to determine IC₅₀ values and selectivity indices (SI). Zebrafish embryo toxicity was evaluated for teratogenic effects, and an in vivo antihyperglycemic study was performed in diabetic rat models. Results: The biomass exhibited high carbon content (around 41%) and nitrogen levels, with a balanced C/N ratio nearing 5. Protein content exceeded 50%, alongside significant carbohydrate, fiber, and ash levels. Antioxidant assays revealed inhibition rates of approximately 89% (ABTS) and 64% (DPPH). GC-MS analysis identified glucose as the predominant sugar (>80%), followed by galactose and mannose. Additionally, HPLC detected a photoprotective compound, potentially a mycosporin-like amino acid. Cytotoxicity assays demonstrated PsBu’s selective activity against colon, lung, and melanoma cancer cell lines (IC₅₀: 100–500 µg·mL⁻¹), while effects on normal cell lines were lower (IC₅₀ > 1300 µg·mL⁻¹ for HaCaT, >2500 µg·mL⁻¹ for HGF-1), with SI values approaching 27, supporting PsBu’s potential as a targeted anticancer agent. Zebrafish embryo assays yielded LC₅₀ values ranging from 1.4 to 1.8 mg·mL⁻¹. In vivo, PsBu reduced fasting blood glucose levels in hyperglycemic rats (approximately 210 mg·dL⁻¹ vs. 230 mg·dL⁻¹ in controls) and preserved pancreatic β-cell integrity (around 80% vs. 65% in controls). Conclusions: These findings suggest that B. utriformis biomass and PsBu exhibit strong antioxidant activity, selective cytotoxicity against cancer cells, and antihyperglycemic potential, making them promising candidates for further biomedical applications. Full article

A simple, fast, green, and sensitive method for determining Bisphenol A (BPA) levels in commercial milk was developed using a solventless sample preparation technique known as stir bar sorptive extraction, coupled with thermal desorption–gas chromatography/mass spectrometry. BPA was selected due to its ubiquitous presence in the environment and its classification as an endocrine-disrupting chemical of concern (i.e., its ability to mimic hormone functions). Studies have reported that BPA can leach into various food sources, including milk, a dietary staple for infants. It is critical to have an effective and efficient process for monitoring the presence of BPA in milk to protect children’s health. Current detection methods for BPA in milk are lengthy and tedious and tend to require the use of organic solvents for the extraction of BPA. This optimized “green” method provides an effective alternative for BPA detection in a challenging matrix, e.g., milk. Factors such as pH (1.5, 6, and 13), temperature (70–80 °C), and sonication (1 h, 2 h, and 3 h) were studied with a BPA-spiked whole milk sample (final concentration of 8 ppb) to optimize the extraction efficiency without the use of solvents. The developed methodology improves BPA recovery from whole milk by over 50%, with a detection limit in the parts per trillion range (45 ng/L). The sample preparation developed in this report rendered a more sensitive option for analyzing BPA in milk, with a limit of detection in the parts per trillion range (compared to low ppb) even though the recovery performance is not as good as in reported studies (54% vs. >85%); nonetheless, it provides a green alternative for future studies assessing BPA exposure through dairy products. Full article

A comprehensive understanding of the compositions and physicochemical properties of coal-based liquids is conducive to the rapid development of multipurpose, high-performance, and high-value functional chemicals. However, because of their complex compositions, coal-based liquids generate two-dimensional gas chromatography (GC × GC) chromatograms that are very complex and very time consuming to analyze. Therefore, the development of a method for accurately and rapidly analyzing chromatograms is crucial for understanding the chemical compositions and structures of coal-based liquids, such as direct coal liquefaction (DCL) oils and coal tar. In this study, DCL oils were distilled and qualitatively analyzed using GC × GC chromatograms. A deep-learning (DL) model was used to identify spectral features in GC × GC chromatograms and predominantly categorize the corresponding DCL oils as aliphatic alkanes, cycloalkanes, mono-, bi-, tri-, and tetracyclic aromatics. Regional labels associated with areas in the GC × GC chromatograms were fed into the mask-region-based convolutional neural network’s (Mask R-CNN’s) algorithm. The Mask R-CNN accurately and rapidly segmented the GC × GC chromatograms into regions representing different compounds, thereby automatically qualitatively classifying the compounds according to their spots in the chromatograms. Results show that the Mask R-CNN model’s accuracy, precision, recall, F1 value, and Intersection over Union (IoU) value were 93.71%, 96.99%, 96.27%, 0.95, and 0.93, respectively. DL is effective for visually comparing GC × GC chromatograms to analyze the compositions of chemical mixtures, accelerating GC × GC chromatogram interpretation and compound characterization and facilitating comparisons of the chemical compositions of multiple coal-based liquids produced in the coal and petroleum industry. Applying DL to analyze chromatograms improves analysis efficiency and provides a new method for analyzing GC × GC chromatograms, which is important for fast and accurate analysis. Full article

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R² values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R² values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry. Full article

Background: The high global prevalence of prediabetes requires its early identification. Amino acids (AAs) have emerged as potential predictors of prediabetes. This study investigates the association between amino acids and prediabetes in the Kazakh population. Materials and Methods: In this case-control study, serum AAs levels were measured using the Trace GC 1310 gas chromatography system coupled with the TSQ 8000 triple quadrupole mass spectrometer (Thermo Scientific, Austin, TX, USA) followed by silylation with the BSTFA + 1% TMCS derivatization method. Biochemical parameters, including total cholesterol, HDL-C, LDL-C, triglycerides, fasting glucose, HbA1c, and Creatinine, were assessed for each participant. Trained professionals conducted anthropometric and physical examinations (which included taking blood pressure and heart rate measurements) and family history collection. Results: A total of 112 Kazakh individuals with prediabetes and 55 without prediabetes, aged 36–65 years, were included in the study. Only Alanine and valine showed a significant association with prediabetes risk among the 13 AAs analyzed. Our findings revealed an inverse relationship between Alanine and Valine and prediabetes in individuals of Kazakh ethnicity. Conclusion: A lower serum level of Alanine and Valine may serve as a predictive biomarker for prediabetes in the Kazakh population. Full article

Resveratrol (trans-3,5,4′-trihydroxystilbene, RES) is one of the most well-known natural products with numerous health benefits. To explore the nutraceutical potentials of some dietary RES derivatives including isorhapontigenin (trans-3,5,4′-trihydroxy-3′-methoxystilbene, ISO), oxyresveratrol (trans-3,5,2′,4′-tetrahydroxystilbene, OXY) and pterostilbene (trans-3,5-dimethoxy-4′-hydroxystilbene, PTS), their impacts on metabolism and health were assessed in Sprague Dawley rats after a two-week daily oral administration at the dose of 100 µmol/kg/day. Non-targeted metabolomic analyses were carried out with the liver, heart, brain and plasma samples using gas chromatography–tandem mass spectrometry (GC-MS/MS). Notable in vivo health benefits were observed, as the rats received ISO, PTS or RES showed less body weight gain; the rats received OXY or RES displayed healthier fasting blood glucose levels; while all of the tested stilbenes exhibited cholesterol-lowering effects. Additionally, many important metabolic pathways such as glycolysis, pentose phosphate pathway, tricarboxylic acid cycle and fatty acid oxidation were found to be modulated by the tested stilbenes. Besides the reaffirmation of the well-known beneficial effects of RES in diabetes, obesity, cardiovascular disease and Alzheimer’s disease, the metabolomic analyses also suggest the anti-diabetic, cardio-, hepato- and neuro-protective activities of ISO; the anti-diabetic, cardio-, hepato- and neuro-protective effects of OXY; and the anti-aging, anti-inflammatory, cardio-, hepato- and neuro-protective potential of PTS. Interestingly, although these stilbenes share a similar structure, their biological activities appear to be distinct. In conclusion, similarly to RES, ISO, OXY and PTS have emerged as promising candidates for further nutraceutical development. Full article

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively. Full article

In food authentication, it is important to compare different analytical procedures and select the best method. The aim of this study was to determine the fingerprints of Zweigelt and Rondo wines through headspace analysis using ultra-fast gas chromatography (ultra-fast GC) and to compare the effectiveness of this approach at classifying wines based on grape variety and type of malolactic fermentation (MLF) as well as its greenness and practicality with three other chromatographic methods such as headspace solid-phase microextraction/gas chromatography-mass spectrometry with carboxen-polydimethylosiloxane fiber (SPME/GC-MS with CAR/PDMS fiber), headspace solid-phase microextraction/gas chromatography-mass spectrometry with polyacrylate fiber (SPME/GC-MS with PA fiber), and ultra performance liquid chromatography–photodiode array detector-tandem mass spectrometry (UPLC-PDA-MS/MS). Principal Component Analysis (PCA) revealed that fingerprints obtained using all four chromatographic methods were suitable for classification using machine learning (ML). Random Forest (RF) and Support Vector Machines (SVM) yielded accuracies of at least 99% in the varietal classification of Zweigelt and Rondo wines and therefore proved suitable for robust fingerprinting-based Quality Assurance/Quality Control (QA/QC) procedures. In the case of wine classification by the type of MLF, the classifiers performed slightly worse, with the poorest accuracy of 91% for SVM and SPME/GC-MS with CAR/PDMS fiber, and no less than 93% for the other methods. Ultra-fast GC is the greenest and UPLC-PDA-MS/MS is the most practical of the four chromatographic methods. Full article

Shrimp shells are a key source of chitin, commonly extracted through chemical methods, which may cause minor molecular damage. Nowadays, there is great interest in achieving close to zero protein content in crude chitin in order to use it for high-end markets. Therefore, this study optimized the enzymatic deproteination using two commercial proteases (SEB Pro FL100 and Sea-B Zyme L200) for effective and fast removal of residual protein from Northern shrimp (Pandalus borealis) shell chitin for the first time. The protein content was determined using both the Kjeldahl method and amino acid analysis using gas chromatography–mass spectrometry (GC-MS). The performance of papain (Sea B Zyme L200) was superior to fungal protease (SEB Pro FL100) for this application, and it achieved residual protein content of 2.01%, while the calculated optimum for the latter enzyme was 6.18%. A model was developed using 2⁴ factorial design, and it was predicted that the lowest residual protein content using fungal protease and papain could be achieved at the following conditions: a pH of 4.2 and 7, and an enzyme concentration of 4 and 1.5%, respectively. Thus, the low-protein content obtained using enzymatic deproteination could be an alternative approach to the traditional methods, indicating their potential to produce premium-quality chitin. Full article

Lately, pyrolysis has attracted significant attention due to its substantial potential for bio-oil production, with the ability to serve as a renewable energy source and/or facilitate the production of valuable chemical compounds. The chemical compounds generated and their amounts are completely influenced by the traits and chemical makeup of the initial biomass. In this work, the catalytic fast pyrolysis of Eucalyptus urograndis canopy was carried out using a pyrolyzer coupled to gas chromatography/mass spectrometry (Py-GC/MS) at different temperatures and in the presence and absence of catalysts. Elemental composition analysis was employed to characterize the chemical composition of the biomass. The results showed a biomass with a carbon percentage of 50.20%, oxygen of 43.21%, and hydrogen of 6.34%, as well as a lower calorific power of 17.51 MJ/kg. The Py-GC/MS analyses revealed the presence of several noteworthy compounds, including acetic acid (C₂H₄O₂) and, in smaller quantities, hydrogen (H₂), furfural (C₅H₄O₂), and levoglucosan (C₆H₁₀O₅). The technical-economic evaluation revealed that the production of acetic acid, furfural, hydrogen, and levoglucosan commands a high market price. Additionally, a single production cycle is anticipated to yield a favorable technical-economic balance, generating approximately USD 466.10 /ton of processed biomass. This outcome is achieved through the process of catalytic fast pyrolysis, where CuO has been identified as the most suitable catalyst. Full article