Search Results (1,264)

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

The natural sunlight driven photocatalytic degradation of organic pollutants is a sustainable solution for water purification. The use of heterojunction nanocomposites in this process shows promise for improved photodegradation efficiency. In this work, nanocrystalline Zn₂SnO₄/SnO₂ obtained by the solid-state synthesis method was tested as a heterojunction photocatalyst material for the degradation of methylene blue (MB) and Rhodamine B (RhB) dyes as single and multicomponent systems in natural sunlight. Characterization of the structure and morphology of the synthesized nanocomposite using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) combined with energy dispersive X-ray spectroscopy (EDS), and photoluminescence (PL) spectroscopy confirmed the formation of Zn₂SnO₄/SnO₂ and heterojunctions between Zn₂SnO₄ and the SnO₂ nanoparticles. A photodegradation efficiency of 99.1% was achieved in 120 min with 50 mg of the photocatalyst for the degradation of MB and 70.6% for the degradation of RhB under the same conditions. In the multicomponent system, the degradation efficiency of 97.9% for MB and 53.2% for RhB was obtained with only 15 mg of the photocatalyst. The degradation of MB occurred through N-demethylation and the formation of azure intermediates and degradation of RhB occurred through sequential deethylation and fragmentation of the xanthene ring, both in single and multicomponent systems. Full article

Background: Fish escape events from aquaculture facilities are increasing and pose significant ecological, economic, and traceability concerns. Accurate methods to differentiate between wild, cultured, and escaped fish are essential for fishery management and seafood authentication. Methods: This study analyzed muscle tissue from Sparus aurata, Dicentrarchus labrax, and Argyrosomus regius using a multiomics approach. Heavy metals were quantified by ICP-MS, fatty acid profiles were assessed via GC-MS, and metabolomic and lipidomic signatures were identified using 1H NMR spectroscopy. Multivariate statistical models (MDS and PLS-LDA) were applied to classify fish origins. Results: Wild seabream showed significantly higher levels of arsenic (9.5-fold), selenium (3.5-fold), and DHA and ARA fatty acids (3.2-fold), while cultured fish exhibited increased linoleic and linolenic acids (6.5-fold). TMAO concentrations were up to 5.3-fold higher in wild fish, serving as a robust metabolic biomarker. Escaped fish displayed intermediate biochemical profiles. Multivariate models achieved a 100% classification accuracy across species and analytical techniques. Conclusions: The integration of heavy metal analysis, fatty acid profiling, and NMR-based metabolomics enables the accurate differentiation of fish origin. While muscle tissue provides reliable biomarkers relevant to human exposure, future studies should explore additional tissues such as liver and gills to improve the resolution of traceability. These methods support seafood authentication, enhance aquaculture traceability, and aid in managing the ecological impacts of escape events. Full article

White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as oxidative debris (OD)—via alkaline treatment of graphene oxide (GO) using KOH solutions ranging from 0.04 M to 1.78 M. The resulting OD, isolated from the supernatant after acid precipitation, exhibits strong and tunable photoluminescence (PL) across the visible spectrum. Emission peaks shift from blue (~440 nm) to green (~500 nm) and yellow (~565 nm) as a function of treatment conditions, with excitation wavelengths between 300 and 390 nm. Optical, morphological. and compositional analyses were performed using UV-Vis, AFM, FTIR, and Raman spectroscopy, confirming the presence of highly oxidized aromatic domains. The blue-emitting (S2) and green/yellow-emitting (R2) fractions were successfully separated and characterized, demonstrating potential color tuning by adjusting KOH concentration and treatment time. This study highlights the feasibility of reusing GO-derived byproducts as sustainable phosphor alternatives in wLEDs, reducing reliance on CRMs and aligning with green chemistry principles. Full article

As demand for plant-based beverages grows, analytical tools are needed to classify and understand their structural and compositional diversity. This study applied a multi-analytical approach to characterize 41 commercial almond-, oat-, rice- and soy-based beverages, evaluating attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, protein secondary structure proportions, colorimetry, and microscopic image texture analysis. A total of 26 variables, derived from ATR-FTIR and protein secondary structure assessment, were employed in multivariate models, using partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) to evaluate classification performance. The results indicated clear group separation, with soy and rice beverages forming distinct clusters while almond and oat samples showing partial overlap. Variable importance in projection (VIP) scores revealed that β-turn and α-helix protein structures, along with carbohydrate-associated spectral bands, were the key features for beverages’ classification. Textural features derived from microscopy images correlated with sugar and carbohydrate content and color parameters were also employed to describe beverages’ differences related to sugar content and visual appearance in terms of homogeneity. These findings demonstrate that combining ATR-FTIR spectral data with protein secondary structure data enables the effective classification of plant-based beverages, while microscopic image textural and color parameters offer additional extended product characterization. Full article

The quality and authenticity of honey are of crucial importance for food safety and consumer confidence. Given the increasing interest in enriched honey and potential fraud, rapid and non-destructive analytical methods for quality assessment, such as Near-Infrared Spectroscopy (NIRS), are needed. Therefore, the aim of this work was to investigate the applicability of NIR spectroscopy coupled with chemometric methods to assess the quality change in honey from three different countries, after addition of five different aromatic plants (lavender, rosemary, oregano, sage, and white pine oil) in three different concentrations (0.5%, 0.8% and 1%). Measurements of basic physicochemical properties, color, antioxidant activity, and NIR spectra were performed for all samples (pure honey and honey with added aromatic plants). Chemometric models, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, were applied to analyze spectral data, correlate spectra with physicochemical properties, color and antioxidant activity measurements, and develop classification and prediction models. Spectral changes in the NIR region, as expected, showed the ability to distinguish samples depending on the type and concentration of added aromatic plants. Chemometric models enabled efficient discrimination between pure and enriched honey samples, as well as assessment of the influence of different additives on antioxidant activity and color. The results highlight the potential of NIRS as a rapid, non-destructive and environmentally friendly method for quality monitoring and detection of specific additives in honey, offering technical support for quality control and food safety regulation. Full article

Sheep and goat milk authenticity is of great importance, especially for countries like Greece, where these products are connected to the country’s rural economy and cultural heritage. The aim of the study is to evaluate the effectiveness of Fourier Transform Infrared Attenuated Total Reflectance (ATR-FTIR) spectroscopy in combination with chemometric techniques for the classification of cow, sheep, and goat milk and consequently support fraud identification. A total of 178 cow, sheep and goat milk samples were collected from livestock farms in Thessaly, Greece. Sheep and goat milk samples were confirmed as authentic by applying a validated Enzyme Linked Immunosorbent Assay (ELISA), while all samples were analyzed using ATR-FTIR spectroscopy in both raw and freeze-dried form. Freeze-dried samples exhibited clearer spectral characteristics, particularly enhancing the signals from triglycerides, proteins, and carbohydrates. Partial Least Squares Discriminant Analysis (PLS-DA) delivered robust discrimination. By using the spectral range between 600 and 1800 cm⁻¹, 100% correct classification of all milk types was achieved. These findings highlight the potential of FTIR spectroscopy as a fast, non-destructive, and cost-effective tool for milk identification and species differentiation. This method is particularly suitable for industrial and regulatory applications, offering high efficiency. Full article

Ag/AgCl-decorated layered lanthanum oxide (La₂O₃) and niobium pentoxide (Nb₂O₅) plasmonic photocatalysts are fabricated through an ionic liquid-mediated co-precipitation method. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), powder X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) techniques were used to illustrate the physicochemical properties of the materials. The photoactivity was evaluated for the degradation of Acid Blue 92 (AB92) azo dye, a typical organic contaminant from the textile industry, and U251 cancer cell inactivation. According to the results, Nb₂O₅–Ag/AgCl was able to remove >99% of AB92 solution in 35 min with the rate constant of 0.12 min⁻¹, 2.4 times higher than that of La₂O₃–Ag/AgCl. A pH of 3 and a catalyst dosage of 0.02 g were determined as the optimized factors to reach the highest degradation efficiency under solar energy at noon, which was opted to have the highest sunlight intensity over the reactor. Also, 0.02 mg/mL of Nb₂O₅–Ag/AgCl was determined to be of great potential to reduce cancer cell viability by more than 50%, revealed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and mitochondrial membrane potential (MMP) examinations. The mechanism of degradation was also discussed, considering the key role of Ag⁰ nanoparticles in inducing a plasmonic effect and improving the charge separation. This work provides helpful insights to opt for an efficient rare metal oxide with good biocompatibility as support for the plasmonic photocatalysts with the goal of environmental purification under sunlight. Full article

Accurate assessment of biomass fuel properties is essential for quality control and fair market pricing, particularly when dealing with variable moisture content (MC) in agricultural residues. This study investigates the use of near-infrared (NIR) spectroscopy to predict the lower heating value (LHV) and ash content of sugarcane leaf pellets while minimizing the interference caused by moisture variability. Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. The optimal model for LHV prediction was developed using GA-PLS regression (Method II), provided a coefficient of determination (R²) of 0.80, a root mean square error of calibration (RMSEc) of 595.80 J/g, and a ratio of performance to deviation (RPD) of 1.74, indicating fair predictive performance. The ash content model showed moderate accuracy, with a maximum R² of 0.61 and an RPD of 1.40. These findings suggest that the variables selected via GA in Method II were not relevant to MC; as Method II provided the best result, this indicates a low impact of MC, which may influence model construction in the future. Moreover, the findings also highlight the potential of NIR spectroscopy, combined with appropriate spectral preprocessing and wavelength optimization, as a rapid, non-destructive tool for evaluating biomass quality, enabling more precise control in bioenergy production and biomass trading. Full article

Ionic liquids (ILs) can ideally reduce defects and improve the film stability of emissive metal halide perovskite films. In this work, we measure how the structure and emission of methylammonium lead tribromide (MAPbBr₃) perovskite films is modulated by long alkyl chain-containing pyridinium, imidazolium, or pyrrolidinium ILs. Two different film deposition methods are compared, with the resultant films characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. For the latter, the differences in PL intensity of the perovskite are quantified using photoluminescence quantum efficiency (PLQE) measurements. It is found that a spin coating method in conjunction with the use of an imidazolium-containing IL (for a given precursor concentration) produces the strongest emissive perovskite. This optimal enhancement is attributed to a function of accessible surface charges associated with the heterocyclic cation of a given IL and perovskite defect passivation by bromide, the latter elucidated with the help of density functional theory. Proof-of-concept device fabrication is demonstrated for the case of a light emitting diode (LED) with the IL present in the emissive perovskite layer. Full article

In this study, a chemometrics-assisted calibration method was developed for the Z-903 SciAps handheld Laser-Induced Breakdown Spectroscopy (h-LIBS) device. For this purpose, seventeen silica-based standard samples with known chemical composition were collected, pelleted, and analyzed using h-LIBS. Spectral data were pre-processed using a Whittaker filter and normalized via Standard Normal Variate (SNV). The dataset was divided into calibration and validation sets using the Kennard–Stone algorithm. Partial Least Square (PLS) regression was employed for multivariate regression analysis, and a variable selection method (i.e., Variable Importance in Projection, VIP) was applied to reduce the number of predictors. Results from the PLS-VIP approach demonstrated that this device is suitable for the quantitative measurement of nineteen chemical elements, including major and minor elements, achieving significant R² values for major elements including Na (R² = 0.91), Mg (R² = 0.95), and Si (R² = 0.89). The limits of detection reached are satisfying, being, for example, 0.24%, 0.41%, 0.43%, 1.5%, and 1.7% for Na, Al, Ca, Si, and Fe, respectively, among major elements, and 189 ppm, 165 ppm, 203 ppm, and 1 ppm for Ba, Cu, Mn, and Rb, respectively, among minor elements. Uncertainties in prediction of the element concentrations were compared with data from the literature, and the effect of another baseline pretreatment algorithm, airPLS (adaptive iteratively reweighted PLS), was also tested. The method was then applied to nine silica-based artifacts of different typologies sampled from the Archaeological Park of Tindari (Italy), including bricks from the theatre, archaeological glasses, and volcanic rocks. Full article

Background: Respiratory pathologies, such as COVID-19 and bronchitis, pose significant challenges for high-level athletes, particularly during demanding altitude training camps. Metabolomics offers a promising approach for early detection of such pathologies, potentially minimizing their impact on performance. This study investigates the metabolic differences between athletes with and without respiratory illnesses during an altitude training camp using urine samples and multivariate analysis. Methods: Twenty-seven elite rowers (15 males, 12 females) participated in a 12-day altitude training camp at 1850 m. Urine samples were collected daily, with nine athletes developing respiratory pathologies (8 COVID-19, 1 bronchitis). Nuclear Magnetic Resonance spectroscopy was used to analyze the samples, followed by data processing with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), allowing to use Variable Importance in Projection (VIP) scores to identify key metabolites contributing to group separation. Results: The PLS-DA model for respiratory illness showed good performance (R² = 0.89, Q² = 0.35, p < 0.05). Models for altitude training achieved higher predictive power (Q² = 0.51 and 0.72, respectively). Metabolites kynurenine, N-methylnicotinamide, pyroglutamate, propionate, N-formyltryptophan, tryptophan and glucose were significantly highlighted in case of respiratory illness while trigonelline, 3-hydroxyphenylacetate, glutamate, creatine, citrate, urea, o-hydroxyhippurate, creatinine, hippurate and alanine were correlated to effort in altitude. This distinction confirms that respiratory illness induces a unique metabolic profile, clearly separable from hypoxia and training-induced adaptations. Conclusions: This study highlights the utility of metabolomics in identifying biomarkers of respiratory pathologies in athletes during altitude training, offering the potential for improved monitoring and intervention strategies. These findings could enhance athlete health management, reducing the impact of illness on performance during critical training periods. Further research with larger cohorts is warranted to confirm these results and explore targeted interventions. Full article

In 2023, the Italian Bracigliano sweet cherries were awarded the important European label PGI. However, reliable information on the compositional and nutraceutical quality of this product is still relatively undefined and fragmented. Therefore, we investigated fresh Bracigliano PGI cherries (Pallaccia, Spernocchia, and Principe varieties) via HRMAS NMR spectroscopy in the semi-solid state, even though it represents an innovative and powerful technique that is still drastically unexplored. We demonstrated the HRMAS NMR suitability for this fruit type as well as identified the primary metabolome of studied Bracigliano PGI types. Moreover, chemometric techniques (ANOVA, PCA, and PLS-DA) permitted the significant definition of a variety-specific compositional fingerprint. HRMAS data were associated with the assessment of chemical and nutraceutical quality parameters. Importantly, in all studied varieties, a relatively high content of total phenols and antioxidant agents was detected, with Pallaccia cherries resulting as the healthiest ones. The heatmap clusterization revealed interesting correlations between HRMAS-NMR data and important quality parameters. Our results confirm the role of HRMAS in food chemistry and invite the creation of a spectral database of Bracigliano sweet cherries, useful to conduct traceability studies, protect consumers from frauds, and bolster the producers in promoting and certifying the quality of their products. Full article