Search Results (438)

Background: Metabolite identification and annotation remain major bottlenecks in untargeted metabolomics because mass spectral features often lack sufficient specificity. High-confidence annotation requires experimental validation using authentic standards analyzed under matched chromatographic and ionization conditions, providing greater reliability than in silico predictions or database matching alone. This study aimed to develop a practical and scalable workflow for constructing a high-quality mass spectral library using a commercially available analytical standards kit. Methods: A total of 603 metabolites from the MSMLS kit were organized into 42 mixtures, each containing approximately 15 compounds. Mixture design was based on molecular mass and distribution coefficient values, specifically logD at pH 3.1, with a minimum logD spacing of 0.15 to improve chromatographic separation and reduce co-elution. This strategy was used to minimize the total number of injections while maintaining spectral quality. The resulting spectra were evaluated against online spectral resources and in silico fragmentation predictions. A preliminary proof-of-concept analysis was also performed using human serum samples. Results: Using this workflow, 471 metabolites, corresponding to approximately 78% of the standards, were successfully detected and incorporated into the spectral library. Comparison with online resources and in silico fragmentation predictions demonstrated improved spectral quality and reliability. The proof-of-concept serum analysis enabled identification of endogenous metabolites using the constructed library. In addition, the robustness and applicability of the workflow were further supported by a method validation study using metabolites derived from this library. Conclusions: This workflow provides a scalable strategy for constructing mass spectral libraries that balances spectral quality with analytical throughput. By using rational mixture design and authentic standards analyzed under matched experimental conditions, the approach enables substantial metabolite coverage while maintaining data reliability and minimizing experimental effort. Full article

Accurate measurement and control of impurities are critical for ensuring the quality and therapeutic performance of solid-state pharmaceutical formulations. This study introduces a rapid, minimal sample preparation analytical approach for quantifying low-level dalmelitinib impurities in dalmelitinib mesylate, employing near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). To mimic actual manufacturing conditions, a mixture system was designed comprising dalmelitinib mesylate, dalmelitinib impurity, and formulation excipients. Various spectral preprocessing strategies were systematically evaluated, including Savitzky–Golay first derivative (SG1st), Savitzky–Golay second derivative (SG2nd), multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet denoising, wavelet compression, and their combinations. The optimal model was obtained using SG1st combined with wavelet denoising. The resulting PLSR model (7 latent variables) showed good predictive performance, with an R² of 0.99569 and an RMSECV of 0.00315. The limit of detection (LOD) and limit of quantification (LOQ) were 0.234% and 0.708%, respectively, demonstrating applicability for monitoring low-level impurities in pharmaceutical formulations. Method validation demonstrated satisfactory precision (RSD < 3%), accuracy (100.77–102.01%), and stability over 24 h (RSD ≤ 4.75%). Compared with conventional solid-state analytical techniques, the proposed NIR–PLSR framework enables rapid, non-destructive analysis with minimal sample preparation. The combination of derivative preprocessing and wavelet denoising improved extraction of impurity-related spectral information in complex pharmaceutical systems, highlighting the potential of this approach for process analytical technology (PAT) and pharmaceutical quality monitoring. Full article

The increasing prevalence of drug-resistant microorganisms has prompted research into novel antimicrobial compounds, with 2-thiophene carboxylic acid thiourea derivatives showing promise for future therapeutic applications. However, the poor water solubility of these compounds limits their practical use. This study investigates the formation and characterization of inclusion complexes between 2-hydroxypropyl-β-cyclodextrin (HPβCD) and 2-thiophene carboxylic acid-halogenated (chlorine-, bromine-, and iodine-) thiourea derivatives, seeking to improve their physicochemical properties. The dynamic light scattering (DLS) measurements and UV-Vis spectroscopy provided information related to thiourea–HPβCD aggregates and stoichiometry. Solid-state inclusion compounds and physical mixtures were prepared in two different molar ratios (thioureas:HPβCD = 1:1 and 1:2), and the morphology of the resulting powders was observed by scanning electron microscopy (SEM). Thermogravimetry (TG) and differential scanning calorimetry (DSC) (TG-DSC) coupled analysis were used to analyze thermal profiles in the temperature range of 25 °C to 600 °C, while the spectral data obtained by Fourier transform infrared spectroscopy (FTIR) provided the characteristic vibrational bands of the pure guest molecules and data corresponding to the structural and chemical changes in the host–guest systems. The structural and thermal analyses revealed significant interactions between the host and thioureas molecules, with evidence of possible interactions involving two cyclodextrin molecules. The results demonstrate the presence of intermediate stoichiometry in the inclusion compounds, with possible enhancement of the therapeutic potential of these thiourea derivatives. Full article

Stellar irradiation is thought to be a significant contributor to the origin of life. Ultraviolet (UV) light interacting with iron cyanide complexes may play an important role in prebiotic chemistry. The UV–Visible (UV–Vis) spectra of these iron cyanide complexes can be measured by the same source that drives the chemistry, providing a real-time in situ quantitative analysis of prebiotically relevant, UV-driven photochemistry. We measure the UV–Vis absorbances of ferrocyanide and nitroprusside, and relate these absorbances to known concentrations. We show that these absorbances can be combined to accurately predict the concentrations of ferrocyanide–nitroprusside mixtures that could be generated from ferrocyanide and nitroxyl salts irradiated by ultraviolet light. The ferrocyanide molar attenuation coefficients were found to be maximal at the following: ${\epsilon }_{ferrocyanide}\left(340\mathrm{nm}\right)=(2.2\pm 0.4)\times {10}^3{\mathrm{dm}}^2{\mathrm{mol}}^{-1}$. Nitroprusside peaks show the following values: ${\epsilon }_{nitroprusside}\left(340\mathrm{nm}\right)=(4.1\pm 0.3)\times {10}^2{\mathrm{dm}}^2{\mathrm{mol}}^{-1}$, ${\epsilon }_{nitroprusside}\left(400\mathrm{nm}\right)=(1.71\pm 0.05)\times {10}^2{\mathrm{dm}}^2{\mathrm{mol}}^{-1}$, and ${\epsilon }_{nitroprusside}\left(500\mathrm{nm}\right)=62.1\pm 1.7{\mathrm{dm}}^2{\mathrm{mol}}^{-1}$. With the help of our measured absorbances, we consider ferrocyanide and nitroprusside to function as sunscreens. In the absence of continuous ferrocyanide sources, UV-sensitive compounds could be protected on timescales of months. This would allow for compounds like nicotinamide adenine dinucleotide, NADH, to survive for over a year at depths of 5 m, compared to a lifetime of 6 months when unprotected. Our toy model constrains the photochemical survival of compounds of interest to the origin of life community across a comprehensive spectral range and can be used to constrain the survival using different exoplanetary irradiative conditions; thus, we are able to explore the UV environment with the presence of ferrocyanide and nitroprusside and contribute to the wider discussion surrounding the prevalence of the origin of life in the Universe. Full article

This study evaluates the performance of hyperspectral imaging (HSI) as a non-contact method for assessing railroad ballast fouling. A severely degraded ballast sample was collected from a derailment site. Conventional fouling indices were measured, indicating extreme ballast deterioration and fouling. To establish a quantitative baseline for degradation severity, hyperspectral reflectance data in the Visible–Near Infrared (VNIR) and Near Infrared (NIR) ranges were acquired for field samples under fouled-wet (as-received), fouled-dry (oven-dried), and clean-dry (oven-dried and sieved) conditions. Field spectra were compared with laboratory-fabricated ballast mixtures containing clay and coal fouling agents to ensure the results were not skewed due to the sampling procedure. Spectral similarity analysis using the Spectral Angle Mapper (SAM) was employed to quantify differences across ballast conditions. The maximum SAM angle reached approximately 0.45 radians between the as-received and clean-dry states in the NIR range, reflecting the combined effects of fouling and moisture. Comparisons between field and laboratory-fabricated samples showed moderate similarity, with SAM angles below 0.30 radians, indicating general agreement between field and laboratory spectra while capturing differences related to fouling agents, moisture retention, and compositional variability. Full article

To combat poultry meat adulteration, we developed a poultry universal primer-based fluorescent PCR (PUP-fPCR). Through comprehensive genomic alignment analysis, a poultry-specific nuclear DNA sequence containing phylogenetically conserved regions and hypervariable segments with interspecies nucleotide polymorphisms was employed to develop universal primers targeting conserved flanking sequences and TaqMan probes for hypervariable segments. Then, a multiplex quantitative PCR method incorporating universal primers with four TaqMan probes was developed with high specificity and sensitivity (limit of detection: 0.005 ng). Analytical performance evaluation using prepared DNA mixtures revealed robust accuracy (relative deviation: 0.80–5.05%) and precision (relative standard deviation: 0.94–13.84%). This single-tube multiplex system leverages the spectral discrimination of TaqMan probes to simultaneously detect four poultry species, overcoming primer competition issues inherent in conventional multiplex PCR designs. This integrated approach reduces system complexity while maintaining detection efficiency, providing regulatory agencies with a robust tool for combating meat adulteration and ensuring food quality supervision. Full article

The state of São Paulo is home to remnants of the Atlantic Forest and Cerrado biomes, both of which face intense anthropogenic pressure and hight fragmentation. In this context, Environmental Recovery Commitment Agreements (TCRAs) serve as essential instruments for restoring degraded areas and monitoring vegetation recovery over time. This study assesses land use and land cover (LULC) classification performance in TCRA sites by integrating Satellite Image Time Series (SITS) with spectral fractions derived from the Linear Spectral Mixture Model (LSMM), utilizing 2025 Sentinel-2A/2B imagery. Data were organized into spatiotemporal cubes within R environment and classified using the Random Forest algorithm. Model performance was assessed using a confusion matrix and accuracy metrics, including User’s Accuracy (UA), Producer’s Accuracy (PA), F1-score, and Intersection over Union (IoU), as well as spatial analysis of agreement and disagreement between predicted maps and reference data. Results demonstrate high classification precision for vegetation classes, specifically pasture (F1 = 0.91) and forest formations (F1 = 0.87). Primary misclassifications occurred between spectrally similar classes, particularly within small fragments and intermediate regeneration stages. Overall, the integration of SITS and LSMM enhanced class separability by incorporating temporal dynamics and mitigating spectral mixing effects, highlighting its potential as an operational tool for environmental restoration monitoring. Full article

A clustering analysis was conducted to identify distinct patient subgroups with White Blood Cells (WBC) count alongside Age and Total Immunoglobulin E (IgE) biomarkers. All data were obtained from a coordinated primary care network operating in Apulia (Southern Italy). We analyzed 300 patient records, performed preprocessing and exploratory data analysis, and then applied unsupervised clustering directly to the standardized three-variable feature space (Age, WBC, and Total IgE), followed by supervised validation steps. Several algorithms were applied for clustering. Among the evaluated methods, K-means and Spectral Clustering showed the most favorable internal validation profiles, based on Silhouette Score (SS), Calinski–Harabasz Index (CH), and Davies–Bouldin Index (DB). K-means achieved the best scores (SS = 0.406, CH = 190.00, DB = 0.900), closely followed by Spectral Clustering (SS = 0.398, CH = 182.57, DB = 0.936), outperforming Agglomerative Clustering (SS = 0.361, CH = 160.41, DB = 1.016) and Gaussian Mixture Models (SS = 0.233, CH = 103.89, DB = 1.289). Post-clustering ANOVA analyses indicated significant differences in WBC, age, and total IgE across the five consensus clusters. An evaluation of cluster internal separability occurred through the training of a Random Forest classifier to predict cluster membership. The results indicate internal cluster separability within the analyzed dataset, but more external verification and clinical evidence are necessary for validation. The research group established clinical descriptions along with suggested treatment plans and detected co-existing diseases to help validate model-based findings. A simplified cluster-informed clinical summary based on biomarker ranges was derived to support interpretation of the identified patient profiles. This integrated method preliminarily suggests that patient strata may be identified from routine clinical variables, while highlighting the importance of internal validation and clinical interpretability in clustering research. Full article

Multi-temporal orbital satellite imagery is an alternative for measuring behavioral patterns or trends in different physiognomies through vegetation indices (VIs) and Spectral Linear Mixture Models (SLMMs). In this study, time series of Landsat 7/8/9 and Sentinel-2 have been used to classify a considerable quantity of areas spread over the São Paulo state from 2021 to 2024. Because the large amount of samples considered in our analysis, self-organizing maps (SOMs) have been applied as a convenient method to group similar satellite image time series samples with respect to a certain vegetation index or green vegetation fraction (VEG). Since every dataset area belongs to different types of physiognomies, each cluster has been labeled according to the plurality technique. Additionally, we obtained the mean spectral behavior of the VIs and VEG in the 2021–2024 seasonal cycle of all samples. The results showed similar variations from the rainy to the dry season for most of the physiognomies. On the other hand, this research indicates that the proposed method for classification the Brazilian areas spread over the São Paulo state is consistently good, obtaining the best performance (quantization error) associated with Normalized Difference Vegetation Index (NDVI) time series samples. Full article

Multivariate calibration methods have proven to be helpful in interpreting complex spectral data, particularly in the simultaneous analysis of pharmaceutical mixtures. In this study, three chemometric-assisted spectrophotometric methods were developed and validated for the simultaneous assessment of flunixin meglumine (FM) and florfenicol (FF), namely, multivariate curve resolution–alternating least squares (MCR-ALS), artificial neural networks (ANNs), and partial least squares (PLS). These methods were successfully utilized to address the significant spectral overlap between FM and FF in their combined dose form, enabling simultaneous quantification without prior chromatographic separation. Statistical analysis was conducted to compare the performance of the proposed methods to that of a published HPLC method, and the results showed no significant variation in trueness or precision. The proposed methods were validated according to ICH guidelines, showing high sensitivity, low LOD and LOQ, and excellent precision (%RSD < 2.0%). Furthermore, they were evaluated for environmental sustainability using the analytical greenness (AGREE) metric and the complex modified green analytical procedure index (Complex MoGAPI), which provided a greenness score of 0.7 and a total sustainability score of 80. These results demonstrate the applicability of the proposed chemometric methods as straightforward, effective, and ecologically beneficial substitutes for regular quality control analysis. Full article

The present study aimed to investigate whether Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy could be effectively applied for the botanical origin confirmation of monofloral (fir, thyme, pine) and flower/polyfloral (flower, citrus, asfaka, and mixtures) honey in accordance with melissopalynological analysis, and to unveil the adulteration of monofloral honey with flower/polyfloral honey. Fifty-nine samples were subjected first to melissopalynological analysis to record the dominant pollen flora. Afterwards, ATR-FTIR analysis identified the dominant spectral regions of interest. Among them, 3300–3200 cm⁻¹, 2970–2920 cm⁻¹, 1730–1600 cm⁻¹, 1420–1410 cm⁻¹, 1390–1380 cm⁻¹, 1380–1330 cm⁻¹, 1260–1225 cm⁻¹, 1210–1180 cm⁻¹, 1150–1130 cm⁻¹, 1100–1010 cm⁻¹, and 950–750 cm⁻¹ showed a differentiation potential. Pattern recognition [multivariate analysis of variance (MANOVA)/linear discriminant analysis (LDA) and dimension reduction (factor analysis)] techniques resulted in 100% classification of samples by botanical origin, with the most significant factor parameters being the regions of 1730–1600 cm⁻¹, 1420–1410 cm⁻¹, and 950–750 cm⁻¹, which indicate the presence of water, carbohydrates, ketones, amino acids, and organic acids. Fir, thyme, and pine samples were also adulterated with the batch of flower/polyfloral honey samples (20% w/w), and ATR-FTIR, in combination with the aforementioned multivariate techniques, differentiated the adulterated samples from monofloral samples with an overall cross-validation prediction rate of 91.2% based on LDA. ATR-FTIR, when combined with chemometrics, can be a rapid analytical technique for confirming the botanical origin and adulteration of monofloral honey with polyfloral honey, with an error rate below 9%. Full article

Fourier Transform Infrared Spectroscopy (FTIR) is increasingly used for the mineralogical characterization of complex materials such as ceramics, soils and clays. However, its quantitative application remains limited due to spectral overlapping and matrix effects in solid samples. In this study, a semi-quantitative mineralogical analysis method based on Attenuated Total Reflectance FTIR (ATR-FTIR) is proposed. The method uses the principal absorption band of calcite as a normalization reference in order to estimate relative molar absorptivity coefficients according to the Lambert–Beer law. Experimental spectra obtained from pure minerals and laboratory mineral mixtures were analyzed using derivative spectroscopy and numerical optimization. The correlation between experimental and calculated spectra was performed using the GAMS equation modeling environment and the nonlinear programming solver CONOPT. Mineral mixtures were used to determine the minimum detectable band intensity and detection limits. Bands with normalized intensities lower than 0.01 were discarded, corresponding to a detection limit of approximately 7 mol%. Application of the proposed methodology to ceramic coatings samples from Teruel and Castellón demonstrated that the FTIR spectra are dominated by aluminosilicate bands associated with quartz and clay minerals, together with carbonate features attributable to calcite. These results are consistent with the expected mineralogical composition of ceramic raw materials and confirm the suitability of the method for analyzing natural samples. However, the ATR-FTIR method presents several inherent limitations that may affect both the accuracy and reproducibility of spectral data. Full article

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecular analysis, yet the recognition of mixed spectra remains challenging because severe peak overlap makes mixture-specific data expensive to acquire and difficult to cover exhaustively. Current machine-learning approaches often rely on labeled mixture datasets, synthetic mixed spectra, or prior component-matching schemes, making their performance strongly dependent on task-specific mixture data. A pure-spectrum-trained framework for SERS mixture recognition is presented here based on component evidence learning and two-stage inference. Using paraquat, thiram, and tricyclazole as representative target compounds, the framework learns reusable constituent-level evidence directly from pure-substance spectra and converts it into mixture-category predictions within a unified recognition model. This design avoids mixture-specific parameter training while enabling direct recognition of binary and ternary mixtures. Experiments on SERS spectral datasets yielded a mixture recognition accuracy of 98.58%. The results show that pure-substance spectral learning can support accurate recognition of complex SERS mixtures and provide a scalable strategy for mixture analysis when labeled mixture data are limited. Full article

Binding affinity prediction in computational drug discovery is confounded by trivial correlations between molecular size and measured potency. We introduce cellular sheaf Laplacians as descriptors of ligand molecular geometry that quantify geometric frustration independent of system size. Sheaves are constructed over molecular graphs by assigning three-dimensional coordinate spaces to atoms and projection operators encoding ideal bonding geometry to edges; eigendecomposition of the resulting Laplacian yields spectral features measuring inconsistencies between local geometric constraints and global topology. Applied to 14,050 protein-ligand complexes from the PDBbind v2020 refined set, MW-residualized Sheaf features capture a statistically significant geometric signal ($r_{\mathrm{partial}} = 0.171$, $p<{10}^{-70}$) that is orthogonal to the Wiener index ($r=0.013$) and persists after controlling for both molecular weight and classical graph-theoretic descriptors ($r_{\mathrm{partial}} = 0.390$, $p<{10}^{-9}$). Sheaf spectral features alone achieve predictive performance ($R^2=0.403$) approaching that of fourteen classical cheminformatics descriptors ($R^2=0.446$), and their combination yields consistent improvements across the binding affinity spectrum (RMSE $=1.43$$p{K}_d$). Permutation importance analysis confirms the Sheaf Frobenius norm as the second most influential descriptor after molecular weight. We introduce Topological Binding Efficiency as a size-normalized quality metric identifying ligands that achieve potent binding through geometric complementarity rather than molecular bulk. Gaussian mixture analysis of the maximum eigenvalue distribution among strong binders reveals two distinct spectral modes corresponding to planar aromatic and three-dimensional sp3-rich scaffolds, confirmed by significant differences in fraction of sp3 carbons and aromatic ring counts ($p<{10}^{-8}$). As an intentionally ligand-centric framework, our approach complements rather than replaces protein-aware co-modelling architectures. This work establishes cellular sheaf theory as a principled framework for encoding molecular topology with statistically significant associations with binding affinity, providing interpretable geometric insights that are inaccessible to conventional molecular descriptors. Full article

Flunarizine is a calcium channel blocker widely used in neurological disorders; however, its low aqueous solubility may influence formulation stability and drug dispersion in polymer-based systems. The present study aimed to evaluate the compatibility of flunarizine with selected excipients and to investigate its incorporation into polymeric hydrogel matrices. Binary mixtures of flunarizine with excipients such as hydroxypropyl-β-cyclodextrin, polyethylene glycol (PEG 6000), Tween 20, gelatin, and citric acid were prepared and characterized using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG/DTG), and high-performance liquid chromatography (HPLC). The FTIR spectra of the analyzed samples do not reveal the appearance of new absorption bands that may indicate chemical interactions; instead, minor spectral variations were observed due to weak intermolecular interactions within the polymer network. Thermal analysis revealed decomposition patterns consistent with those of the individual components, suggesting the absence of significant incompatibilities. A validated RP-HPLC method enabled sensitive and reliable quantification of flunarizine in the analyzed systems, with a limit of detection (LOD) of 0.05 µg/mL and a limit of quantitation (LOQ) of 0.16 µg/mL. Accuracy testing showed average recovery rates of 100% across 80–120% spiking levels. Overall, the results support the compatibility of flunarizine with the investigated excipients and the suitability of the studied hydrogels as potential drug delivery matrices. Full article