Search Results (453)

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

Modeling mainshock–aftershock ground motions is essential for seismic risk assessment, especially in regions experiencing frequent earthquakes. Recent studies have often employed Copula-based joint distributions or machine learning techniques to simulate the statistical dependency between mainshock and aftershock parameters. While effective at capturing nonlinear correlations, these methods are typically black box in nature, data-dependent, and difficult to generalize across tectonic settings. More importantly, they tend to focus solely on marginal or joint parameter correlations, which implicitly treat mainshocks and aftershocks as independent stochastic processes, thereby overlooking their inherent spectral interaction. To address these limitations, this study proposes an explicit and parameterized modeling framework based on the evolutionary power spectral density (EPSD) of random ground motions. Using the magnitude difference between a mainshock and an aftershock as the control variable, we derive attenuation relationships for the amplitude, frequency content, and duration. A coherence function model is further developed from real seismic records, treating the mainshock–aftershock pair as a vector-valued stochastic process and thus enabling a more accurate representation of their spectral dependence. Coherence analysis shows that the function remains relatively stable between 0.3 and 0.6 across the 0–30 Rad/s frequency range. Validation results indicate that the simulated response spectra align closely with recorded spectra, achieving R² values exceeding 0.90 and 0.91. To demonstrate the model’s applicability, a case study is conducted on a representative frame structure to evaluate seismic vulnerability and economic loss. As the mainshock PGA increases from 0.2 g to 1.2 g, the structure progresses from slight damage to complete collapse, with loss rates saturating near 1.0 g. These findings underscore the engineering importance of incorporating mainshock–aftershock spectral interaction in seismic damage and risk modeling, offering a transparent and transferable tool for future seismic resilience assessments. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article

Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. Full article

The EGCG/PPN composite, prepared by combining pea protein nanofibrils (PPNs) with epigallocatechin gallate (EGCG), could be used as a multifunctional nanocarrier. Compared to pea protein isolate (PPI), EGCG/PPN composites exhibited remarkably higher turbidity and zeta potential, along with similar UV spectra. Intrinsic fluorescence spectroscopy, ThT fluorescence spectroscopy, and surface hydrophobicity analysis suggested that the interactions between EGCG and PPN were primarily driven by hydrophobic forces. UV spectra indicated that the microenvironment of amino acid residues in the tertiary structure of the protein changes upon complexation, and circular dichroism (CD) revealed that the incorporation of EGCG increases the β-sheet content in the protein’s secondary structure. Analyses of DPPH and ABTS radical scavenging activity, as well as reducing power, demonstrated that the synergistic effect between EGCG and PPN did not hinder the inherent antioxidant properties of EGCG but rather enhanced them significantly. Transmission electron microscopy (TEM) images showed that the addition of EGCG reconstructed the fibril morphology, thereby affecting the properties of PPNs. Overall, the composite fabricated through the interaction between PPN and EGCG shows great potential as a nanocarrier in the processing of functional foods. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

We report on the synthesis and characterization of a novel fluorenyl-methoxycarbonyl (Fmoc)-containing thioxo-triazole-bearing dipeptide 5, evaluated for potential therapeutic applications. The compound was tested for its antioxidant and antimicrobial properties, demonstrating significant effects in scavenging reactive oxygen species (ROS) and inhibiting microbial growth, particularly when combined with plant extracts from an endemic Peonia species from the Caucasus. Circular dichroism (CD) binding studies with bovine serum albumin (BSA) and calf thymus DNA revealed important interactions, suggesting the dipeptide’s potential in biomedically relevant conditions that involve DNA modulation. Molecular docking and CD spectra deconvolution provided additional insights into the binding mechanisms and structural characteristics of the formed complexes with the biomolecular targets. The Fmoc group enhances the dipeptide’s lipophilicity, which may facilitate its interaction with cellular membranes, supporting efficient drug delivery. A computational evaluation at the ωB97XD/aug-cc-pVDZ level of theory was carried out, confirming the experimental results and revealing a powerful potential of the peptide as an antioxidant, through FMOs, MEP analysis, and antioxidant mechanism assessments. Together, these findings suggest that this dipeptide could be valuable as an antimicrobial and antioxidant agent, with potential applications in pathologies involving oxidative stress, DNA modulation, and microbial infections. Full article

Background: A key challenge in modern pharmaceutical research is developing predictive models for drug formulation behavior. Since permeability is closely linked to molecular properties, considering a broad of characteristics is essential for building reliable predictive tools. Near-infrared spectroscopy (NIR), a non-destructive, non-invasive, and chemically specific method, offers a powerful alternative to current gold-standard methods approved by regulatory agencies. Objectives: This study aims to apply a partial analytical quality by design (AQbD) approach to enhance the understanding and development of NIR and RP-HPLC methodologies. Methods: The employment of NIR with multivariate data analysis enabled the establishment of chemometric models for the classification and quantification of bifonazole (BFZ) in cream formulations. Results: An analytical target profile (ATP) was defined to guide the selection of critical method variables and support method design and development activities. Risk assessment was carried out using an Ishikawa diagram. For the RP-HPLC method, key performance parameters such as peak area, theoretical plates, tailing factor, and assay were evaluated, while NIR spectra and BFZ concentration were considered for method performance. The quantification models enabled the accurate determination of BFZ content, yielding results of 8.48 mg via NIR and 8.34 mg via RP-HPLC, with an RSD of 1.25%. Conclusions: These findings demonstrate the robustness and reliability of the models, making them suitable for routine quality control of BFZ formulations. Future research should aim to explore its use for monitoring permeation dynamics in real time and integrating it into regulatory frameworks to standardize its application in pharmaceutical quality control and formulation development. Full article

Archaeological mollusk shells, such as those of Littorina obtusata/fabalis, hold valuable information about past human behavior and cultural practices. However, the original coloration of these shells, crucial for understanding their symbolic significance, is often lost due to taphonomic processes. Raman spectroscopy is a powerful technique for non-destructive analysis of archaeological samples, enabling the identification of pigments and mineralogical components. In this study, we present a methodology to predict, using Raman spectroscopy and k-means clustering, the original coloration of archaeological L. obtusata/fabalis shells which have lost their original coloration. Raman spectra were acquired from both modern shells, exhibiting a range of natural colors, and archaeological shell samples from La Chora cave (Cantabria, northern Spain). Spectral data were preprocessed to remove noise and baseline effects, and k-means clustering was applied to group the spectra based on their inherent spectral similarities. By comparing the spectral signatures of the archaeological samples with those of the modern shells within the generated clusters, we inferred the likely original coloration of the archaeological specimens. This approach provides a quantitative framework for predicting archaeological shell colors. Full article

During transformer operation, long-term vibration causes the winding to loosen axially. When hit by a short-circuit, the winding deforms to different extents. Thus, identifying early looseness faults in transformer windings is vital for power systems’ stability. To address issues including scarce vibration data across multiple load conditions for transformer winding looseness faults, inadequate extraction of two-dimensional spectrogram features, and the inability to boost recognition accuracy caused by overfitting during fault recognition model training, this study constructed a 10 kV power transformer vibration test platform. It measured the vibration signals on the box surface under various winding looseness conditions and built a time–frequency-domain vibration spectrum library for different load currents. Then, a fault identification model based on vibration spectra and ConvNeXt was constructed, and model verification and analysis were carried out. The results indicate that after training, the fault recognition accuracy of the spectrum containing three load conditions is comparable to that of a single load condition. The average recognition accuracy at six box-surface measuring points reaches 97.9%. Moreover, the ConvNeXt model outperforms the traditional ResNet50 by 1.2%. This new model effectively addresses overfitting and offers strong technical support for detecting different transformer winding looseness faults. Full article

Early detection and continuous monitoring of diseases are critical to improving patient outcomes, treatment adherence, and diagnostic accuracy. Traditional melanoma diagnosis relies primarily on visual assessment and biopsy, with reported accuracies ranging from 50% to 90% and significant inter-observer variability. Among emerging diagnostic technologies, Raman spectroscopy has demonstrated considerable promise for non-invasive disease detection, particularly in early-stage skin cancer identification. A portable, real-time Raman spectroscopy system could significantly enhance diagnostic precision, reduce biopsy reliance, and expedite diagnosis. However, miniaturization of Raman spectrometers for portable use faces significant challenges, including weak signal intensity, fluorescence interference, and inherent trade-offs between spectral resolution and the signal-to-noise ratio. Recent advances in silicon photonics present promising solutions by facilitating efficient light collection, enhancing optical fields via high-index-contrast waveguides, and allowing compact integration of photonic components. This work introduces a numerical analysis of an integrated digital Fourier transform spectrometer implemented on a silicon-nitride (SiN) platform, specifically designed for Raman spectroscopy. The proposed system employs a switch-based digital Fourier transform spectrometer architecture coupled with a single optical power meter for detection. Utilizing a regularized regression method, we successfully reconstructed Raman spectra in the 800 cm⁻¹ to 1800 cm⁻¹ range, covering spectra of both benign and malignant skin lesions. Our results demonstrate the capability of the proposed system to effectively differentiate various skin cancer types, highlighting its feasibility as a non-invasive diagnostic sensor. Full article

Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe⁺ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. Methods: In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. Results: We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. Conclusions: The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well. Full article