Search Results (380)

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility because strong attenuation lowers contrast, while conventional rhodium (Rh) K-edge filtering suppresses part of the high-energy spectral tail. This study presents a Monte Carlo framework for spectroscopic mammography using a voxelated 1 mm thick cadmium telluride (CdTe) sensor and a first-order detector interaction model to evaluate energy-dependent image quality. The model reproduces fluorescence and inter-voxel energy redistribution in CdTe, but not the full detector chain, and remains idealized with respect to charge transport, carrier collection, threshold dispersion, and pile-up. Energy-resolved simulations in the 10–50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and to form integrated-spectrum (IS) images for four tested spectra. For the dense-breast calcium hydroxyapatite (HA) speck detection task considered here, and under the present simulation assumptions, replacing the standard 28 kVp + 50 μm Rh spectrum with 28 kVp + 1 mm Al increased the simulated IS image CNR by 23.11%, with an approximately 5% increase in estimated primary-incident air kerma at the phantom entrance plane. Preliminary experimental implant-phantom images were included as a qualitative feasibility check, showing a trend consistent with simulations. Within the limits of this task-specific simulation, the results suggest that preserving the transmitted high-energy tail can improve HA speck visibility for the present 1 mm CdTe photon-counting detector, with the 28 kVp + 1 mm Al spectrum outperforming the other tested cases. Full article

Nitridation of different materials using ion implantation is of considerable interest for many applications. As electronic components, oxynitride (SiO_xN_y) layers exhibit beneficial properties such as precise compositional variability, refractive index tunability, oxidation resistance, and low mechanical stress. In the present study we investigate nanoscale SiO_xN_y synthesized using ion implantation methods. To introduce N⁺ ions into a shallow Si subsurface region, both conventional ion beam implantation and plasma immersion ion implantation with subsequent high-temperature treatment in dry O₂ are used. The optical and morphological properties and chemical bonding of formed SiO_xN_y layers were studied by applying spectroscopic ellipsometry in the range of VIS-Near IR (SE) and IR (IR-SE), Raman spectroscopy and Atomic Force Microscopy (AFM). Monte Carlo modeling of implant profiles contributed to understanding physical and chemical processes and predicted different influences of the incorporated N⁺ ions on the oxidation mechanism, confirmed by the thickness dependence of SiO_xN_y/Si layers obtained from the SE data analysis. IR-SE spectral analysis established the formation of Si-O, Si-N, Si-N-O and Si-Si chemical bonds in the grown layers. The occurrence of amorphization of the Si crystal lattice due to incorporation of high-energy N⁺ ions into the Si lattice is confirmed by the Raman and ellipsometry results. The free Si atoms can congregate, forming nanocrystalline clusters. AFM imaging revealed that both implantation methods left the surface of the resulting SiO_xN_y layers considerably smooth with similar roughness parameter values. The results of the studies imply that the technological approaches used allow the production of high-quality nanoscale silicon oxynitride films with appropriate tunable composition and properties for possible application in advanced electronic devices for nanoelectronics, optoelectronics and sensor applications. Full article

The growing demand for gold in various high-technology applications necessitates the development of efficient and selective methods for its recovery and analysis, which can be achieved using such macrocyclic ligands as crown esters and their aza-substituted derivatives. The present paper reports on the equilibrium constants for the formation of gold(III) complexes with 18-crown-6, 1-aza-18-crown-6, 1,10-diaza-18-crown-6, and the cryptand 4,7,13,16,21,24-hexaoxa-1,10-diazabicyclo[8.8.8]hexacosane (Kryptofix 222) in aqueous solution at T = 298.2 K, p = 0.1 MPa, I → 0. The equilibrium constants (log β) for the substitution of chloride ions by macrocycles were determined to be 4.52 ± 0.04, 9.15 ± 0.03, 9.08 ± 0.07, and 11.51 ± 0.08, respectively. Equilibrium constants for protonated and polyligand species are also provided. The complexation mechanism was elucidated using a combination of spectroscopic techniques. UV-Vis and IR spectroscopy confirm the substitution of chloride ligands by the nitrogen donor atoms of the aza-macrocycles within the tetrachloroaurate(III) ion. Furthermore, ¹H NMR analysis reveals that the diaza-substituted ligands can form both inclusion complexes, where the gold cation is encapsulated within the macrocyclic cavity, and exclusion complexes. These findings provide a quantitative foundation for the design of novel macrocycle-based extractants and sensors for gold(III). Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

Nitrates and nitrites are inorganic anions which, beyond specific concentration threshold, are classified as water pollutants. Nitrate compounds are commonly used as fertilizers; however, their high concentration in soil and in wastewater, as well as their reduction to nitrites, pose serious environmental and human health risks. Therefore, detecting these ions in water intended for human consumption, zootechnical use, and agricultural applications is essential. This work presents a proof of concept for a spectroscopic prototype setup enabling simple, direct, and simultaneous detection of nitrates and nitrites in water. The device employs solid-state sensor technology and requires no sample pretreatment or chemicals. Ultimately, this apparatus will allow real-time, in-line process analysis. UV absorption bands centered at approximately 302 nm and 355 nm were selected for detecting nitrates and nitrites, respectively. Because nitrite exhibits a slight absorption at 302 nm as well, a straightforward method for simultaneous nitrate and nitrite detection is proposed. The proposed system incorporates a UV deuterium lamp, a 10 cm path length optical cuvette, and a custom home-built silicon carbide detector. This configuration enables testing various concentrations, achieving detection limits of 2.2 mg/L for nitrates and 0.5 mg/L for nitrites. Potential interferences from substances commonly found in drinking and treated agricultural wastewaters, including sodium bicarbonate, sodium sulfate, ammonium chloride, hydrogen peroxide, and sodium hypochlorite, were also investigated. Finally, a compact on-site and online monitoring future device is illustrated. Full article

Superconducting transition-edge sensors (TESs) exhibit excellent single-photon detection performance. The quantum efficiency (QE), which quantifies the probability that an incident photon is absorbed and converted into a measurable signal, is strongly governed by the optical properties of the constituent thin films. Specifically, for typical TES device architectures where optical transmission is negligible, maximizing the QE requires the minimization of surface reflectance to ensure high photon absorptance. In this work, we systematically study how anodic oxidation modifies the optical response of superconducting titanium (Ti) thin films that are relevant for TES devices. Anodization is carried out under well-controlled constant-current conditions in an aqueous electrolyte containing ammonium pentaborate and ethylene glycol. Experimentally, we show that anodic oxidation substantially reduces the ultraviolet (UV) reflectance and induces a monotonic redshift of the reflectance minimum as the anodic oxidation cutoff voltage (V_ocv) increases. Finite-difference time-domain (FDTD) simulations based on spectroscopic ellipsometry data reproduce the measured spectra with good fidelity for most samples, validating the extracted optical constants. By comparing samples prepared at different current densities and oxidation times, we identified V_ocv as the primary parameter controlling the reflectance response, because it determines the thickness and effective optical properties of the anodic TiO_x layer. Under optimized conditions, reflectance values below 1% in the 320.9–340.2 nm wavelength range and below 2% in the 316.3–346.3 nm range are achieved, indicating a significant enhancement in potential absorptance. These results demonstrate that anodic oxidation provides a simple, post-fabrication, and voltage-tunable route for engineering the UV optical response of Ti-based TES structures and for enhancing their potential QE by suppressing reflection losses. Full article

Benzo[a]pyrene (BaP), a potent carcinogenic polycyclic aromatic hydrocarbon, is a critical food contaminant originating from environmental deposition and thermal processing, posing a significant threat to public health and driving stringent global regulations. This review critically examines recent advancements in analytical methodologies for BaP determination, giving particular emphasis to sample preparation and detection techniques. The discussion covers the evolution from conventional methods, such as solid-phase extraction, towards more efficient and sustainable approaches, including magnetic, dispersive, and molecularly imprinted solid-phase extraction, as well as microextraction techniques and gel permeation chromatography. For detection, the performance of established chromatographic methods, such as gas chromatography–mass spectrometry (GC-MS) and high-performance liquid chromatography with fluorescence detection (HPLC-FLD), is evaluated against emerging rapid techniques such as sensors, immunoassays, and spectroscopic methods. The analysis reveals that while significant progress has been made in improving sensitivity, selectivity, and throughput, challenges remain in balancing speed with accuracy, managing matrix effects, and translating novel materials from research to routine application. The review concludes by underscoring the necessity for future development to focus on the integration of smart materials, automation, and advanced data science to achieve robust, on-site, and holistic monitoring solutions for ensuring food safety against BaP contamination. Full article

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article

Ethanol stability is an important indicator for evaluating the quality and heat-processing suitability of raw milk. Traditional ethanol stability testing relies on destructive laboratory procedures, which are not suitable for large-scale industrial use. In contrast, parameters such as protein, fat, lactose and other basic compositional indicators are already measured routinely in dairy plants through sensor-based or spectroscopic systems. This provides the basis for developing a non-destructive soft sensing approach for ethanol stability. In this study, a soft sensing model was developed to predict ethanol stability from commonly monitored raw-milk intake indicators. An autoencoder was used to examine feature correlations and select variables with stronger relevance to ethanol stability. TabNet was then applied to build the classification model, and a TabDDPM-based data generation method was introduced to address class imbalance in the dataset. The proposed model was trained and tested using three years of industrial raw-milk intake data from a dairy company. It achieved an accuracy of 92.57% and a recall of 90.26% for identifying ethanol-unstable samples. These results demonstrate the model’s strong potential for practical engineering applications in real-world dairy quality monitoring. Full article

Infrared (IR) spectroscopy has emerged as a rapid, cost-effective, and reliable alternative to traditional methods, enabling real-time, indirect monitoring of nutrients. Most reviews have discussed visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy individually for soil analysis. This review highlights the application of IR spectroscopy, particularly Vis-NIR, MIR spectroscopy, and their data fusion, coupled with chemometrics and spectral preprocessing for estimating soil attributes. Additionally, the crucial functions of assessing model accuracy and validating model estimates of soil properties are discussed. Partial least squares regression (PLSR) was used in more than 100 studies in 2022. Based on the literature published from 2020 to 2025, the data fusion method predicts soil properties more accurately. This review also sheds light on recent advances in spectroscopic methods, including improvements in speed (e.g., MIR spectroscopy is up to 12 times faster than traditional methods), instrument miniaturization, and integration with portable devices, which can make field analysis more affordable. However, the sensitivity of IR spectroscopy to soil moisture, sample heterogeneity, vegetation cover, and calibration transfer issues remains a significant challenge in certain studies. Therefore, a discussion on the challenges in implementing this technique is included in this review, and future perspectives, such as integration of various sensors and portable devices for real-time soil assessment, are successively discussed. Full article

A novel series of benzoxazole-derived iminocoumarins was synthesized via a Knoevenagel condensation and fully characterized using NMR, UV–Vis spectroscopy, and computational methods. Their photophysical properties were systematically examined in solvents of varying polarity, revealing pronounced effects of both substituents and solvent environment on absorption maxima and intensity. Derivatives bearing electron-donating substituents on the coumarin core exhibited distinct and reversible pH-responsive spectral shifts, confirming their potential as optical pH probes. Experimental pK_a values derived from absorption titrations showed excellent agreement with DFT-calculated data, validating the proposed protonation-deprotonation equilibria and associated electronic structure changes. Structure–property relationships revealed that electron-donating groups enhance intramolecular charge transfer, while electron-withdrawing substituents modulate spectral response and stability. In parallel, the compounds were evaluated for antiproliferative, antiviral, and antifungal activities in vitro. Strong electron-donating substituents were associated with potent but non-selective cytotoxicity, whereas derivatives bearing electron-withdrawing groups displayed moderate and more selective antiproliferative effects against leukemia cell lines. Antifungal screening revealed moderate inhibition of phytopathogenic fungi, particularly for compounds with electron-withdrawing or methoxy substituents. Overall, these findings demonstrate that benzoxazole iminocoumarins represent a promising class of multifunctional heterocycles with potential applications as optical pH sensors and scaffolds for bioactive compound development. Full article

This study evaluated the combined effect of the modified atmosphere packaging (MAP1: 60% CO₂, 10% O₂/30% N₂ & MAP2: 40% CO₂, 30% O₂/30% N₂), and active packaging of oregano essential oil (1% v/w) used as a natural preservative, on the quality and shelf-life extension of fresh sea bream fillets. The samples were stored at four different temperatures (0, 4, 8, and 12 °C), and a microbiological analysis, pH measurements, and sensory evaluations were performed. In parallel, spectral data were obtained using three different spectroscopic sensors (two MultiSpectral Imaging devices and an FT-IR one), and nine different machine-learning regression models were applied to predict the microbiological counts. Oregano essential oil positively affected preservation, reducing microbial growth by 0.5 to 2 log CFU/g, and extending the fillets’ shelf life by up to 48 h based on sensory evaluation. Regarding the sensors’ data, the examined nine models exhibited encouraging results for the rapid microbiological assessment, with the FT-IR data showing the best performance for evaluating the microbiological population. Among the tested algorithms, the least Angle Regression (lars) achieved the best performance for both the flesh and skin datasets, with RMSE values of 0.6075 and 0.5953, MAE of 0.3008 and 0.4567, R² of 0.8858 and 0.7532, and accuracy of 87% and 91%, respectively. The Benchtop-MSI showed the best predictive performance for flesh (RMSE = 0.5926, MAE = 0.4876, R² = 0.7338, and Accuracy = 92%), while the artificial neural network (nnet) performed best for skin (RMSE = 0.6761, MAE = 0.5247, R² = 0.6560, and Accuracy = 84%). Regarding the Portable-MSI, the artificial neural network model gave the highest accuracy for flesh (RMSE = 0.5908, MAE = 0.4663, R² = 0.5903, and Accuracy = 87%), whereas principal component regression was the most effective for skin (RMSE = 0.6600, MAE = 0.5413, R² = 0.5534, and Accuracy = 83%). Full article

Chemical oxygen demand (COD) serves as a critical indicator for assessing the extent of water pollution caused by organic matter. This study proposes an integrated COD detection methodology that combines laser absorption spectroscopy with laser-induced fluorescence spectroscopy, enabling accurate measurement of COD parameters across a wide concentration range. For high-concentration COD, conventional ultraviolet absorption spectrophotometry based on the Lambert–Beer law is employed. However, since laser absorption spectrophotometry exhibits substantial errors in detecting low-concentration COD, laser-induced fluorescence spectroscopy is adopted for the precise quantification of trace-level COD. By integrating these two laser-based approaches, a spectroscopic COD detection system has been developed that simultaneously records absorbance after the laser passes through the sample and quantifies fluorescence intensity perpendicular to the beam with an image sensor, thereby achieving comprehensive COD analysis. Laboratory validation using COD standard solutions demonstrated relative errors below 11% across the concentration range of 2–220 mg/L. Further application to natural water samples confirmed that the integrated laser absorption–fluorescence spectroscopy approach achieves wide-range COD measurement with high sensitivity, a compact configuration, and rapid response, demonstrating strong potential for real-time online water quality monitoring. Full article

Magnetooptics (MO) explores light—matter interactions in magnetized media and has advanced rapidly with progress in materials science, spectroscopy, and integrated photonics. This review highlights recent developments in fundamental principles, experimental techniques, and emerging applications. We revisit the canonical MO effects: Faraday, MO Kerr effect (MOKE), Voigt, Cotton—Mouton, Zeeman, and Magnetic Circular Dichroism (MCD), which underpin technologies ranging from optical isolators and high-resolution sensors to advanced spectroscopic and imaging systems. Ultrafast spectroscopy, particularly time-resolved MOKE, enables femtosecond-scale studies of spin dynamics and nonequilibrium processes. Hybrid magnetoplasmonic platforms that couple plasmonic resonances with MO activity offer enhanced sensitivity for environmental and biomedical sensing, while all-dielectric magnetooptical metasurfaces provide low-loss, high-efficiency alternatives. Maxwell-based modeling with permittivity tensor ($\epsilon$) and machine-learning approaches are accelerating materials discovery, inverse design, and performance optimization. Benchmark sensitivities and detection limits for surface plasmon resonance, SPR and MOSPR systems are summarized to provide quantitative context. Finally, we address key challenges in material quality, thermal stability, modeling, and fabrication. Overall, magnetooptics is evolving from fundamental science into diverse and expanding technologies with applications that extend far beyond current domains. Full article