Search Results (609)

A comprehensive multiregional characterization of the spectral response of cassava leaves across different ontogenetic stages was performed. For this, ultraviolet (UV), visible (VIS) and shortwave near-infrared (UV-VIS-NIR; 200–900 nm) regions were used to identify spectral signatures and indices for their potential use as biomarkers of leaf development and physiological status of plants under induced senescence conditions. Manihot esculenta Crantz (HMC-1 variety) was used as a model. Spectral signatures were obtained from leaves at two phenological stages (4 and 6 months after planting) using UV-VIS-NIR spectroscopy by the diffuse reflectance technique. Classical and experimental spectral indices were evaluated, and their discriminatory power through different ontogenies was assessed using ANOVA/Kruskal–Wallis and post hoc tests. Senescence effects were further examined by postharvest monitoring (1–20 days), with temporal, ontogenetic, and interaction effects validated using linear mixed models (LMMs), while multivariate structure and spectral convergence were explored via principal component analysis and hierarchical clustering (PCA-HCA). Functionally Enhanced Derivative Spectroscopy (FEDS), comparative analysis, and spectral correlation mapping allowed signal’s selective enhancement and the identification of phenolic compounds, photosynthetic pigments, and structural molecular components. Results showed high ontogenetic stability of UV-associated phenolic signals (~210–220 nm), whereas the VIS region (420–600 nm) clearly differentiated young leaves. The NIR region was stable across ontogeny but highly sensitive to temporal degradation, reflecting changes in water status and internal structure. UV-VIS-NIR indices effectively differentiated young leaves and changes by stress. It is concluded that multiregional characterization of the spectral response supported by FEDS allows the extraction of robust indices with strong potential as biomarkers of leaf maturation and senescence in cassava. Full article

Thermal annealing improves the mechanical, structural, and electrical properties of polymer thin films, promoting processes like residual solvents and stress removal, as well as the crystallization and densification of the gel layer. The effects are strongly dependent on the annealing temperature, where optimal temperatures enhance film performance, while excessive thermal exposure may induce negative outcomes like amorphous structural transitions, increased roughness, and defect formation. In this work, thin films of two humidity-sensitive poly(vinyl alcohol) (PVA)-based copolymers with grafted poly(N,N-dimethylacrylamide) (PDMA) chains were investigated. The polymers differ in grafting density and chain length, enabling the assessment of macromolecular architecture’s effects. Spin-coated films with 150–200 nm thickness were annealed at three temperatures: 60 °C, 120 °C, and 180 °C. By using UV-VIS-NIR spectroscopy and the quartz crystal microbalance method, a comprehensive characterization of temperature- and humidity-induced changes in swelling, hysteresis, sensitivity, detection resolution, and water uptake is performed, elucidating the role of the macromolecular architecture on the post-deposition annealing modification of gel film properties and its humidity response. High-performance humidity sensing with a resolution of 0.8% RH is achieved through the optimization of the interplay between the macromolecular architecture and annealing temperature. In addition, the study highlights and explores the potential of these films for optical color-based moisture detection. Full article

This study evaluates the effect of decorative ceramic screen printing on the optical and photovoltaic performance of glass covers intended for building-integrated photovoltaics (BIPV). Nine ceramic-printed glass samples with different colors and optical densities were compared with a 4 mm Optiwhite reference glass and a bare silicon solar cell. The samples were characterized by UV-VIS-NIR spectrophotometry, energy-dispersive X-ray spectroscopy (EDS), and electrical measurements under simulated AM 1.5G irradiation at 1000 W/m². The optical results showed that the Optiwhite reference provided the highest transmittance, whereas the printed samples exhibited lower transmission, typically in the range of 60–80% in the visible region, depending on the coating type. Among the decorative variants, sample 1 showed the highest transparency, while sample 6 exhibited the lowest transmittance. The spectral behavior of the coated glasses indicates that the ceramic layers modify the photon flux reaching the solar cell through wavelength-dependent absorption and scattering effects. The photovoltaic measurements confirmed a clear relationship between decorative coating and electrical performance. Relative to the Optiwhite-covered reference cell, the printed samples showed power losses ranging from approximately 17% to 32%, with sample 1 achieving the highest maximum power among the decorative variants at 1.41 W, and sample 4 the lowest at 1.16 W. The main electrical effect of the ceramic coatings was a reduction in short-circuit current, whereas the open-circuit voltage remained nearly constant across the tested samples. EDS analysis identified the presence of ceramic-layer constituents associated with silica-, zinc-, titanium-, iron-, cobalt-, aluminum-, and fluorine-containing compounds, supporting the interpretation of vitrified decorative coatings formed during high-temperature processing. Overall, the results demonstrate that decorative ceramic printing can provide a practical compromise between architectural appearance and photovoltaic output when the optical density of the coating is appropriately controlled. Full article

Rapid and effective estimation of soil organic matter (SOM) is crucial for the scientific management of Moso bamboo forests. This study investigated Moso bamboo forest soils in Yongan City, Fujian Province, and systematically evaluated the synergistic adaptation strategies coupling spectral preprocessing methods, feature extraction strategies, and machine learning models based on visible and shortwave near-infrared (Vis-NIR) spectroscopy. The results indicated that: (1) Conventional preprocessing algorithms attenuated the SOM spectral feature signals dominated by soil color within the limited wavelength range of field in situ spectral data, resulting in a general decline in the accuracy of the estimation models. (2) Feature extraction and modeling algorithms exhibited distinct adaptability across different content intervals. Within the low-content interval (<15 g/kg), simple physical indices combined with random forest (RF) achieved effective estimation at a lower computational cost (RPD = 2.18). Within the high-content interval (>25 g/kg), the synergistic strategy of the CARS algorithm combined with support vector regression (SVR) yielded the optimal estimation performance (R² = 0.83, RPD = 2.45) and effectively mitigated the underestimation of high values caused by data imbalance. In conclusion, this study proposed a feature–model synergistic estimation approach, validating its feasibility for estimating SOM in Moso bamboo forests under the specific constraints of the current study area, thereby serving as a valuable reference for forest soil SOM monitoring in specific regions. Full article

Tomato seedlings are highly sensitive to nutrient deficiencies, and rapid identification of nitrogen (N), phosphorus (P), and potassium (K) stress is essential for precision fertilization. In this study, a novel hierarchical classification framework integrating visible and near-infrared (Vis-NIR) spectroscopy and machine learning was developed for fine-grained identification of nutrient stress in tomato seedlings. A total of 2814 leaf spectra were collected, and multiple preprocessing methods were systematically evaluated. Feature wavelength selection was conducted using the successive projection algorithm (SPA) and Random Frog to reduce redundancy and enhance model performance. Four machine learning models were implemented within a three-stage classification strategy to identify stress occurrence, nutrient type, and deficiency severity across three gradients (50%, 70%, and 100%). Results indicated that multiplicative scatter correction (MSC) achieved the best preprocessing performance. The MSC-SPA-XGBoost model yielded the highest overall classification accuracy of 92.74% across the complete 10-class stress categorization on an independent test set. Bootstrap analysis further confirmed model robustness, with a 95% confidence interval of [0.9024, 0.9436]. Compared with traditional vegetation indices (which achieved a maximum validation accuracy of only 75.73%), the proposed method showed superior discriminative capability for multi-class nutrient stress. These findings demonstrate that Vis-NIR spectroscopy combined with feature-driven machine learning provides a rapid and reliable approach for precision nutrient management in tomato cultivation. Full article

Fatty acid abundance is a key parameter for evaluating the quality of edible oils. This study developed a rapid and non-destructive method for predicting palmitic acid content in edible oils by combining visible-near-infrared (Vis-NIR) reflectance spectroscopy with deep learning models. A total of 1740 reflectance spectra in the range of 350–2500 nm were collected from 87 brands of edible oils, including peanut, soybean, corn, sunflower, rapeseed, sesame, and olive oils. Reference values of palmitic acid content were determined via gas chromatography–mass spectrometry (GC-MS). Two conventional machine learning models (SVR and KNN) and four deep learning models (1D-CNN, 1D-ResNet, 1D-Inception, and 1D-Inception-ResNet) were developed and compared using both full-spectrum data and CARS selected characteristic wavelengths. Among the full-spectrum models, the designed 1D-ResNet model achieved the best performance, with the determination coefficient of prediction ($R_p^2$) of 0.9027 and the root mean square error of prediction ($RMS{E}_p$) of 1.13 in the prediction dataset. The proposed 1D-Inception-ResNet model yielded the best prediction results based on the 91 selected informative wavelengths via competitive adaptive reweighted sampling (CARS), achieving an $R_p^2$ of 0.9825 and an $RMS{E}_p$ of 0.4804 in the prediction dataset. The experimental results indicated that Vis-NIR reflectance spectroscopy combined with informative wavelength selection and deep learning models provided an effective strategy for rapid prediction of palmitic acid content in edible oils. Full article

Clear aligner therapy (CAT) increasingly relies on composite-based attachments to improve force transmission and aligner retention, yet the role of flowable composite properties in clinical performance remains poorly understood. In this study, five commercially available flowable composites used for orthodontic attachments—Aligner FLOW LC, SIMPLY SHADE, SOFT ENA Flow, TETRIC EvoFlow, and VENUS Bulk Flow One—were comparatively investigated through physicochemical, morphological, optical, thermal, and rheological characterization. Scanning electron microscopy coupled with energy-dispersive X-ray analysis, thermogravimetric analysis, UV–Vis–NIR and ATR–FTIR spectroscopy, and rheological measurements before and after curing were employed to probe composition, filler content, viscoelastic behavior, and mechanical response. The results revealed marked differences among the investigated materials, with post-curing storage modulus spanning nearly two orders of magnitude, from 0.06 MPa for SOFT ENA Flow to approximately 5 MPa for SIMPLY SHADE. Similarly, the elastic modulus ranged from about 20 MPa to nearly 1000 MPa for the softest and stiffest resins, respectively. Interestingly, SOFT ENA Flow, the softest material after curing, also exhibited the highest pre-curing viscosity, nearly one order of magnitude greater than the least viscous resin, Aligner FLOW LC. These findings highlight an intrinsic trade-off between pre-cure processability and post-cure mechanical stability, providing a rational framework for material selection in orthodontic attachments and supporting more predictable and durable CAT outcomes. Full article

Carbon dots (CDs), including carbon quantum dots (CQDs), are ultra-small carbon-based nanomaterials, typically below 10 nm, with tunable photoluminescence, high aqueous dispersibility, favorable biocompatibility, low toxicity, and abundant surface functional groups. These properties make CDs promising multifunctional platforms for nanomedicine, particularly in bioimaging, biosensing, targeted drug/gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), antimicrobial treatment, and theranostic applications. This review critically examines recent advances in CD fabrication, including top-down, bottom-up, green biomass-derived, microwave-assisted, hydrothermal, and emerging hybrid strategies, with emphasis on how precursor selection, heteroatom doping, surface passivation, and polymer/ligand functionalization regulate optical performance, biological interaction, and therapeutic efficiency. The review discusses structural classification, including CQDs, graphene quantum dots (GQDs), carbon nanodots, and carbonized polymer dots (CPDs), together with major characterization approaches such as ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM). Particular attention is given to red/near-infrared (NIR) emission, renal clearance, drug-loading behavior, reactive oxygen species (ROS) generation, toxicity mechanisms, biodistribution, and long-term biosafety. This review also highlights key translational barriers, including batch-to-batch variability, limited standardization, scalable manufacturing, regulatory uncertainty, and incomplete pharmacokinetic evaluation. It considers artificial intelligence (AI) and machine learning (ML) as emerging tools for reproducible CD design. CDs represent versatile and clinically promising nanoplatforms, but their translation requires standardized synthesis, rigorous safety assessment, and application-specific regulatory validation. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

Iron-exchanged bentonites derived from a natural montmorillonite-rich clay (Taganskoe deposit, Kazakhstan) were prepared through a simple aqueous ion-exchange route using Fe(II) or Fe(III) inorganic salt precursors, yielding final Fe contents of ca. 5–7 wt.%, while preserving the smectite layered framework. A mild thermal treatment under air was applied to tune iron coordination without triggering major structural collapse. The resulting materials were characterized by ED-XRF, PXRD, FE-SEM/EDX, DLS/ζ-potential and DR UV–Vis–NIR spectroscopy, revealing predominantly exchanged Fe species with a limited fraction of surface iron-oxide clusters, whose contribution increases after activation. Structure–reactivity relationships were probed under mild conditions in liquid-phase ethyl acetate using dimethyl methylphosphonate (DMMP) and 2-chloroethyl ethyl sulfide (2-CEES) as organophosphorus and organosulfur hazardous chemicals and chemical warfare agent simulants, respectively. Fe(III)-bentonite enabled very fast DMMP removal (ca. 93% within 0.5 h) with a remarkable improved performance with respect to Fe(II)-bentonite and the pristine mineral clay. For 2-CEES, the presence of H₂O₂ markedly enhanced oxidation on Fe-containing clays, reaching quantitative abatement within 24 h (up to >90%), with strong retention of oxidized sulfur products by the clay matrix. These results highlight Fe-exchanged natural bentonites as robust, cheap and multifunctional adsorption/catalytic solids for decontamination and water-treatment applications. Full article

Rapid and cost-effective estimation of soil total nitrogen (TN) is essential for soil fertility assessment and nutrient management. However, the performance of laboratory visible–near-infrared (Vis–NIR) models is shaped not only by preprocessing and modeling strategy but also by sample preparation and the soil’s compositional background. In this study, TN prediction was evaluated using 376 topsoil samples from two contrasting datasets: Mollisols from the black-soil region of Northeast China and Ultisols from Qiyang County, Hunan Province, southern China. Spectra acquired over 350–2500 nm for three particle-size fractions were preprocessed using Savitzky–Golay smoothing combined with standard normal variate (SNV), first-derivative, or second-derivative transformations, and modeled using partial least squares regression (PLSR), support vector regression (SVR), and extreme gradient boosting (XGBoost). Model development used a 5 × 5 nested cross-validation followed by evaluation on a sample-grouped held-out test set. Among all combinations, XGBoost with first-derivative preprocessing on the 0.25 mm fraction produced the best performance, with test R² values of 0.91 for Mollisol and 0.78 for Ultisol. Shapley additive explanations (SHAP) and principal component analysis (PCA) consistently identified informative spectral regions at 430–480 and 1330–1450 nm for Mollisol and at 585–635, 820–900, and 2180–2240 nm for Ultisol. Prediction errors were larger in the sampled Ultisol dataset and increased with DCB-extractable Fe and mineral backgrounds. A second-stage log-domain residual correction incorporating ancillary soil properties further reduced the Ultisol RMSE from 0.30 to 0.27 g kg⁻¹. These findings support the 0.25 mm, first-derivative, XGBoost workflow as a robust laboratory Vis–NIR approach for TN prediction and indicate that composition-aware residual correction can improve prediction in oxide- and mineral-rich soils. Full article

Moldy pear core constitutes a severe internal defect that compromises fruit quality. This study proposes a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals, aimed at supporting post-harvest quality control and automated industrial sorting. We collected spectral signals from pears and quantified the moldy pear core area to classify samples into healthy (S = 0%), slightly moldy (0 < S ≤ 10%), and severely moldy (S > 10%) categories. We constructed a three-tier comparative framework to evaluate the progression from conventional machine learning to advanced deep learning: traditional methods using univariate selection (US) and random forest (RF) for feature extraction followed by support vector machine (SVM) classification; 1D-ResNet for direct processing of spectral signals; and two-dimensional approaches transforming signals into improved gramian angular field (IGAF) or Laplacian pyramid Markov transition field (LPMTF) images processed through deep belief network (DBN), MobileNetv3, and Vision Transformer (ViT). The LPMTF-ViT combination delivered the best performance with 98.98% test accuracy and 94.44% external validation accuracy, significantly exceeding traditional approaches and 1D-ResNet. This innovative approach delivers effective technical support for early-stage, nondestructive detection of internal fruit defects. It also establishes a scalable foundation for automated industrial inspection systems, potentially reducing post-harvest losses while ensuring premium quality control in modern fruit supply chains. Full article

Soil colour and texture play important roles in identifying archaeological features during excavations, particularly in rescue archaeology where rapid and reliable interpretation is required. This study investigated the application of visible-near-infrared (VIS-NIR) soil spectroscopy for quantitatively characterising cultural heritage materials and archaeological soils on freshly exposed surfaces after topsoil removal during excavation. Surface soil spectra were collected using a portable spectrometer from nine features at a rescue excavation site in Hyeondo-myeon, Republic of Korea. A PCA-based spectral deviation approach was applied to detect deviations of archaeological soils from locally defined natural background spectra. Balanced accuracy values exceeded 0.70 under optimised configurations across all sites, with several sites achieving values above 0.80. Strong statistical discrimination coincided with spatially coherent clustering of elevated anomaly values corresponding to archaeologically identified feature zones. The 400–1000 nm wavelength range, combined with locally calibrated background spectra, yielded the most stable and reproducible performance. The proposed workflow demonstrates that field-based VIS-NIR spectroscopy can provide rapid, quantitative, and spatially interpretable support for archaeological feature identification. By integrating sensor-based spectral characterisation with anomaly mapping, the approach minimises interpretive subjectivity and improves analytical reproducibility in excavation decision-making processes. Full article

While both Urbach tails and dangling bonds are known to be present in a-Si films, the current literature lacks parametrization that simultaneously accounts for both types of defects using only transmittance spectra, reflectance spectra, or spectroscopic ellipsometry. To address this issue, we performed parametrizations of three magnetron-sputtered a-Si thin films deposited on glass substrates at different low pressures of argon gas, using only their measured UV–Vis–NIR transmittance spectra T(λ = [300, 2500] nm) and different dispersion models. We preprocessed T(λ) by suppressing both general and bandpass noise to yield the spectrum T_d(λ). The films were parametrized from T_d(λ) using two versions of the Tauc–Lorentz–Urbach dispersion model and the universal dispersion model (UDM) of Franta. The most accurate parametrization was achieved employing UDM including Urbach tail and three subgap oscillators. JDOS and the dielectric function ε(E) were computed by this UDM, and it was concluded that these three oscillators correspond to electron transitions via two bands of dangling bonds. The respective DOS is similar to the DOS previously reported for a-Si:H, but not to a-Si, indicating a relatively low density of dangling bonds in our a-Si films. Record low parametrization errors are achieved, which confirms the accuracy of these results. Full article

This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R² > 0.70), while pH and texture fractions showed moderate performance (R² = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K₂O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients. Full article