Search Results (847)

UV-Visible spectroscopy provides qualitative and quantitative information on colored dissolved organic matter (CDOM) that can be used as a proxy for dissolved organic carbon (DOC). We developed an absorbance-based linear model of DOC for the San Sebastian River estuary in NE Florida. We compared linear and mixed models, with and without salinity as an additional fixed effect. All models exhibited strong correlations (R² = 0.88–0.97) with measured DOC values for the training dataset. The model with the strongest performance on the testing dataset was a linear model containing the absorption coefficient at 254 nm, the spectral slope at 275–295 nm, and salinity. The range of measured DOC was 0.5 to 52.3 mg/L, and the model was able to predict DOC concentrations for an independent testing dataset with a relative mean absolute error of 17%. Incubation experiments indicated that aging and photolysis altered absorption coefficients and spectral slopes, which negatively affected the model performance, particularly for photolysis. However, predicted DOC was still well-correlated (R² > 0.9) with measured DOC, even for photolyzed samples. Spectral slope ratios indicate that DOM in the San Sebastian River is mainly terrigenous, and that hydrologic variability, possibly associated with freshwater inflow from rainfall, influences DOC/CDOM concentration and composition. Full article

Sunflowers are one of the most important agricultural crops in the world. Given the high importance of sunflower products in the world market and the scale of their cultivation, the introduction of precision farming technologies into its culture can have a significant economic and environmental effect. This study demonstrated the fundamental possibility of developing a technology for rapid, remote, and non-invasive assessment of sunflower seed moisture to determine the optimal timing for desiccation and harvesting. It has been shown that the moisture content of sunflower seeds can be assessed with high accuracy based on the spectral characteristics of the underside of the inflorescences obtained using a hyperspectral camera in the visible and near-infrared range (VNIR) (from 450 to 950 nm). Random forest regression (RFR) was used to predict sunflower seed moisture. The model performed excellently on the training data (R²_c = 1.00; MAE_c = 0.58; RMSE_c = 0.74, MAPE_c = 1.29) and with a high performance on the testing data (R²_t = 0.98, MAE_t = 2.99, RMSE_t = 3.28, MAPE_t = 12.22). The most significant vegetation indices for determining moisture are CCI, Booch, Datt3, Datt4, LSIRed, modPRI, SR5, TCARI, and TCARI2. Full article

The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors. Full article

Advancements in flexible, low-cost, and recyclable alternatives to transparent conductive oxides (TCOs) are critical challenges in the sustainability of third-generation solar cells. This work introduces a copper mesh-based transparent electrode for dye-sensitized solar cells, replacing conventional fluorine doped-tin oxide (FTO)-coated glass to simultaneously reduce spectral reflection losses, enhance mechanical flexibility, and enable material recyclability. Titanium dioxide (TiO₂) photoanodes were synthesized and directly deposited onto the mesh via a single-step, low-energy ball milling process, which eliminates TiO₂ paste preparation and high-temperature annealing while reducing fabrication time from over three hours to 30 min. Structural and surface analyses confirmed the deposition of high-purity anatase-phase TiO₂ with strong adhesion to the mesh branches, enabling improved dye loading and electron injection pathways. Optical studies revealed higher visible light absorption for the copper mesh compared to FTO in the visible range, further enhanced upon TiO₂ and Ru-based dye deposition. Electrochemical measurements showed that TiO₂/Cu mesh electrodes exhibited significantly higher photocurrent densities and faster photo response rates than bare Cu mesh, with dye-sensitized Cu mesh achieving the lowest charge transfer resistance in impedance analysis. Techno–economic and sustainability assessments revealed a decrease of 7.8% in cost and 82% in CO₂ emissions associated with the fabrication of electrodes as compared to conventional TCO electrodes. The synergy between high conductivity, transparency, mechanical durability, and a scalable, recyclable fabrication route positions this architecture as a strong candidate for next-generation dye-sensitized solar modules that are both flexible and sustainable. Full article

Finding high-performance and low-cost materials is essential for high-quality photocatalytic water purification to expand the spectral response and improve light utilization. In this paper, we used relatively inexpensive materials such as Ag and TiO₂. The influence of particle spacing, core radius, shell thickness, environmental refractive index, and incident light direction angle on the light absorption and scattering properties, local electric field enhancement, and photothermal effect of the Ag@TiO₂ core–shell nanosphere dimer is investigated by using the finite element method and the finite difference time domain. The formation mechanism of multipole resonance mode of the dimer is revealed by means of the multipole decomposition theory and the internal current distribution of the particles. The results show that light absorption and scattering of the dimer can be tuned within the visible light range by changing the particle spacing, core radius, and shell thickness. With the azimuth angle of incident light increases, the longitudinal local surface plasmon resonance (L-LSPR) mode will transform into the transverse local surface plasmon resonance (T-LSPR) mode, and the L-LSPR mode makes the dimer have better local electric field enhancement. Strong light absorption can easily cause a sharp increase in the temperature around the dimer, accelerating the rate of catalytic oxidation reactions and the elimination of bacteria and viruses in water. Strong light scattering causes a significant enhancement of the electric field between the particles, making the generation of hydroxyl and other active oxides more efficient and convenient. This work establishes a theoretical basis for designing efficient water purification photocatalysts. Full article

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management. Full article

Apple jam is a widely used all-season product. The quality of the jam is closely related to its sugar concentration, which affects its taste, texture, shelf life, and legal compliance with production requirements. Although traditional methods for measuring sugar, such as titration, enzymatic methods, and chromatography, are accurate, they are also invasive, destructive, and unsuitable for rapid screening. This study investigates a non-destructive and non-invasive alternative method that uses hyperspectral imaging (HSI) in combination with machine learning to estimate the sugar content in processed apple products. Eight cultivars were selected from the Central Asian region, recognized as the origin of apples and known for its rich diversity of apple cultivars. A total of 88 jam samples were prepared with sugar concentrations ranging from 25% to 75%. For each sample, several hyperspectral images were obtained using a visible-to-near-infrared (VNIR) camera. The acquired spectral data were then processed and analyzed using regression models, including the support vector machine (SVM), eXtreme gradient boosting (XGBoost), and a one-dimensional residual network (1D ResNet). Among them, ResNet achieved the highest prediction accuracy of R² = 0.948. The results highlight the potential of HSI and machine learning for a fast, accurate, and non-invasive assessment of the sugar content in processed foods. Full article

Ultraviolet radiation (UVR) is a well-established risk factor for ocular diseases; however, the ultraviolet-blocking properties of daily disposable contact lenses remain insufficiently characterized. This study evaluated thirteen commercially available lenses to determine their spectral transmittance across UV-B, UV-A, and visible light ranges using a UV–visible spectrophotometer. The oxygen permeability, central thickness, water content, and FDA material classification of each lens were documented, and oxygen transmissibility was subsequently calculated. A generalized linear mixed model (GLMM) was applied to identify predictors of spectral transmittance. All lenses demonstrated high visible light transmittance (>88%), but exhibited substantial variation in UV attenuation. While several lenses effectively blocked most UV radiation, others transmitted more than 70%. The analysis revealed that lens power was the most consistent predictor of spectral transmittance, with higher minus powers associated with reduced UV-blocking efficacy. Moisture content and material classification also influenced UV protection but had minimal effect on visible light transmission. In conclusion, daily disposable contact lenses vary considerably in their UV-blocking capabilities, and although lens power cannot be altered, consideration of material composition and UV transmittance properties may assist in selecting lenses that provide optimal ocular protection. Full article

Surface organic coatings (SOCs) composed of drying oils, resins, and bitumen were commonly applied to small Renaissance bronze sculptures to enhance their visual and physical properties, producing dark, lustrous surfaces that were both esthetic and protective. Yet, the identification of these coatings remains challenging due to aging, conservation interventions, and the damage caused by physical sampling. This study presents a reproducible, non-destructive protocol for characterizing SOCs on metal substrates using external reflection Fourier transform infrared spectroscopy (ER-FTIR) and fiber optic reflectance spectroscopy (FORS). Twenty-seven reference coating mock-ups of linseed oil, walnut oil, mastic resin, pine resin, and bitumen were stoved onto bronze coupons and artificially aged. Spectra were analyzed across the visible/near-infrared (VIS-NIR) (~400–1000 nm), short-wave-infrared (SWIR) (~1000–2500 nm), and mid-infrared (MIR) (~2.5–25 µm) ranges, with key diagnostic features identified for each component and blend, including primary absorptions, combination bands, and overtones. ER-FTIR proved highly effective in detecting oil–resin mixtures and later wax coatings through characteristic bands in the MIR, while FORS, enhanced by first-derivative processing, successfully differentiated triterpenoid and diterpenoid resins and identified multi-component SOCs in the SWIR region. The reference spectral database generated in this study is intended to serve as a comparative tool for future non-invasive analysis of organic coatings on metal surfaces and to demonstrate that ER-FTIR and FORS, used in tandem, offer a practical and scalable framework for the non-destructive identification of SOCs. Full article

Smart polyester materials with embedded near-infrared (NIR) functionalities offer a promising pathway for low-cost, covert tagging, and object identification. In this study we present the development and characterization of polyester packaging surfaces printed with spectrally matched twin dyes that are invisible under visible light but selectively absorbed in the NIR region. The dye patterns were applied using a Direct-to-Film transfer (DTF) method onto polyester substrates. To validate their optical behavior, we applied a dual measurement approach. Laboratory grade NIR absorbance spectroscopy was used to characterize the spectral profiles of the twin dyes in the 400–900 nm range. A custom photodiode-based detection system was constructed to evaluate the feasibility of low-cost, embedded NIR absorbance sensing. Results from both methods show correlation in absorbance contrast between the dye pairs, confirming their suitability for spectral tagging. The developed materials were evaluated in a real-world detection scenario using commercially available NIR cameras. Under dark field conditions with edge illuminated planar lighting, the twin dye patterns were successfully recognized through custom software, enabling non-contact identification and spatial localization of the NIR codes. This work presents a low-cost, scalable approach for smart packaging applications based on optical detection of actively illuminated twin dyes using accessible NIR imaging systems. Full article

Background/Objectives: Cystoid macular lesion (CML) is a treatable cause of central vision loss in inherited retinal diseases (IRDs). We aimed to determine the frequency of CML in a large Hungarian IRD cohort and examine associations with causative genes. Methods: This longitudinal, retrospective, monocentric study included patients with genetically confirmed IRD identified from our database. Targeted next-generation sequencing (351-gene panel) and comprehensive ophthalmic evaluation were performed, including best-corrected visual acuity (BCVA) and spectral domain optical coherence tomography (SD-OCT). CML was defined as intraretinal hyporeflective spaces with well-defined borders visible on at least two B-scans within the SD-OCT macular volume and was categorized as cystoid macular edema (CME) or non-CME. Results: We enrolled 430 patients with genetically confirmed IRDs. CML was detected in 93 eyes of 57 patients. Mean age at OCT was 36.6 ± 18.7 years (range, 3–76); 32 were male (56.1%). Inheritance patterns were autosomal recessive in 24 (42.1%), X-linked in 19 (33.3%), and autosomal dominant in 14 (24.6%). Frequently implicated genes were RS1 (12/57), USH2A (7/57), NR2E3 (7/57), PRPF31 (4/57), RPGR (4/57), and RHO (4/57). CME predominated in retinitis pigmentosa (32/57, 56%), with mean BCVA 0.44 ± 0.29 (decimal) and central retinal thickness (CRT) 401 ± 181 µm. Non-CME CML occurred in 25/57 (44%)—notably in X-linked retinoschisis and enhanced S-cone syndrome—with BCVA 0.40 ± 0.23 and CRT 465 ± 258 µm. BCVA did not correlate with CRT (r_S = 0.18). Conclusions: CML occurred in 13.2% of patients within a large Hungarian cohort of genetically confirmed IRDs. Patients with IRD—mainly RP—are at higher risk for CML. Gene therapy is promising for retinal diseases, but CMLs can compromise effectiveness. Reducing and managing CME before gene therapy corroborates retinal stability and the functional state essential for the proper delivery and penetration of corrective genes to the target cells. Full article

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

Reliable power generation from solar cells is critical for spacecraft operation. High-energy laser irradiation poses a significant threat, as it can potentially cause irreversible damage to solar cells, which is difficult to detect remotely using conventional techniques such as radar or optical imaging. Spectral detection offers a potential approach through unique “spectral fingerprints,” but the spectral characteristics of laser-damaged solar cells remain insufficiently documented. This study investigates the scattering spectral characteristics of triple-junction GaAs (Gallium Arsenide) solar cells subjected to nanosecond pulsed laser irradiation to establish spectral signatures for damage assessment. GaAs solar cells were irradiated at varying energy densities. Bidirectional Reflectance Distribution Function (BRDF) spectra (400–1200 nm) were measured. A thin-film interference model was used to simulate damage effects by varying layer thicknesses, thereby interpreting experimental results. The results demonstrate that as the laser energy density increases from 0.12 to 2.96 J/cm², the number of absorption peaks in the visible range (400–750 nm) decreases from three to zero, and the oscillation in the near-infrared range vanishes completely, indicating progressive damage to the GaInP (Gallium Indium Phosphide) and GaAs layers. This study provides a spectral-based approach for remote assessment of laser-induced damage to solar cells, which is crucial for satellite health monitoring. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

Melanin is one of the key light absorbers in skin and is responsible for the colour of the skin. This study evaluates the responsivity of different skin tones to white light within the visible spectral range of 300–700 nm on 12 participants. The results show that the peak amplitude of the reflected light signal decreased by 90% for darker skin tones, compared to 70% for lighter skin tones. There were also visible differences at the 460 nm and 570 nm wavelengths between the skin tones, suggesting that the standard one-glove-fits-all pulse oximeter might not be ideal. Full article