Search Results (536)

This study explores the effects of varying exposure times of microelement fertilization on hydrochemical parameters, plant growth, and nutrient content in an aquaponic system cultivating Capsicum annuum L. (pepper) with Cyprinus carpio (Common carp L.). It also investigates the potential of visible–near-infrared (VIS-NIR) spectroscopy to differentiate between treated plants based on their spectral characteristics. The findings aim to enhance the understanding of microelement dynamics in aquaponics and optimize the use of VIS-NIR spectroscopy for nutrient and stress detection in crops. The effects of microelement exposure on the growth and health of Cyprinus carpio (Common carp L.) in an aquaponic system are investigated, demonstrating a 100% survival rate and optimal growth performance. The findings suggest that microelement treatments, when applied within safe limits, can enhance system productivity without compromising fish health. Concerning hydrochemical parameters, conductivity remained stable, with values ranging from 271.66 to 297.66 μS/cm, while pH and dissolved oxygen levels were within optimal ranges for aquaponic systems. Ammonia nitrogen levels decreased significantly in treated variants, suggesting improved water quality, while nitrate and orthophosphate reductions indicated an enhanced plant nutrient uptake. The findings underscore the importance of managing water chemistry to maintain a balanced and productive aquaponic system. The increase in root length observed in treatments 2 and 6 suggests that certain microelement exposure times may enhance root development, with treatment 6 showing the longest roots (58.33 cm). Despite this, treatment 2 had a lower biomass (61.2 g), indicating that root growth did not necessarily translate into increased plant weight, possibly due to energy being directed towards root development over fruit production. In contrast, treatment 6 showed both the greatest root length and the highest weight (133.4 g), suggesting a positive correlation between root development and fruit biomass. Yield data revealed that treatment 4 produced the highest yield (0.144 g), suggesting an optimal exposure time before nutrient imbalances negatively impact growth. These results highlight the complexity of microelement exposure in aquaponic systems, emphasizing the importance of fine-tuning exposure times to balance root growth, biomass, and yield for optimal plant development. The spectral characteristics of the visible–near-infrared region of pepper plants treated with microelements revealed subtle differences, particularly in the green (534–555 nm) and red edge (680–750 nm) regions. SIMCA models successfully classified control and treated plants with a misclassification rate of only 1.6%, highlighting the effectiveness of the spectral data for plant differentiation. Key wavelengths for distinguishing plant classes were 468 nm, 537 nm, 687 nm, 728 nm, and 969 nm, which were closely related to plant pigment content and nutrient status. These findings suggest that spectral analysis can be a valuable tool for the non-destructive assessment of plant health and nutrient status. Full article

Different sulfates (Ca-, Mg, and Fe- sulfates) have been extensively detected on the Martian surface. As one of the Martian sulfates, the presence of ferrous sulfates will provide valuable clues about the redox environment, hydrological processes, and climatic history of ancient Mars. In this study, three hydrated ferrous sulfates were prepared in the laboratory by heating dehydration reactions. These samples were analyzed using X-ray Diffraction (XRD) to confirm their phase and homogeneity. Subsequently, Raman, mid-infrared (MIR) spectra, visible near-infrared (VNIR) spectra, and laser-induced breakdown spectroscopy (LIBS) were measured and analyzed. The results demonstrate that the spectra of three hydrated ferrous sulfates exhibit distinctive features (e.g., the v₁ and v₃ features of ${\mathrm{S}\mathrm{O}}_4^{2-}$ tetrahedra in their Raman and MIR spectra) that can offer new insights for identifying different ferrous sulfates on Mars and aid in the interpretation of in-situ data collected by instruments such as the Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC), SuperCam, and ChemCam, etc. Full article

To characterize the trace-element characteristics of chrysoberyl, we studied twenty-six chrysoberyl samples from various localities by using laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS), photoluminescence (PL), and ultraviolet–visible–near-infrared (UV–Vis–NIR) spectroscopy. Chemical analysis has confirmed the existence of trace elements, including Fe, Ti, Ga, Sn, B, Cr, and V. The phenomenon of ionic isomorphic substitution frequently occurs at lattice sites within chrysoberyl. Notably, the isomorphic substitution of Al³⁺ in octahedral sites is significant, with the primary substituting elements being Fe, Ti, Cr, V, Ga, and Sn. The PL spectra of chrysoberyl samples exhibit sharp peaks at 678 and 680 nm, which are attributed to Cr³⁺, even in samples in which the Cr concentration is below the detection limit of LA-ICP-MS. This demonstrates the high-sensitivity feature of PL spectroscopy. The UV–Vis–NIR spectra of chrysoberyl samples consistently exhibit a band at 440 nm, and strong double narrow bands near 367 nm and 375 nm are observed. These spectral features are associated with Fe³⁺ chromophores—specifically, Fe³⁺-Fe³⁺ pairs or clusters and Fe³⁺ ions, respectively. By combining LA–ICP–MS analysis and PL mapping on a sample exhibiting color zoning, it has been found that the darker sections contain a higher concentration of Cr compared to the lighter sections, while the concentrations of other elements remain largely consistent. In other words, subtle variations in Cr concentration may be the underlying cause of color zoning in chrysoberyl. Full article

Fusarium wilt of banana is a soil-borne vascular disease caused by Fusarium oxysporum f. sp. cubense. The rapid and accurate detection of this disease is of great significance to controlling its spread. The research objective was to explore rapid banana Fusarium wilt latency and onset detection methods and establish a disease severity grading model. Visible/near-infrared spectroscopy analysis combined with machine learning methods were used for the rapid in vivo detection of banana Fusarium wilt. A portable visible/near-infrared spectrum acquisition system was constructed to collect the spectra data of banana Fusarium wilt leaves representing five different disease grades, totaling 106 leaf samples which were randomly divided into a training set with 80 samples and a test set with 26 samples. Different data preprocessing methods were utilized, and Fisher discriminant analysis (FDA), an extreme learning machine (ELM), and a one-dimensional convolutional neural network (1D-CNN) were used to establish the classification models of the disease grades. The classification accuracies of the FDA, ELM, and 1D-CNN models reached 0.891, 0.989, and 0.904, respectively. The results showed that the proposed visible/near infrared spectroscopy detection method could realize the detection of the incubation period of banana Fusarium wilt and the classification of the disease severity and could be a favorable tool for the field diagnosis of banana Fusarium wilt. Full article

Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions. Full article

Transition metal dichalcogenides (TMDCs), particularly molybdenum disulfide (MoS₂), have gained significant attention in the field of optoelectronics and photonics due to their unique electronic and optical properties. The integration of TMDCs with plasmonic materials allows to tailor the optical response and offers significant advantages for photonic applications. This study presents a novel approach to synthesize MoS₂-Au nanocomposites utilizing femtosecond laser ablation in liquid to achieve tunable optical properties in the near-infrared (NIR) region. By adjusting ablation and fragmentation protocols, we successfully synthesize various core–shell and core–shell–satellite nanoparticle composites, such as MoS₂/MoS_xO_y, MoS_xO_y/Au, and MoS₂/MoS_xO_y/Au. UV-visible absorption spectroscopy unveils considerable changes in the optical response of the particles depending on the fabrication regime due to structural modifications. Hybrid nanoparticles exhibit enhanced photothermal properties when subjected to NIR-I laser irradiation, demonstrating potential benefits for selective photothermal therapy. Our findings underscore that the engineered nanocomposites not only facilitate green synthesis but also pave the way for tailored therapeutic applications, highlighting their role as promising candidates in the field of nanophotonics and cancer treatment. Full article

The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400–2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes’ activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H₂O₂] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H₂O₂, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products. Full article

Plasmon chirality has garnered significant interest in sensing application due to its strong electromagnetic field localization and highly tunable optical properties. Understanding the effects of mode coupling in chiral structures on chiral optical activity is particularly important for advancing this field. In this work, we numerically investigate the circular dichroism (CD) of elliptical nanodisk dimers arranged in an up-and-down configuration with a specific rotation angle. By adjusting the inter-particle distance and geometric parameters, we introduce the coupling between dipole and electric hexapole modes, forming an extended Born–Kuhn model that achieves strong CD. Our findings show that the coupling of dipole modes with electric hexapole modes in elliptical nanodisks can also show obvious Fano resonance and a strong CD effect, and the structure with the largest Fano asymmetry factor shows the highest CD. In addition, CD spectroscopy is highly sensitive to changes in the refractive index of the surrounding medium, especially in the visible and near-infrared regions, highlighting its potential for application in high-sensitivity refractive index sensors. Full article

Herein, composite films were produced by incorporating different amounts (1, 3, 5, and 7%) of barium titanate nanoparticles into the thermoplastic polyurethane matrix using a solution casting method. This study examined the impact of the presence and concentration of a barium titanate additive on morphologic properties, mechanical performance, thermal stability, solar behavior, and wettability of produced film samples. The films were characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, thermal gravimetric analysis, scanning electron microscope, ultraviolet-visible near-infrared spectrophotometer, water contact angle, and tensile strength measurements. In the present study, the mass loss of samples containing 7% barium titanate was 24% lower than that of the pure polyurethane reference. The increase of barium titanate rate added to polyurethane enhanced the solar reflectance property of the films, including the near-infrared region. As a prominent result, the transmittance value decreased significantly compared to the reference in the ultraviolet region, and it dropped to 3% for the highest additive concentration. The contact angle values of polyurethane films increased by 11–40% depending on the barium titanate addition ratio. The nano additive also positively affected the mechanical performance of the reference polyurethane film by slightly increasing the tensile strength values. Full article

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R² of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R² of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R² of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection. Full article

Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectroscopic methods in malaria research. From the identification of reliable biomarkers to the development of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This review will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leveraging the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global efforts towards the eradication of this devastating disease. Full article

The microstructure of aluminum thin films, including the grain morphology and surface roughness, are key parameters for improving the thermal or electrical properties and optical reflectance of films. The first step in optimizing these parameters is a thorough understanding of the grain growth mechanisms and film structure. To investigate these issues, thin aluminum films with thicknesses ranging from 25 to 280 nm were coated on SiO_x/Si substrates at ambient temperature under high-vacuum conditions and a low argon pressure of 3 × 10⁻³ mbar (0.3 Pa) using the radio frequency magnetron sputtering method. Quantitative analyses of the surface roughness and nanograin characteristics were conducted using atomic force microscopy (AFM), transmission electron microscopy (TEM), and X-ray diffraction. Changes in specular reflectance were measured using ultraviolet–visible and near-infrared spectroscopy. The low roughness values obtained from the AFM images resulted in high film reflectivity, even for thicker films. TEM and AFM results indicate monomodal, randomly oriented grain growth without a distinct columnar or spherical morphology. Using TEM cross-sectional images and the dependence of the grain size on the film thickness, we propose a grain growth mechanism based on the diffusion mobility of aluminum atoms through grain boundaries. Full article

Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78%. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm². This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes. Full article

This paper reports on the successful preparation of a low absorption–emission thermal control coating on the surface of LAZ933 magnesium–lithium alloy using the micro-arc oxidation method. This study analyzed the microstructure, phase composition, and thermal control properties of the coating using Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), UV–visible near-infrared spectroscopy (UV-VIS-NIR) and infrared emissivity measurements. The results indicate that the hemispherical emissivity of the coating remains unaffected with an increase in temperature and holding time, while the solar absorption ratio gradually increases. The thermal control performance of the coating after a high-temperature experiment was found to be related to the diffusion of the Li metal element in the magnesium lithium alloy matrix, as determined by X-ray photoelectron spectroscopy (XPS), flame graphite furnace atomic absorption spectrometry (GFAAS) and Glow Discharge Optical Emission Spectroscopy (GD-OES). As the holding time is extended, the coating structure gradually loosens under thermal stress. The Li metal element in the substrate diffuses outward and reacts with O₂, H₂O and CO₂ in the air, forming LiO₂, LiOH, Li₂CO₃ and other products. This reaction affects the coating’s solar absorption ratio in the end. Full article

Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis–SWNIR) spectroscopy (400–1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis–SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications. Full article