Search Results (121)

Tea is one of the most widely consumed non-alcoholic beverages globally, yet its yield and quality are significantly impacted by herbivory from tea geometrids. To accurately detect herbivory stress in tea leaves, this study integrated metabolomics with visible-near-infrared spectroscopy (VIS-NIRS) to explore its in situ capabilities and underlying mechanisms. The results demonstrated that metabolomic data, combined with PCA-based linear dimensionality reduction, could effectively distinguish between tea leaves subjected to herbivory by different densities of tea geometrids. VIS-NIRS successfully identified herbivore-damaged leaves, achieving an optimal average classification accuracy of 0.857. Furthermore, VIS-NIRS was able to differentiate leaves subjected to herbivory on different days. The application of appropriate preprocessing techniques significantly enhanced temporal classification, achieving the highest average classification accuracy of 0.773. By integrating metabolomics and spectral band analysis, the spectral range of 800–2500 nm was found to more accurately identify leaves exposed to herbivory for a prolonged period. Compared to using the full spectrum, the model built within this wavelength range improved classification accuracy by 10%. In conclusion, this study provides a solid theoretical foundation for the in situ, rapid detection of tea geometrid herbivory stress in the field using VIS-NIRS, offering key technical support for future applications. Full article

Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article

The potato is the third most important crop in the world, and more than 375 million metric tonnes of potatoes are produced globally on an annual basis. Potato Virus Y (PVY) poses a significant threat to the production of seed potatoes, resulting in economic losses and risks to food security. Current detection methods for PVY typically rely on serological assays for leaves and PCR for tubers; however, these processes are labor-intensive, time-consuming, and not scalable. In this proof-of-concept study, we propose the use of unmanned aerial vehicles (UAVs) integrated with hyperspectral cameras, including a downwelling irradiance sensor, to detect the PVY in commercial growers’ fields. We used a 400–1000 nm visible and near-infrared (Vis-NIR) hyperspectral camera and trained several standard machine learning and deep learning models with optimized hyperparameters on a curated dataset. The performance of the models is promising, with the convolutional neural network (CNN) achieving a recall of 0.831, reliably identifying the PVY-infected plants. Notably, UAV-based imaging maintained performance levels comparable to ground-based methods, supporting its practical viability. The hyperspectral camera captures a wide range of spectral bands, many of which are redundant in identifying the PVY. Our analysis identified five key spectral regions that are informative in identifying the PVY. Two of them are in the visible spectrum, two are in the near-infrared spectrum, and one is in the red-edge spectrum. This research shows that early-season PVY detection is feasible using UAV hyperspectral imaging, offering the potential to minimize economic and yield losses. It also highlights the most relevant spectral regions that carry the distinctive signatures of PVY. This research demonstrates the feasibility of early-season PVY detection using UAV hyperspectral imaging and provides guidance for developing cost-effective multispectral sensors tailored to this task. Full article

In order to address the problems of in-plane rotation and fast motion during near-infrared (NIR) video target tracking, this study explores the application of capsule networks in NIR video and proposes a capsule network method based on background information and spectral position prediction. First, the history frame background information extraction module is proposed. This module performs spectral matching on the history frame images through the average spectral curve of the groundtruth value of the target and makes a rough distinction between the target and the background. On this basis, the background information of history frames is stored as a background pool for subsequent operations. The proposed background target routing module combines the traditional capsule network algorithm with spectral information. Specifically, the similarity between the target capsule and the background capsule in the spectral feature space is calculated, and the capsule weight allocation mechanism is dynamically adjusted. Thus, the discriminative ability of the target and background is strengthened. Finally, the spectral information position prediction module locates the center of the search region in the next frame by fusing the position information and spectral features of adjacent frames with the current frame. This module effectively reduces the computational complexity of feature extraction by capsule networks and improves tracking stability. Experimental evaluations demonstrate that the novel framework achieves superior performance compared to current methods, attaining a 70.3% success rate and 88.4% accuracy on near-infrared (NIR) data. Meanwhile, for visible spectrum (VIS) data analysis, the architecture maintains competitive effectiveness with a 59.6% success rate and 78.8% precision. Full article

Chemical doping is a well-established technique for increasing the electrical conductivity of polyconjugated polymers, and its effectiveness can be assessed through IR spectroscopy, thanks to the rise of the so-called IRAVs (infrared activated vibrations), which prove the formation of polarons on the polymer chain. While the mechanism of the IRAVs activation has been widely explored in the past, several peculiar features remain unclear. Changes in the Raman spectrum of doped polymers (RaAV, Raman activated vibrations) are widely used as well for monitoring the doping process, but the interpretation is often limited to purely empirical correlations. By means of an experimental campaign on doped regio-regular poly(3-hexylthiophene-2,5-diyl) (P3HT) samples in chloroform solution and on the solid samples cast from the same solutions, this paper presents for the first time a thorough comparative analysis of IRAVs and RaAVs, aiming at a unified description of the structure of doped P3HT. In particular, we will discuss the effect of the doping level on the vibrational features of the polymer and the dopant so that spectroscopic markers can be found to be used in the identification of the presence of ICT (integer charge transfer) complexes in different doping regimes. This study demonstrates that combining IR, Raman, and UV-Vis-NIR spectroscopies provides a powerful, complementary set of tools to diagnose not only the doping level but also the detailed molecular and supramolecular structure of the doped P3HT, useful for the development of structure/properties relationships in the perspective of the optimization of the charge transport performances. Full article

Extensive research has already been conducted on sapphires, yet there remains a notable absence of methods available to identify the provenance of pink-purple sapphires, particularly those originating from Vietnam and Madagascar. This study examined pink-purple sapphires from Vietnam and Madagascar by conducting basic gemological tests, microscopic observations, infrared spectroscopy, Raman spectroscopy, UV–Vis–NIR spectroscopy, and LA ICP MS, while also drawing comparisons with pink-red corundum from other locations. In appearance, the Vietnamese samples have a foggy appearance and orange iridescence, while the Madagascan samples show a relatively strong purple hue. The color origin analysis reveals that the absorption peaks of the ultraviolet spectrum caused by Cr³⁺ in the yellow-green and blue-purple regions account for the pink color of the Vietnamese and Madagascan samples. The lower UV wavelength position of the two main peaks in the Madagascan samples, as compared to the Vietnamese ones, indicates that Fe³⁺ d–d transitions, as well as transitions between Fe²⁺—Ti⁴⁺ and Fe³⁺—Ti³⁺ ions, enhance blue light transmission and cause the samples to tend towards a purple hue. Regarding inclusions, the Vietnamese samples are characterized by white and blue bands, cloudy inclusions, and extensive yellow-orange staining, whereby the cloudy inclusions give them their special appearance, and their calcite and apatite inclusions indicate that they come from marble-type deposits. The presence of many small-grained zircon formations, especially clusters, in the Madagascan samples indicates that they come from alkaline basalt. Chemical analysis confirmed the origin of the samples from the two locations. Compared with the pink-red corundum of the same marble type (Myanmar and Yunnan, China), the Vietnamese samples have lower V, Mg, and Ga contents and a higher Fe content. Compared with the pink-red corundum of the high-iron type (Thailand, Cambodia, and Tanzania), the Madagascan samples have lower Fe and higher Ga contents overall. This study possesses considerable significance in tracing and identifying the origin of pink-purple sapphires. Full article

This study evaluates the potential of integrating visible and near-infrared (visNIR) spectroscopy, color analysis, and firmness testing for non-destructive tomato quality assessment and shelf-life prediction. Tomato fruit (cv. HM1823) harvested at four ripening stages were monitored over 12 days at 22 °C to investigate ripening stage-specific variations in key quality parameters, including color (hue angle), firmness (compression), and nutritional composition (pH, soluble solids content, and titratable acidity ratio). Significant changes in these parameters during storage highlighted the need for advanced tools to monitor and predict quality attributes. Spectral data (340–2500 nm) captured using advanced and cost-effective portable spectroradiometers, coupled with chemometric models such as partial least squares regression (PLSR), demonstrated reliable predictions of shelf-life and nutritional quality. The near-infrared spectrum (900–1700 nm) was particularly effective, with variable selection methods such as genetic algorithm (GA) and variable importance in projection (VIP) scores enhancing model accuracy. This study highlights the promising role of visNIR spectroscopy as a rapid, non-destructive tool for optimizing postharvest management in tomato. By enabling real-time quality assessments, these technologies support sustainable agricultural practices through improved decision-making, reduced postharvest losses, and enhanced consumer satisfaction. The findings also validate the utility of affordable spectroradiometers, offering practical solutions for stakeholders aiming to balance cost efficiency and reliability in postharvest quality monitoring. Full article

Visible near-infrared (Vis-NIR) spectroscopy is widely used for rapid soil element detection, but calibration models are often limited by instrument-specific constraints, including varying laboratory conditions and sensor configurations. To address this, we propose a novel calibration transfer method that eliminates the conventional requirement of designating ‘master’ and ‘slave’ devices. Instead, spectral data from two spectrometers are fused to create a master spectrum, while independent spectral data serve as slave spectra. We developed an ensemble stacking model, incorporating partial least squares regression (PLSR), support vector regression (SVR), and ridge regression (Ridge) in the first layer, with BoostForest (BF) as the second layer, trained on the fused master spectrum. This model was further integrated with three calibration transfer methods: direct standardization (DS), piecewise direct standardization (PDS), and spectral space transfer (SST), to enable seamless application across slave spectra. Applied to soil total nitrogen (TN) detection, the method achieved an R²_P of 0.842, RMSEP of 0.017, and RPD of 2.544 on the first slave spectrometer, and an R²_P of 0.830, RMSEP of 0.018, and RPD of 2.452 on the second. These results demonstrate the method’s ability to simplify calibration processes while enhancing cross-instrument prediction accuracy, supporting robust and generalizable cross-instrument applications. 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

Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. For morphological analysis, a total of 25 soil profiles were chosen and made visible. In the dark room, an ASD Fieldspec portable spectroradiometer (350–2500 nm) was used to measure the spectrum. Based on how similar their surface spectra were, related soils were categorized. Ward’s method served as the basis for the grouping. Despite the fact that the VIS–NIR spectra of the surface soils from various land uses have a similar reflectance shape, it is still possible to compare the soil reflectance curves and the effects of the surface soils. As a result, three groups of soil curves representing various land uses were observed. Cluster analysis was performed on the reflectance data in four ranges (350–750, 751–1150, 1151–1850, and 1851–2500 nm). The groups derived from the soil surface ranges of 350–750 nm and 751–1150 nm were not the same as those derived from the ranges of 1151–1850 nm and 1851–2500 nm. The last two categories are strikingly comparable to various land uses with marginally similar features. Based on the ranges of 1151–1850 nm and 1851–2500 nm in surface spectral data, the dendrogram effectively separated and combined the profiles into two separate clusters. These clusters matched different land uses exactly. The results can be used to promote the widespread usage of in situ hyperspectral data sets for the investigation of various soil characteristics. 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

Background: This paper aimed to measure and characterize eleven monochromatic filters and twenty-two combinations used empirically to treat patients with visual dysfunctions to propose enhanced protocols based on solid evidence. Their wavelength, transmittance, and relative sensitivity were defined on the retinal cone cells. Methods: A double-beam UV-VIS-NIR spectrophotometer, VARIAN brand, Cary 5000 model, owned by the National Center of Metrology, with high precision and accuracy, was used to characterize all filters. Filters were purchased from Optomatters Corporation, Belgium. Results: When two or three filters are combined, their transmittance and relative sensitivity on the retinal cone cells decrease regardless of wavelength. As a result, the efficiency of combined filters may decrease during treatments. Additionally, most filters and combinations, regardless of the wavelength, transmit a considerable percentage of light from the red spectrum. A depressant is the best monochromatic filter, and Upsilon–Neurasthenic is the strongest combination to stimulate blue cone cells. In contrast, Stimulant and Delta–Theta are best for red and green cone cells. Mu–Delta and Mu–Theta can be interchangeable, as well as Alpha–Delta and Alpha–Theta. Conclusions: Results suggest that the current phototherapy treatment protocol must be deeply revised, and the number of filters and combinations should be reduced to reduce costs and time and boost efficiency. Full article

Quaternary stream sediments and beach black sand in north-western Saudi Arabia (namely Wadi Thalbah, Wadi Haramil and Wadi Al Miyah) are characterized by the enrichment of heavy minerals. Concentrates of the heavy minerals in two size fractions (63–125 μm and 125–250 μm) are considered as potential sources of “strategic” accessory minerals. A combination of mineralogical, geochemical and spectroscopic data of opaque and non-opaque minerals is utilized as clues for provenance. ThO₂ (up to 17.46 wt%) is correlated with UO₂ (up to 7.18 wt%), indicating a possible uranothorite solid solution in zircon. Hafnoan zircon (3.6–5.75 wt% HfO₂) is a provenance indicator that indicates a granitic source, mostly highly fractionated granite. In addition, monazite characterizes the same felsic provenance with rare-earth element oxides (La, Ce, Nd and Sm amounting) up to 67.88 wt%. These contents of radionuclides and rare-earth elements assigned the investigated zircon and monazite as “strategic” minerals. In the bulk black sand, V₂O₅ (up to 0.36 wt%) and ZrO₂ (0.57 wt%) are correlated with percentages of magnetite and zircon. Skeletal or star-shaped Ti-magnetite is derived from the basaltic flows. Mn-bearing ilmenite, with up to 5.5 wt% MnO, is derived from the metasediments. The Fourier-transform infrared transmittance (FTIR) spectra indicate lattice vibrational modes of non-opaque silicate heavy minerals, e.g., amphiboles. In addition, the FTIR spectra show O-H vibrational stretching that is related to magnetite and Fe-oxyhydroxides, particularly in the magnetic fraction. Raman data indicate a Verwey transition in the spectrum of magnetite, which is partially replaced by possible ferrite/wüstite during the measurements. The Raman shifts at 223 cm⁻¹ and 460 cm⁻¹ indicate O-Ti-O symmetric stretching vibration and asymmetric stretching vibration of Fe-O bonding in the FeO₆ octahedra, respectively. The ultraviolet-visible-near infrared (UV-Vis-NIR) spectra confirm the dominance of ferric iron (Fe³⁺) as well as some Si⁴⁺ transitions of magnetite (226 and 280 nm) in the opaque-rich fractions. Non-opaque heavy silicates such as hornblende and ferrohornblende are responsible for the 192 nm intensity band. Full article

Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. The objectives of this study were to identify ML models able to differentiate P. maximum cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. The experiment was carried out at the experimental area of the Forage Sector of the School Farm belonging to the Federal University of Mato Grosso do Sul (UFMS). The leaf samples of the cultivars Massai, Mombaça, Tamani, Quênia, and Zuri were collected from experimental plots in the field. Analysis was carried out on 120 leaf samples from the P. maximum cultivars using a VIS/NIR hyperspectral sensor. After obtaining the spectral data and separating them into bands, the data were submitted for ML analysis to classify the cultivars based on the spectral variables. The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. Two input models were evaluated in the algorithms: the entire spectrum band provided by the sensor (ALL) and another input configuration using the calculated bands. The reflectances from the P. maximum cultivars showed different behavior, especially in the green and NIR regions. RL and ANN algorithms using all information in the spectrum are able to accurately classify the cultivars, reaching accuracies above 70 for CC and above 0.6 for kappa and F-score. VIS/NIR leaf reflectance can be a powerful tool for low-cost, non-destructive, and high-performance analysis to distinguish P. maximum cultivars. Here, we achieved better model accuracy using only 40 leaf samples. In the present study, the J48 decision tree model proved to have good classification performance regardless of the sample size used, which makes it a strategic model for forage cultivar classification studies in smaller or larger datasets. Full article

Carbon nanomaterials are increasingly being integrated into modern research, particularly within the textile industry, to significantly boost performance and broaden application possibilities. This study investigates the impact of incorporating three distinct carbon-based nanofillers—carbon nanotubes (CNTs), carbon black (CB), and graphene (Gn)—into polyamide 6 (PA6) multifilament yarns. It explores how these nanofillers affect the physical, mechanical, and thermal properties of PA6 yarns and fabrics. By utilizing melt extrusion, the nanomaterials were uniformly distributed in the yarns, and knitted fabrics were subsequently produced for detailed analysis. The research offers critical insights into how each nanofiller improves the thermal behavior of PA6-based textiles, enabling the customization of their applications. FTIR spectroscopy revealed significant chemical interactions between polyamide and carbon additives, while DSC analysis showed enhanced thermal stability, particularly with the inclusion of graphene. The introduction of these nanomaterials led to increased absorbance and decreased transmittance in the UV-Vis-NIR spectrum. Additionally, Far-Infrared (FIR) emissivity and thermal effusivity varied with different concentrations, with optimal improvements observed at specific levels. Although thermal conductivity decreased with the addition of these nanomaterials, heat management experiments demonstrated varied effects on heat accumulation and cooling times, underscoring potential applications in insulation and cooling technologies. These findings enrich the existing knowledge on nanomaterial-enhanced textiles, providing valuable guidance for optimizing PA6 yarns and fabrics for use in protective clothing, sportswear, and technical textiles. The comparative analysis offers a thorough understanding of the relationship between carbon nanomaterials and thermal properties, paving the way for innovative advancements in functional textile materials. Full article