Search Results (80)

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring. Full article

Imaging techniques have revolutionized cultural heritage analysis, particularly for objects that cannot be sampled. This study investigated the utilization of spectral imaging for the identification of pigments in artifacts from the Arxiu Valencià del Disseny, in conjunction with other portable spectroscopy techniques such as XRF, Raman, FT-NIR, and FT-MIR. Four early 1930s watercolors were examined using point-wise elemental and molecular spectroscopic data for pigment classification. Initially, the data cubes obtained with the spectral camera were processed using various methods. The spectral behavior was analyzed pixel-point, and the reflectance curves were qualitatively compared with a set of standards. Subsequently, a computational approach was applied to the data cube to produce RGB, false-color infrared (IRFC), and principal component (PC) images. Algorithms, such as the Vector Angle (VA) mapper, were also employed to map the pigment spectra. Consequently, 19th-century pigments such as Prussian blue, chrome yellow, and alizarin red were distinguished according to their composition, combining the spatial and spectral dimensions of the data. Elemental analysis and infrared spectroscopy supported these findings. In this context, the use of reflectance imaging spectroscopy (RIS), despite its technical limitations, emerged as an essential tool for the documentation and conservation of design heritage. Full article

Recently, the development of nanoparticle pigments has attracted interest in chemical preparation due to their potential functional properties, such as phosphate-based pigments. The present research focuses on the feasibility of synthesising the BaCr₂(P₂O₇)₂ pigment under hydrothermal conditions. The effect of the microstructural features of ceramic pigments (the crystalline structure, morphology, and particle size) on their optical properties (colour and reflectance) was also studied. The BaCr₂(P₂O₇)₂ compound was prepared in different fluid media, including water and NaOH solutions (0.5–1.0 M), at several reaction temperatures (170–240 °C) and intervals (6–48 h). The single-phase BaCr₂(P₂O₇)₂ did not crystallise without by-products (BaCr₂O₁₀, BaCr₂(PO₇)₂) in water and the alkaline solutions, even at 240 °C for 48 h; in these fluids, the ionic Cr³⁺ species oxidised to Cr⁶⁺. In contrast, the BaCr₂(P₂O₇)₂ single-phase crystallisation was favoured by adding urea as a reductant agent (25.0–300.0 mmol). Monodispersed BaCr₂(P₂O₇)₂ fine particles with a mean size of 44.0 nm were synthesised at a low temperature of 170 °C for 6 h with 0.5 M NaOH solution in the presence of 50.0 mmol urea. The phosphate pigment particle grew to approximately 62.0 nm by increasing the treatment temperature to 240 °C. A secondary dissolution–recrystallisation achieved after 24 h triggered a change in the particle morphology coupled with the incrementation of the concentration of NaOH in the solution. The pyrophosphate BaCr₂(P₂O₇)₂ pigments prepared in this study belong to the green colour spectral space according to the CIELab coordinates measurement, and exhibit 67.5% high near-infrared (NIR) solar reflectance. Full article

This study presents a novel approach combining near-infrared (NIR) spectroscopy with multivariate calibration to develop simplified yet robust regression models for evaluating the quality of various edible oils. Using a reduced number of NIR wavelengths selected via the stepwise decorrelation method (SELECT) and ordinary least squares (OLS) regression, the models quantify pigments (carotenoids and chlorophyll), antioxidant activity, and key sensory attributes (rancid, fruity green, fruity ripe, bitter, and pungent) in nine extra virgin olive oil (EVOO) varieties. The dataset also includes low-quality olive oils (e.g., refined and pomace oils, supplemented or not with hydroxytyrosol) and sunflower oils, both before and after deep-frying. SELECT improves model performance by identifying key wavelengths—up to 30 out of 700—and achieves high correlation coefficients (R = 0.86–0.96) with low standard errors. The number of latent variables ranges from 26 to 30, demonstrating adaptability to different oil properties. The best models yield low leave-one-out (LOO) prediction errors, confirming their accuracy (e.g., 1.36 mg/kg for carotenoids and 0.88 for rancidity). These results demonstrate that SELECT–OLS regression combined with NIR spectroscopy provides a fast, cost-effective, and reliable method for assessing oil quality under diverse processing conditions, including deep-frying, making it highly suitable for quality control in the edible oils industry. Full article

The Cape Floristic Region (CFR) boasts rich biodiversity but faces threats from invasive species and land-use changes. Fynbos wetland vegetation within the CFR is under-mapped despite its crucial role in supporting biodiversity and maintaining hydrological cycles. This study assessed the potential of UAV VIS-NIR data, gathered during Spring and Summer, to identify the spectral characteristics of eleven Fynbos wetland species in a seep wetland. Spectral distances derived from reflectance data revealed distinct spectral clustering of plant species, highlighting which species could be distinguished from each other. UAV data also captured differences in reflectance across spectral bands for both dates. Spectral statistics indicated that certain species could be more accurately classified in Spring than in Summer, and vice versa. These findings underscore the efficacy of UAV multispectral data in analyzing the reflectance patterns of fynbos wetland species. Additionally, the sensitivity of UAV multispectral data to foliar pigment composition across different seasonal stages was confirmed. Lastly, species classification results demonstrated that a random forest classifier is well suited, with relative producer and user accuracies aligning with the derived spectral distances. The results highlight the potential of UAV imagery for monitoring these endemic species and creating opportunities for scalable mapping of Fynbos seep wetlands. Full article

The early detection of bronze disease is a significant challenge not only in conservation science but also in various industrial fields that utilize copper alloys (i.e., shipbuilding and construction). Due to the aggressive nature of this corrosion pathway, developing methods for its early detection is pivotal. The presence of copper trihydroxychlorides is the main key indicator of the ongoing autocatalytic process. Commonly used for pigment identification, reflectance imaging spectroscopy (RIS) or fiber optics reflectance spectroscopy (FORS) was recently employed for mapping atacamite distribution in extended bronze corrosion patinas. In this work, we detected the onset of bronze disease using visible–near-infrared (VIS-NIR) multispectral reflectography, which allowed for disclosing features that were poorly detectable to the naked eye. The image cube was analyzed using the spectral correlation mapper (SCM) algorithm to map the distribution of copper trihydroxychlorides. FORS and Raman spectroscopy were employed to characterize the patina composition and validate RIS data. A set of bronze samples, representative of Florentine Renaissance workshops, was specifically realized for the present study and artificially aged at different corrosion stages. Full article

Environmentally benign, highly stable oxides exhibiting desirable optical properties and high near-IR reflectance are being researched for their potential application as pigments. Mg_1−xM_xV₂O₆ (M = Mn, Cu, Co, or Ni) oxides with brannerite-type structures were synthesized by the conventional solid-state reaction method to study their optical properties. These series exhibit structural transitions from brannerite (C2/m) to distorted brannerite (P$\overline{1}$) and NiV₂O₆-type (P$\overline{1}$) structures. The average color of Mg_1−xM_xV₂O₆ compounds varies from reddish-yellow to brown to dark brown. The L*a*b* color coordinates reveal that Mg_1−xCu_xV₂O₆ and Mg_1−xNi_xV₂O₆ show more red hues in color with x = 0.4 and x = 0.5, respectively. The UV–Vis diffuse reflectance spectra indicate a possible origin for these results include the ligand-to-metal charge transfer (O²⁻ 2p-V⁵⁺ 3d), metal-to-metal charge transfer (from Mn²⁺ 3d/Cu²⁺ 3d/Co²⁺ 3d/Ni²⁺ 3d to V⁵⁺ 3d), band gap transitions, and d–d transitions. Magnetic property measurements revealed antiferromagnetic behavior for the compounds Mg_1−xM_xV₂O₆ (M = Mn, Cu, Co, and Ni), and an oxidation state of +2 for the M ions was deduced from their Curie–Weiss behavior. The system Mg_1−xMn_xV₂O₆ has a NIR reflectance in the range between 40% and 70%, indicating its potential to be utilized in the pigment industry. Full article

The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered. Spectral data emphasized genetic similarities and differences among the cultivars, capturing changes in the pigment content and macro components of the apricot samples. In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. In this study, prediction models were developed based on a multilayer perceptron artificial neural network (ANN-MLP) combined with the Levenberg–Marquardt learning algorithm. Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R² = 0.855) and DM (R² = 0.857), while the performance for TA was unsatisfactory (R² = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R² = 0.904; DM: R² = 0.918, TA: R² = 0.811), as confirmed by external validation. Moreover, the ANN allowed us to identify the most predictive input spectral regions for each model. The results showed the potential of Vis/NIR spectroscopy as an alternative to traditional destructive methods to monitor the qualitative traits of apricot fruits, reducing the time and costs of analyses. Full article

Multidrug-resistant Pseudomonas aeruginosa infections pose a critical challenge to healthcare systems, particularly in nosocomial settings. This drug-resistant bacterium forms biofilms and produces an array of virulent factors regulated by quorum sensing. In this study, metal-tolerant bacteria were isolated from a metal-contaminated site and screened for their ability to synthesize multifunctional nanocomposites (NCs). Rapid color changes in the reaction solution evidenced the biotransformation process. The potent isolated Bacillus cereus SASAK, identified via 16S rRNA sequencing and deposited in GenBank under accession number MH885570, facilitated the microbial-mediated synthesis of ZnO nanoparticles and silver-doped ZnO NCs. These biogenic nanocomposites were characterized using UV-VIS-NIR spectroscopy, FTIR, XRD, zeta potential, HRTEM, FESEM, and EDX analyses. At a sub-MIC concentration of 100 µg/mL, 2% Ag-ZnO NCs effectively inhibited virulent factor production and biofilm formation in P. aeruginosa without affecting bacterial growth. Notably, there was a significant reduction in violacein pigment (96.25%), swarming motility, and pyocyanin concentration (1.87 µg/mL). Additionally, biofilm formation (81.1%) and EPS production (83.9%) using P. aeruginosa were substantially hindered, along with reduced extracellular protease activity, as indicated by zone formation (from 2.3 to 1.8 cm). This study underscores the potential of Ag-ZnO NCs as promising agents for combating quorum sensing-mediated virulence in chronic infections caused by multidrug-resistant P. aeruginosa. Full article

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

Alfalfa is a widely grown forage with a high crude protein content. Clarifying the interactions between light quality and nitrogen level on yield and nitrogen metabolism can purposely improve alfalfa productivity in plant factories with artificial light (PFAL). In this study, the growth, quality, and nitrogen metabolism of alfalfa grown in PFAL were investigated using three nitrate-nitrogen concentrations (10, 15, and 20 mM, labeled as N₁₀, N₁₅, and N₂₀) and continuous light (CL) with two light qualities (red–blue and red–blue–green light, labeled as RB-C and RBG-C). The results showed that the adaptation performance of alfalfa to nitrogen concentrations differed under red–blue and red–blue–green CL. Plant height, stem diameter, leaf area, yield, Chl a + b, Chl a, Chl b, crude protein contents, and NiR activity under the RB-CN₁₅ treatment were significantly higher than RB-CN₁₀ and RB-CN₂₀ treatments. The RB-CN₂₀ treatment showed morphological damage, such as plant dwarfing and leaf chlorosis, and physiological damage, including the accumulation of proline, H₂O₂, and MDA. However, the difference was that under red–blue–green CL, the leaf area, yield, and Chl a + b, carotenoid, nitrate, and glutamate contents under RBG-CN₂₀ treatment were significantly higher than in the RBG-CN₁₀ and RBG-CN₁₅ treatments. Meanwhile, the contents of soluble sugar, starch, and cysteine were significantly lower. However, the crude protein content reached 21.15 mg·g⁻¹. The fresh yield, dry yield, stomatal conductance, leaf area, plant height, stem diameter, crude protein, GS, and free amino acids of alfalfa were positively correlated with increased green light. In addition, with the increase in nitrogen concentration, photosynthetic capacity, NiR, and GOGAT activities increased, promoting growth and improving feeding value. The growth, yield, photosynthetic pigments, carbon, nitrogen substances, and enzyme activities of alfalfa were significantly affected by the interaction between nitrogen concentration and light quality, whereas leaf/stem ratio and DPPH had no effect. In conclusion, RB-CN15 and RBG-CN₂₀ are suitable for the production of alfalfa in PFAL, and green light can increase the threshold for the nitrogen concentration adaptation of alfalfa. Full article

Inorganic cool pigments are widely used as cooling agents in residential coatings due to their ability to achieve near-infrared reflectance. These coatings can be designed to exhibit a variety of colors independent of their reflectivity and absorption properties. Recent studies have highlighted the development of novel near-infrared (NIR) blue pigments, with an increasing emphasis on environmentally sustainable options that demonstrate high NIR reflectivity. This trend highlights the importance of creating novel and eco-friendly NIR reflective blue pigments. This study presents the synthesis of cobalt aluminates with varying concentrations of coloring ions (Co²⁺), achieved through the recycling of aluminum can seals via chemical precipitation. The formation of the spinel phase was confirmed through X-ray diffraction (XRD), and a colorimetric analysis was performed in the CIEL*a*b* color space. The synthesized pigments exhibited high near-infrared solar reflectance, with R% values ranging from 34 to 54%, indicating their potential as energy-efficient color pigments for use in coatings. Full article

Invasive species have historically disrupted environments by outcompeting, displacing, and extirpating native species, resulting in significant environmental and economic damage. Developing approaches to detect the presence of invasive species, favorable habitats for their establishment, and predicting their potential spread are underutilized management strategies to effectively protect the environment and the economy. Spotted lanternfly (SLF, Lycorma delicatula) is a phloem-feeding planthopper native to China that poses a severe threat to horticultural and forest products in the United States. Tools are being developed to contain the spread and damage caused by SLF; however, methods to rapidly detect novel infestations or low-density populations are lacking. Vegetation spectroscopy is an approach that can represent vegetation health through changes in the reflectance and absorption of radiation based on plant physiochemical status. Here, we hypothesize that SLF infestations change the spectral and chemical characteristics of tree canopies. To test this hypothesis, we used a full range spectroradiometer to sample canopy foliage of silver maple (Acer saccharinum) and red maple (Acer rubrum) trees in a common garden in Berks County, Pennsylvania that were exposed to varying levels of SLF infestation. Foliar spectral profiles separated between SLF infestation levels, and the magnitude of separation was greater for the zero-SLF control compared with higher infestation levels. We found the red-edge and portions of the NIR and SWIR regions were most strongly related to SLF infestation densities and that corresponding changes in vegetation indexes related to levels of chlorophyll were influenced by SLF infestations, although we found no change in foliar levels of chlorophyll. We found no influence of SLF densities on levels of primary metabolites (i.e., pigments, nonstructural carbohydrates, carbon, and nitrogen), but did find an increase in the phenolic compound ferulic acid in response to increasing SLF infestations; this response was only in red maple, suggesting a possible species-specific response related to SLF feeding. By identifying changes in spectral and chemical properties of canopy leaves in response to SLF infestation, we can link them together to potentially better understand how trees respond to SLF feeding pressure and more rapidly identify SLF infestations. Full article

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of “Tochigi iW1 go” white strawberries and 150 samples of “Tochigi i27 go” red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R²_p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R²_p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system. Full article

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants. Full article