Search Results (931)

Roasting intensity and cultivar shape the physicochemical composition and sensory characteristics of date seed-based coffee alternatives. This study evaluated quality traits among eight date seed cultivars (Zahidi, Medjool, Deglet nour, Thoory, Halawi, Barhee, Khadrawy, Bau Strami) roasted at three intensities (light: 180 °C; medium: 200 °C; dark: 220 °C) using digital technologies, including near-infrared spectroscopy (NIR), electronic nose (e-nose), and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), supported by machine learning (ML) modeling. NIR spectra showed distinct chemical fingerprints for date seed powders and beverages, with key absorption bands from 1673–2396 nm and 1720–1927/2238–2396 nm, respectively. E-nose outputs showed higher volatile emissions in dark-roasted samples, particularly for ethanol and NH₃. GC-MS identified 25 volatile compounds, mainly pyrazines and furanic compounds. Pyrazine concentration was greatest in Bau Strami and Medjool cultivars, whereas Halawi and Thoory cultivars had greater content of furfural. Two ML classification models achieved high accuracy in classifying cultivars (NIR inputs: 99%; e-nose inputs: 98%) and roasting levels, while regression models (NIR inputs: R = 0.88; e-nose inputs: R = 0.90) effectively predicted volatile aromatic compounds obtained using GC-MS. Dark roasting resulted in a significant pH reduction and intensified browning, with furfural persisting as a stable aroma contributor. These findings highlight the potential of date seeds as a coffee alternative, with roasting level and cultivar selection influencing flavor profiles. The findings also demonstrate the utility of digital sensing technologies as an efficient, low-cost tool for rapid quality assessment and process optimization in the development of novel beverages. Full article

The aim of this study is to evaluate the usefulness of UV-Vis-NIR spectroscopy as a tool for detecting the adulteration of cold-pressed walnut oil and other edible oils (rapeseed, sunflower, and soybean oils) at varying percentages. The spectra were recorded between 200 and 1800 nm, but the analyses focused on 350–1650 nm due to high UV and NIR absorption. Color was determined in CIEL*a*b* coordinates to achieve the differences among the samples. The spectra were submitted to several pre-treatment (none, normalization, SNV, MSC, baseline/detrend, first/second derivative, and 1st-order smoothing) to improve the statistical model’s parameters. The differentiation of the samples was carried out using an unsupervised method (principal component analysis—PCA) and two supervised methods (linear discriminant analysis—LDA and partial least squares linear discriminant analysis—PLS-DA). Partial least squares regression (PLS-R) was used for predicting the degree of adulteration. Separation between the authentic and adulterated samples was visible in the PCA scores plot, primarily along the spectral regions of 420–500 nm (pigment-related absorption band) and 1150–1450 nm (lipid-associated band). PLS-DA was superior to DA for the discrimination of authentic/adulterated samples, with baseline spectra of 350–1650 nm yielding a 100% overall accuracy and near-perfect accuracy with MSC (98.48%). PLS-R was able to predict the adulteration level, depending on the pre-treatment applied. Full article

Background: Sport-related concussion is common in rugby union, yet female players remain underrepresented in research. This study examined seasonal changes in cerebral oxygenation, cardiac function, and concussion symptomology in adult female rugby players, and explored acute physiological responses following a single documented concussion. Methods: A total of 29 adult females (19 amateur rugby, 10 control) completed pre-, mid-, and end-season assessments. Measures included functional near-infrared spectroscopy (fNIRS) of the pre-frontal cortex, seismocardiography (SCG)-derived cardiac timing indices, and Sport Concussion Assessment Tool 6 (SCAT6). Group and time effects were analysed using general linear models and statistical parametric mapping. Typical error (TE) and its 90% confidence intervals (90% CI) were used to determine meaningful changes post-concussion. Results: Rugby players reported more SCAT6 symptoms (number: p = 0.006, η²_p = 0.23; severity: p = 0.020, η²_p = 0.17). They also had shorter systolic time (p = 0.002, η²_p = 0.19) and higher twist force values (p = 0.014, η²_p= 0.21) than controls. fNIRS revealed higher right-hemisphere oxyhaemoglobin (ΔO₂Hb) responses for both tasks (ps < 0.001, η²_p = 0.77 and η²_p = 0.80) and lower activation in specific prefrontal channels. No seasonal changes occurred in global oxygenation or frequency band activity. In the exploratory single-concussion case, symptomology, SCG twist force, ΔO₂Hb, and cardiac band power exceeded TE and its 90% CI at 5 days post-injury. Conclusions: The multimodal approach detected stable group-level physiology alongside localised cortical and cardiac differences, and acute changes following concussion. While these results highlight the potential of combined fNIRS and SCG measures to capture physiological disturbances, the small sample size and single-concussion case necessitate cautious interpretation. Further validation in larger, longitudinal cohorts is required before any biomarker utility can be inferred. Full article

The calorific value of coal is a key parameter for pricing, trade, and combustion management. Conventional bomb calorimetry provides accurate results but is time-consuming, labor-intensive, and destructive. Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, yet its application is limited by strong band correlations, nonlinear spectral responses, and the lack of interpretability in many predictive models. In this study, the Kolmogorov–Arnold Network (KAN) is applied to the prediction of coal calorific value, demonstrating its capability to describe nonlinear spectral relationships within an interpretable mathematical structure. Based on this framework, a Radial Basis Function KAN (RBF-KAN) is further developed by replacing the B-spline bases in the KAN with radial basis functions, allowing improved representation of localized and irregular spectral variations while maintaining model transparency. Using 671 coal-powder samples measured by a portable MicroNIR spectrometer, the RBF-KAN achieved an RMSE of 1.35 MJ/kg and an MAE of 0.92 MJ/kg under five-fold cross-validation, outperforming conventional regression models, deep neural networks, and other KAN variants. Analysis of RBF activations and spectral attribution maps indicates that the model consistently responds to characteristic O-H and C-H overtone regions, which correspond to known absorption features in coal. These results suggest that the RBF-KAN provides a practical and interpretable framework for on-site estimation of coal calorific value, complementing traditional calorimetric analysis. Full article

Soil nutrient detection in wetlands is critical for rapidly and effectively managing these ecosystems. Our objective was to provide a methodological framework for identifying optimal data processing methods and machine learning model for predicting soil organic carbon (SOC) and total nitrogen (TN) content using Vis–NIR spectroscopy, under the confounding influence of varying soil moisture. Soil samples (474) were collected from the Shaanxi Yellow River Wetland Provincial Nature Reserve with five moisture levels (0, 5, 10, 20, and 30%). Using a Vis–NIR spectroscopy system (ASD FS4 spectrometer), soil organic carbon (SOC) and total nitrogen (TN) were detected within the 350–2500 nm spectral range. Machine learning models were established using the Random Forest model (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR). The results indicated: (1) spectral reflectance values increased as soil moisture content decreased, with the 0% moisture model being consistently more accurate; (2) models for SOC and TN on first-derivative spectra had higher accuracy; and (3) the RF exhibited higher inversion accuracy and stability (R² = 0.30–0.69). (4) The SHAP analysis confirmed 1865 nm and 1419 nm as the most contributory bands for SOC and TN prediction respectively, validating the RF model’s spectral interpretation capability. Full article

In Eritrea, efforts are being made to tackle the widespread land degradation and promote natural resources and the agricultural sector. However, these efforts lack digital resources assessment, mapping, planning and monitoring. Thus, we developed soil organic carbon (SOC) predictor models for the Western Lowlands of the country, employing 6 machine learning models with different input variables (36, 27, 15, and 08) obtained following these variables selection strategies: (1) all proposed SOC predictor variables; (2) very high multicollinearity (≥0.900 **) reduction; (3) high multicollinearity (≥0.700 **) reduction; (4) the Boruta feature selection algorithm. The results revealed that SOC levels were generally low (mean = 0.43%). Grazing lands, rainfed croplands, and irrigated farmlands all exhibited similarly low SOC values, attributed to unsustainable land management practices that deplete soil nutrients. In contrast, natural forestlands exhibited significantly higher SOC concentrations, highlighting their potential for soil carbon sequestration. Among the tested models, the XGBoost algorithm using 27 covariates achieved the highest predictive performance (RMSE = 0.118, R² = 0.758, RPD = 2.252), whereas the multiple linear regression (MLR) model with 8 variables yielded the lowest performance (RMSE = 0.141, R² = 0.742, RPD = 1.883). Compared to the Boruta-based feature selection, the MLR, PLS, XGBoost, Cubist, and GB models showed performance improvements of 10.41%, 10.06%, 6.72%, 6.50%, and 3.15%, respectively. Rainfall emerged as the most influential predictor of SOC spatial variability in the study area. Other important predictors included temperature, soil taxonomy, SWIR2 and NIR bands from Landsat 8 imagery, as well as sand and clay contents. We conclude that reducing very high multicollinearity is essential for improving model performance across all tested algorithms, while reducing moderate multicollinearity is not consistently necessary. The developed SOC prediction models demonstrate robust predictive capabilities and can serve as effective tools for supporting soil fertility management, land restoration planning, and climate change mitigation strategies in the Western Lowlands of Eritrea. Full article

Accurate characterization of liquid manure properties, such as dry matter (DM), total nitrogen (TN), ammonium nitrogen (NH₄-N), and total phosphorus (TP), is essential for effective nutrient management in agriculture. This study investigates the use of near-infrared spectroscopy (NIRS) within the 941–1671 nm range, combined with advanced pre-processing and machine learning techniques to accurately predict the liquid manure properties. The predictive accuracy of NIRS was assessed by comparison with nuclear magnetic resonance (NMR) spectroscopy as a benchmark method. A number of 51 liquid manure samples were analyzed in the laboratory for the reference manure properties and scanned with NIRS and NMR. The NIR data underwent spectral pre-processing, which included two- and three-band index transformations and feature selection. Partial least squares regression (PLSR) and LASSO regression were employed to develop calibration models. According to the results, using cohort-tuned models, NIRS showed fair predictive accuracy for DM (R² = 0.78, RPD = 2.15) compared to factory-calibrated NMR (R² = 0.68, RPD = 0.81). Factory-calibrated NMR outperformed for chemical properties, with R² (RPD) of 0.89 (1.74) for TN, 0.97 (5.70) for NH₄-N, and 0.95 (2.64) for TP, versus NIRS’s 0.66 (1.68), 0.84 (2.45), and 0.84 (2.51), respectively. In this study with 51 samples, two- and three-band indices significantly enhanced NIRS performance compared to raw data, with R² increases of 34%, 57%, 25%, and 33% for DM, TN, NH₄-N, and TP, respectively. Feature selection efficiently reduced NIR spectral dimensionality without compromising the prediction accuracy. This study highlights NIRS’s potential as a portable tool for on-site manure characterization, with NMR providing superior laboratory validation, offering complementary approaches for nutrient management. Full article

Phase and band gap engineering of (CdS)_x(CuInS₂)_1−x nanomaterials is critical for their potential applications in photovoltaics and photocatalysis, yet it remains a challenge. Here, we report a precursor-mediated colloidal method for phase-control synthesis of alloyed (CdS)_x(CuInS₂)_1−x nanocrystals with tunable band gap. When CuCl, InCl₃, and Cd(AC)₂·2H₂O are used as the respective cation sources, wurtzite-structured alloyed (CdS)_x(CuInS₂)_1−x nanocrystals can be synthesized with a tunable optical band gap ranging from 1.56 to 2.45 eV by directly controlling the molar ratio of the Cd precursor. Moreover, using Cu(S₂CNEt₂)₂, In(S₂CNEt₂)₃, and Cd(S₂CNEt₂)₂ as cation sources results in alloyed (CdS)_x(CuInS₂)_1−x nanocrystals with a zinc-blende structure, demonstrating that the optical band gap of these nanocrystals can be compositionally tuned from 1.50 to 1.84 eV through precisely adjusting the molar ratio of Cd precursor. The results were validated through a comprehensive characterization approach employing XRD, TEM, HRTEM, STEM-EDS, XPS, UV-vis-NIR absorption spectroscopy, and Mott–Schottky analysis. Full article

Background/Objectives: It has become a major direction of research in affective computing to explore the brain-information-processing mechanisms based on physiological signals such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, existing research has mostly focused on feature- and decision-level fusion, with little investigation into the causal relationship between these two modalities. Methods: In this paper, we propose a novel emotion recognition framework for the simultaneous acquisition of EEG and fNIRS signals. This framework integrates the Granger causality (GC) method and a modality–frequency attention mechanism within a convolutional neural network backbone (MFA-CNN). First, we employed GC to quantify the causal relationships between the EEG and fNIRS signals. This revealed emotional-processing mechanisms from the perspectives of neuro-electrical activity and hemodynamic interactions. Then, we designed a 1D2D-CNN framework that fuses temporal and spatial representations and introduced the MFA module to dynamically allocate weights across modalities and frequency bands. Results: Experimental results demonstrated that the proposed method outperforms strong baselines under both single-modal and multi-modal conditions, showing the effectiveness of causal features in emotion recognition. Conclusions: These findings indicate that combining GC-based cross-modal causal features with modality–frequency attention improves EEG–fNIRS-based emotion recognition and provides a more physiologically interpretable view of emotion-related brain activity. Full article

The carob tree (Ceratonia siliqua L.) is receiving growing attention for its agro-industrial potential, particularly due to its seeds, which are the source of locust bean gum (LBG), a galactomannan-rich polysaccharide with wide applications in food and pharmaceutical industries. Efficient dehusking of carob seeds is critical to maximize LBG purity and yield, yet current industrial methods pose environmental concerns and lack robust quality control tools. In this study, we demonstrate the use of Diffuse Reflectance Spectroscopy (DRS) and Kubelka–Munk (KM) modeling as a rapid, non-destructive technique to assess dehusking efficiency. By combining spectral data from four complementary spectrometers (450–1800 nm), we identified key reflectance and absorbance features capable of distinguishing raw, industrially treated, and laboratory-dehusked seeds. Notably, our laboratory-treated seeds exhibited a considerably lower reflectance in the NIR plateau (800–1400 nm) compared to raw and industry-treated seeds, and their KM-reconstructed skin showed enhanced absorption bands at 960, 1200, and 1400 nm, consistent with more complete husk removal and improved light penetration. Principal Component Analysis revealed tighter clustering and lower variability in lab-processed seeds, indicating superior process reproducibility. These results establish DRS as a scalable, green analytical tool to support quality control and optimization in carob processing. Full article

Diamond traceability has been a major challenge for the gemological industry in recent decades. In this context, this paper presents new studies using UV-VIS-NIR spectroscopy to identify the traceability and geographical origin of diamonds. The aim of the work is to identify characteristic centers of fancy-color diamonds collected from Cullinan Mine, Democratic Republic of Congo (DRC), and the geographical regions with unknown origin. Depending on the origin of the diamonds, the UV-VIS-NIR spectra can be differentiated as follows: (i) the diamonds collected from Cullinan Mine show absorption bands assigned to N₁⁰, NV⁰, NV⁻, N₃V⁰, N₄V₂, and N₄V centers, which are accompanied by a vibronic structure localized between 415 and 394 nm (2.987–3.147 eV) and (ii) the diamonds from DRC show absorption bands attributed to N₁⁰, NV⁻, N₃V⁰, N₁⁺, and NVH centers. Using Raman spectroscopy, nitrogen concentration values of diamonds collected from the Cullinan mines and DRC between 41 and 185 ppm and 204–336 ppm, respectively, were reported. We prove that the simultaneous applicability of UV-VIS-NIR spectroscopy and Raman scattering as comparative tools for assessing diamond provenance can be a valuable strategy for an initial attribution of diamonds with unknown geographical origin, knowing the optical features of diamonds collected from Cullinan Mine and DRC. 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

Coastal lagoons are fragile and dynamic ecosystems that are particularly vulnerable to climate change and anthropogenic pressures such as urbanization and eutrophication. These vulnerabilities highlight the need for frequent and spatially extensive monitoring of water quality (WQ). While satellite remote sensing offers a valuable tool to support this effort, the optical complexity and shallow depths of lagoons pose major challenges for retrieving water column biogeochemical parameters such as chlorophyll-a ([chl-a]) and suspended particulate matter ([SPM]) concentrations. In this study, we develop and evaluate a robust satellite-based processing chain using Sentinel-2 MSI imagery over two French Mediterranean lagoon systems (Berre and Thau), supported by extensive in situ radiometric and biogeochemical datasets. Our approach includes the following: (i) a comparative assessment of six atmospheric correction (AC) processors, (ii) the development of an Optically Shallow Water Probability Algorithm (OSWPA), a new semi-empirical algorithm to estimate the probability of bottom contamination (BC), and (iii) the evaluation of several [chl-a] and [SPM] inversion algorithms. Results show that the Sen2Cor AC processor combined with a near-infrared similarity correction (NIR-SC) yields relative errors below 30% across all bands for retrieving remote-sensing reflectance R_rs(λ). OSWPA provides a spatially continuous and physically consistent alternative to binary BC masks. A new [chl-a] algorithm based on a near-infrared/blue R_rs ratio improves the retrieval accuracy while the 705 nm band appears to be the most suitable for retrieving [SPM] in optically shallow lagoons. This processing chain enables high-resolution WQ monitoring of two coastal lagoon systems and supports future large-scale assessments of ecological trends under increasing climate and anthropogenic stress. 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

The responsibility for risk assessment and user safety in forested and recreational areas lies with the property owner. This study shows that unmanned aerial vehicles (UAVs), combined with remote sensing and GIS analysis, effectively support the identification of high-risk trees, particularly those with reduced structural stability. UAV-based surveys successfully detect 78% of dead or declining trees identified during ground inspections, while significantly reducing labor and enabling large-area assessments within a short timeframe. The study covered an area of 6.69 ha with 51 reference trees assessed on the ground. Although the multispectral camera also recorded the red-edge band, it was not included in the present analysis. Compared to traditional ground-based surveys, the UAV-based approach reduced fieldwork time by approx. 20–30% and labor costs by approx. 15–20%. Orthomosaics generated from images captured by commercial multispectral drones (e.g., DJI Mavic 3 Multispectral) provide essential information on tree condition, especially mortality indicators. UAV data collection is fast and relatively low-cost but requires equipment capable of capturing high-resolution imagery in specific spectral bands, particularly near-infrared (NIR). The findings suggest that UAV-based monitoring can enhance the efficiency of large-scale inspections. However, ground-based verification remains necessary in high-traffic areas where safety is critical. Integrating UAV technologies with GIS supports the development of risk management strategies aligned with the principles of precision forestry, enabling sustainable, more proactive and efficient monitoring of tree-related hazards. Full article