Search Results (1,028)

Wood is a material frequently used in the production of alcoholic beverages. Oak barrels have been used to store wines, beers, and spirits for generations. Nowadays, beverages are increasingly matured in the presence of staves and chips from various wood species. The aim of this article was to describe the impact of different wood species and their thermal processing conditions on the quality of alcoholic beverages. The article describes the chemical composition of wood and the compounds formed during toasting at various temperatures. It also lists the volatile compounds extracted from wood for alcoholic beverages, along with their sensory thresholds and their impact on olfactory sensations. Attention was drawn to potentially harmful substances formed during wood toasting, including aromatic hydrocarbons and polycyclic aromatic hydrocarbons. The amounts of compounds extracted from wines, beers, and spirits from different wood species and toasted under different conditions were compared. Quercus barrels contribute to higher lactone concentrations in beverages, which have a coconut aroma. Cherry, acacia, and ash wood increase the concentration of volatile phenols in beverages. The use of staves and chips shortens the maturation time and facilitates the design of beverages with specific sensory characteristics. Full article

Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectroscopy and machine learning. First, a physically informed preprocessing strategy, integrating adaptive truncation (>1749 cm⁻¹) and first-derivative filtering, is developed to extract a 1309-dimensional composite feature matrix. This step effectively decouples non-linear fluorescence and converts physical detector saturation into highly discriminative features. To mitigate data leakage, the system utilizes a cross-validated Random Forest engine for Stage-1 closed-set discriminative screening. Subsequently, it cascades a high-dimensional One-Class Support Vector Machine (OCSVM) for Stage-2 open-set non-linear boundary verification in the Reproducing Kernel Hilbert Space. This design avoids the “variance trap” of traditional linear dimensionality reduction (e.g., PCA), preserving weak but critical secondary metabolite signals. Under a controlled OOD benchmarking scenario involving three taxonomically and chemically similar substitute species, the optimized Stage-1 engine maintains a 91.67% closed-set accuracy on known species. Crucially, Stage-2 verification achieves an open-set detection AUROC of 0.9722 and limits the FPR95 to 3.33%. Feature importance mapping indicates that the model effectively incorporates macroscopicoptical surrogate features (e.g., fluorescence decay boundaries) for decision-making. Overall, this study offers a robust, controlled non-destructive approach for real-world wood authenticity verification. Full article

Availability of phosphorus (P) is thought to limit bole growth in wet tropical forests, raising concern that removing P through repeated logging in P-limited stands may be unsustainable. Motivated by a study in Indonesia, we analyzed Olsen extractable and total soil P in the upper 10 cm in paired samples we collected under vs. near decaying boles of two contrasting species in a wet tropical forest in Puerto Rico. Guarea guidonia had higher wood and leaf P concentrations than Dacryodes excelsa. G. guidonia colonized valleys with higher soil P concentrations than ridge sites dominated by D. excelsa. We used two age cohorts of trees > 30 cm diameter, felled by hurricanes Hugo in 1989 (11 years old) and Georges in 1998 (1.5 years old), but soil P did not differ with age. Soil Olsen P concentrations were significantly higher under versus away from boles of both species. Paradoxically, augmentation of soil P was greater under boles of D. excelsa than G. guidonia despite having lower wood P. Soil % C and Olsen P were strongly positively correlated in D. excelsa but not in G. guidonia, suggesting that regulation of soil P-availability differs between ridges and valleys. Both soil C and P may be critical for maintaining soil fertility on ridges in a wet tropical forest. Our results are discussed in the context of prior experiments at our site, including two where bole growth increased with wood addition and/or decreased after removal of woody debris. These studies in Puerto Rico, together with others elsewhere, suggest that reduced forest productivity could potentially result from repeated logging of forest stands on ridges with low P-availability in humid tropical areas since decaying wood could directly and indirectly maintain P-availability in sites with low soil P-availability. We suggest several hypotheses on P-cycling in montane humid tropical forests that need further research to elucidate mechanisms controlling soil P-availability and identify sites where repeated logging is likely to be unsustainable. Full article

In the sustainability framework, valorization of organic by-products as reservoirs of phytochemicals useful for human health represents a hot topic. Therefore, this study evaluated Norway spruce bark and twigs (NSB, NST), chestnut tree wood (CTW), and pomegranate fruit waste/pomace (PFW) as sources of bioactive compounds by employing green technologies. Microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), applied individually or sequentially, were optimized by modulating solvent composition, temperature, time, microwave power, and ultrasound amplitude. Hydroalcoholic extraction (50% ethanol) combined with MAE yielded the highest phenolic recovery and antioxidant activity across all matrices. PFW exhibited the highest antioxidant activity assessed through FRAP, ORAC, and DPPH assays. Phytochemical profiling by HPLC-DAD identified stilbenes in spruce extracts, ellagic acid in chestnut wood, and ellagic acid and punicalagins in pomegranate waste as major bioactive constituents. Additionally, NSB and PFW exhibited α-amylase inhibitory activity. Antimicrobial testing demonstrated dose-dependent activity against Gram-positive (Staphylococcus epidermidis and Bacillus subtilis) and Gram-negative (Pseudomonas stutzeri) strains, with PFW exhibiting the strongest inhibition and NSB displaying broad-spectrum effects. Total phenolic content changed moderately after 21 days of storage. These results demonstrate that sustainable extraction enables efficient recovery of bioactive compounds from plant by-products, supporting their further functional, dietary, and medicinal applications. Full article

Terrestrial Laser Scanning (TLS) can provide detailed three-dimensional structural information for individual trees and has become an important data source for tree species classification. However, most existing models are trained using leaf-on point clouds and therefore tend to rely heavily on leaf distribution and crown appearance. When the input changes from leaf-on point clouds to woody-dominated representations, classification performance often declines. To address this issue, this study proposes a mixed-input tree species classification framework for six typical temperate broadleaf tree species. First, a KPConv-based wood–leaf separation model was used to extract woody point sets from leaf-on TLS point clouds, thereby generating woody-only representations for subsequent classification. Second, a multi-task learning network based on DGCNN was constructed. In addition to the main task of tree species classification, an auxiliary task for input-representation discrimination was introduced to enhance the model’s adaptability to different input forms. Experiments were conducted using a dataset composed of local TLS samples from China and publicly available single-tree point clouds from the BioDiv dataset. The results show that the proposed method achieved an overall accuracy of 94.3% on the mixed test set of six typical broadleaf tree species, with average Precision, Recall, and F1 values of 94.3%, 93.6%, and 93.9%, respectively. These results indicate that integrating woody structural representations with multi-task learning can effectively alleviate overreliance on leaf-on appearance features and improve classification robustness under different input representations. Full article

Uranium is a critical raw material for the nuclear industry. Given the vast uranium reserves in seawater, the development of efficient adsorbents is central to extraction technologies. Polyamidoxime (PAO)-based adsorbents are widely utilized due to their high affinity for uranium; however, traditional PAO materials often suffer from low mechanical strength and poor recyclability. To address these limitations, this study utilized natural balsa wood as a substrate. A three-dimensional porous cellulose skeleton (DES-W) featuring high porosity, hydrophilicity, and retained mechanical strength was constructed by partially removing lignin using a deep eutectic solvent (DES). Subsequently, polyamidoxime was loaded onto the inner walls of the DES-W via vacuum impregnation, resulting in a polyamidoxime-functionalized wood-based adsorbent (PAO-WA). The results indicated that PAO-WA achieved an equilibrium adsorption capacity of 45.31 mg/g at pH 6.0 with an initial uranium concentration of 50 mg/L, representing a twofold increase compared to the unmodified DES-W. The adsorption kinetics and isotherms followed the pseudo-second-order and Langmuir models, respectively, suggesting a mechanism dominated by monolayer chemisorption. Mechanism analysis confirmed that uranyl ions were primarily captured via coordination with nitrogen and oxygen atoms in the amidoxime groups, with residual carboxyl groups in the wood contributing to the adsorption process. This work offers a novel strategy for developing efficient, environmentally friendly, and mechanically robust adsorbents for uranium extraction from seawater. Full article

Steroid hormones (SHs) have a high estrogenic potential, and urban wastewater is one of their main ways into the aquatic environment. Constructed wetlands (CWs) are considered one of the most sustainable alternatives for the treatment of wastewater from small communities. However, the use of gravel and sand implies a significant environmental impact associated with their extraction and transport. A more sustainable alternative is the use of plant residues, as they are abundant, inexpensive, and readily available, and they can improve the efficiency of hormone removal through sorption. Thus, the sorption of 15 SHs was studied on conventional, mineral substrates (gravel, sand, and volcanic ash) and alternative vegetal wastes, i.e., mulches from giant reed, palm tree, balsa wood, and pine needles. These materials were characterized by determining their Point of Zero Charge (pH_PZC), ash content, content of leachable polycyclic aromatic hydrocarbons (PAH) and heavy metals, total surface area (BET), and pore characteristics. Results indicated that SH sorption on the mineral substrates was quite low, in most cases less than 10–15%. However, in the mulches it reached between 50 and 95%, except for corticosteroids (11–43%). The pseudo-second-order kinetics provided the best fit in all cases, with R² values between 0.97 and 0.9999. Experiments with a contact time of 7 days showed that the palm tree was the only substrate that completely removed the three corticosteroids studied (cortisone, prednisone, and prednisolone). Additionally, a significant correlation was observed between removal due to sorption (%) and log octanol–water partition coefficient (log Kow). Freundlich isotherm provided a higher number of best fits than Langmuir. Lastly, to compare sand with palm mulch under more realistic experimental conditions, four lab-scale CWs (two with palm mulch and two with sand, with/without plants) were studied. The sand-based CWs achieved faster SH percentage removals, while after 24 h, SH mass removals were significantly higher in the palm mulch-based CWs. Full article

Multivariate carbon and oxygen stable isotope analyses combined with chemometric methods were employed to investigate Aquilaria sinensis samples collected from six major regions in China (Honghe Hani and Yi Autonomous Prefecture and Xishuangbanna Dai Autonomous Prefecture in Yunnan Province; Zhongshan City and Maoming City in Guangdong Province; and Danzhou City and Chengmai County in Hainan Province). Isotopic δ-values were analyzed across different wood parts (longitudinal and north–south orientations), chemical fractions (de-extracted wood and α-cellulose), and geographical origins. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree, and Random Forest were applied to screen and classify the samples. Four discriminant models were successfully established, achieving a maximum accuracy of 85.7% for distinguishing Aquilaria sinensis from different regions, and 88.1% for discrimination at the provincial level. These results demonstrate that stable isotope signatures, when combined with chemometrics, provide a reliable technical approach for the traceability of incense wood and offer a reference framework for verifying the authenticity of Agarwood and related plant-derived materials. Full article

Bio-oil has been obtained from biomass undergoing pyrolysis to yield a complex mixture of organic compounds from different classes. The high content of oxygenated hydrocarbons distinguishes bio-oil from fossil-derived oils with similar properties. Bio-oil can also be used as a feedstock for chemicals due to its rich phenolic composition. Phenolic compounds possess significant industrial value and have been used in industrial sectors for the manufacture of antioxidants, resins, pharmaceuticals, and plastics. Although confirmatory analysis of these compounds is important, it has already been reported in the literature through chromatography hyphenated to mass spectrometry. Thus, this study aimed to obtain a fast and simple screening of suspect phenolic compounds in bio-oil obtained from lignocellulosic biomass (pine wood residue, sugarcane straw, and sugarcane bagasse). Instrumental conditions were optimized for negative electrospray ionization quadrupole time-of-flight mass spectrometry (ESI(−)Q-TOF MS) for screening compounds present in the aqueous phase of bio-oils obtained by pyrolysis of lignocellulosic biomass. A simple extraction method was used to prepare the samples for screening by ESI(−)Q-TOF MS. A total of 21 compounds (primary phenolics) were identified. Full article

Plywood has emerged as a key sustainable material in modern building. Yet, ensuring its consistent performance requires rigorous quality control of the rotary-cut veneers used in its manufacture. This task is complicated by the high-speed nature of industrial conveyors, where motion blur and the complex, varying textures of eucalyptus wood drastically reduce the effectiveness of real-time surface inspection. This study proposes an intelligent, real-time defect detection system specifically optimized for the diverse defect morphology of eucalyptus veneers. A lightweight model, YOLOv11-DMS-Veneers, was developed by integrating MobileNetV4 as the backbone, a Dynamic Head for multi-scale feature extraction, and a Shape-IoU loss function to precisely localize irregular defects like cracks and knots. Additionally, an ERD video enhancement framework (combining ESRGAN, RIFE, and DnCNN) was implemented to mitigate motion blur in dynamic environments. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 96.0% and a Precision of 95.7% with a low computational cost of only 4.5 GFlops, significantly outperforming traditional algorithms. Notably, the detection precision for challenging linear cracks reached 93.9%. In dynamic tests at conveyor speeds up to 24 m/min, the video enhancement strategy increased the average detection confidence by 0.288, maintaining a maximum confidence of 0.890. This technology offers a robust solution for the automated quality control of eucalyptus veneers, facilitating the production of high-performance plywood and advancing the efficient application of engineered wood in the building industry. Full article

This study investigated the surface characteristics and wettability behaviour of grey poplar (Populus × canescens) compared with spruce (Picea abies) in order to evaluate its potential as an alternative raw material for bonded structural wood products. Surface roughness was analysed on freshly planed radial surfaces using amplitude and functional roughness parameters, complemented by multivariate factor analysis and dynamic contact angle measurements. The results showed that grey poplar sapwood exhibited roughness values comparable to spruce (R_a ≈ 6–7 μm; R_z ≈ 35–40 μm). Grey poplar heartwood showed slightly higher roughness and greater variability, which can be attributed to its heterogeneous anatomical structure characterised by larger vessel elements and higher extractive content. Hybrid roughness parameters indicated favourable bonding-related surface characteristics in sapwood due to lower R_pk values, suggesting fewer protruding fibres, while higher R_vk values reflected the diffuse-porous anatomical structure of poplar. Static contact angle measurements revealed higher initial values for grey poplar (37.9° for heartwood and 41.9° for sapwood) compared with spruce (31.7°), indicating lower initial wettability with polar liquids. However, dynamic measurements demonstrated faster early-stage spreading in grey poplar heartwood (Δθ = 26.1° within the first second) compared with sapwood (16.8°) and spruce (17.5°), suggesting that vessel-driven capillary uptake may facilitate liquid penetration once wetting begins. Overall, the results indicate that grey poplar—particularly its sapwood fraction—exhibits surface characteristics comparable to spruce after planing. Despite slightly lower initial wettability, its spreading behaviour and surface morphology indicate favourable conditions for adhesive interaction. These findings support the potential use of grey poplar as an alternative raw material for laminated structural products such as glulam or bonded panels, provided that adhesive application parameters are properly adjusted. Full article

This study investigates how key planning variables—the number of wood assortments, the geometric shape of clear-cut areas, and the extraction (forwarding) distance—influence greenhouse gas (GHG) emissions. Twelve plots formed a heterogeneous sample with similar site type and soil moisture conditions. A Komatsu 931 harvester and a 855 forwarder, driven by the experienced operators, were used to ensure consistency in operator skill. For each plot, the isoperimetric quotient was computed to quantify how plot shape correlated with labor hours, fuel consumption, and the resulting volume of GHG emitted. The number of assortments extracted per plot ranged from three to fourteen product groups. The results show that plots with more complex shapes require significantly more operator time and fuel. Increasing the number of assortments amplifies handling time and fuel use. Longer extraction distances further exacerbate the emissions. These findings underscore the importance of integrating spatial geometry and wood assortment planning into harvest scheduling to enhance productivity and reduce the carbon footprint of forest operations. Recommendations for practitioners include prioritizing more compact treatment units, optimizing assortment grouping, and minimizing extraction distances as key strategies for precision forestry. Full article

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS–AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%–100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS–AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems. Full article

The valorization of agro-industrial byproducts as sources of functional polysaccharides is a promising strategy for developing sustainable materials. In this study, cellulose was extracted and purified from rice husk and apple pomace through sequential alkaline and bleaching treatments. Then it was chemically modified via TEMPO-mediated oxidation to obtain cellulose nanofibers (TOCNFs) with cellulose yields ranging from 23.8 to 32.4% for rice husk and 9.3–13.8% for apple pomace. Owing to its higher recovery and structural regularity, rice husk was selected for surface modification with 3-aminopropyltriethoxysilane (APTES). The resulting TOCNFs exhibited an average width of 8 nm and a carboxyl content of 0.48 mmol g⁻¹. Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and nitrogen determination (1.72 mg g⁻¹) confirmed the presence of aminosilane functionalities. APTES-modified TOCNFs were incorporated as active components to develop hybrid poly(vinyl acetate) (PVA) adhesives synthesized via in situ heterogeneous water-based polymerization. The influence of TOCNF surface chemistry and sodium dodecyl sulfate (SDS) on latex particle size, rheological behavior, and adhesive performance was systematically investigated. Latex particle size increased from 193 nm (PVA-SDS) to 625 nm with TOCNF-APTES and decreased to 247 nm upon SDS addition. Rheological analysis revealed pronounced shear-thinning behavior associated with the formation of percolated nanofibrillar networks, with low-shear viscosity increasing up to 477 Pa·s for TOCNF–APTES and decreasing to 370 Pa·s with SDS. Lap-shear testing (ASTM D905) showed substantial improvements in adhesive strength, reaching up to 250 kPa compared to PVA-SDS. These results demonstrate that surface-modified CNFs act not only as mechanical reinforcements but also as interfacially active components governing polymerization behavior, rheology, and adhesive performance. This exploratory study provides a proof-of-concept for the development of sustainable wood adhesives from agro-industrial byproducts. Full article

In response to the challenges of low detection efficiency, high omission rate in small target detection and high model complexity in wood surface defect detection, this study proposes a lightweight detection model based on YOLO, which integrates a dual-path integrated attention network (DFA-Net). The model is built on the enhanced YOLOv5 framework and achieves a balance of accuracy and efficiency through the collaborative optimization of multiple modules. Specifically, this paper designs a dual-path downsampling convolutional module (DP-DCM), combining wavelet transform with dual-path feature fusion to improve multi-scale feature extraction capabilities while reducing the number of parameters. Next, a fusion attention module (FAM) is designed to dynamically focus on defect features in complex backgrounds through channel and spatial attention mechanisms. Furthermore, a focal modulation network (FMNet) is introduced to enhance the robustness of the augmentation model in detecting small defects. Finally, the NWD Loss function is used to mitigate the localization bias of small targets. Experimental results show that the improved model achieves a 92.8% mAP rate on five types of defect datasets (dead knots, live knots, cracks, notches, and marrow). Compared with the baseline model, YOLOv5s, the performance of this model has been improved by 6.5%. The model runs at a detection speed of 105 FPS, and the number of parameters is only 5.8 million, which is better than models such as YOLOv8 and YOLOv9-t. While maintaining a lightweight design, this method achieves high precision and real-time performance on a consumer-grade GPU platform, indicating its practical applicability in automated wood inspection scenarios. The proposed approach provides an efficient solution for intelligent wood sorting, contributing to improved wood utilization and enhanced processing automation. Full article