Search Results (527)

Heightened consumer awareness of health benefits has propelled nutritionally enhanced cereal products to the forefront of food industry innovation. The present work focused on manufacturing polyphenol-enriched biscuits by incorporating grape seed extract (GSE). The study evaluated different formulations of wheat flour with GSE at addition ratios of 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1% (w/w). Within the 0.2–0.6% GSE range, significant improvements were observed in dough properties and biscuit quality. Specifically, the dough development time and degree of softening decreased, while the dough stability time, peak viscosity, storage modulus, and loss modulus increased. The fortified biscuits had a darker color, lower baking loss rate, and increased spread factor, springiness, and cohesiveness. In addition, total phenolic content (TPC) and total flavonoid content (TFC) of the fortified biscuits increased significantly (p < 0.05), and the antioxidant activity of the biscuits was also enhanced. Sensory evaluation results indicated that biscuits with 0.6% GSE addition achieved the highest overall acceptability score (8.7 ± 0.4 on a 9-point hedonic scale), indicating good consumer acceptance. These findings identify 0.6% GSE as an appropriate fortification level for improving biscuit quality while enhancing polyphenol content and antioxidant capacity, providing a practical basis for the development of polyphenol-enriched bakery products. Full article

Screen content images (SCIs) exhibit complex structural heterogeneity, rendering traditional statistics-based natural scene image quality assessment (NR-IQA) metrics ineffective. Although deep learning models achieve high prediction accuracy, their prohibitive computational demands preclude deployment in latency-sensitive industrial scenarios. While existing handcrafted lightweight SCI-IQA metrics reduce computational overhead, most rely on unsegmented global feature pooling or holistic edge statistics (e.g., edge histograms or Fisher vector coding), thereby diluting locally critical text-edge distortions in vast homogeneous backgrounds. To address this limitation, we propose an ultra-lightweight, deep-learning-free NR-IQA framework centered on human visual masking. Unlike existing lightweight methods, our approach explicitly employs dual-scale Canny edge operators to partition SCIs into edge-sensitive and flat background regions. Guided by this visual prior, structural degradations and micro-compression textures are extracted region-wise using Sobel gradients and uniform local binary patterns (LBPs) and aggregated with global Commission Internationale de I’Eclairage L*a*b*(CIELAB) color statistics into a compact 60-dimensional descriptor. A grid-search-optimized Support Vector Regression (SVR) maps these features to subjective quality scores. Extensive cross-validation on the SIQAD and SCID datasets demonstrates that our metric outperforms existing handcrafted lightweight SCI metrics and traditional NSS models, while achieving accuracy competitive with representative full-reference metrics. Consuming only 79.3 ms per image on a standard CPU, it offers a practical accuracy–efficiency trade-off for resource-constrained periodic quality monitoring. Full article

The growing demand for clean-label, high-quality, and sustainable meat products has increased interest in non-conventional processing technologies as alternatives to conventional processing methods. Therefore, this review aims to critically evaluate the technological advances, practical benefits, limitations, and industrial implementation potential of cold plasma, high hydrostatic pressure, ultrasound, microwave processing, and ohmic heating in meat and meat products. Studies published between 2016 and 2026 were analyzed, with emphasis on mechanisms of action, effects on physicochemical and microbiological properties, processing performance, and evidence of industrial applicability. Current evidence indicates that these technologies have progressed beyond laboratory-scale investigations in several applications, with HHP showing the highest level of commercial adoption, particularly in ready-to-eat meat products, while ultrasound, cold plasma, microwave processing, and ohmic heating exhibit different degrees of pilot- and industrial-scale development depending on the application. These technologies can enhance microbial safety, improve techno-functional properties, optimize processing efficiency, and contribute to shelf-life extension while reducing reliance on synthetic additives. However, their effectiveness is strongly influenced by processing conditions, product composition, economic feasibility, and technology-specific limitations. Reported challenges include lipid oxidation, color deterioration, texture modifications, heating non-uniformity, high implementation costs, limited process standardization, and regulatory uncertainties. Overall, recent advances demonstrate meaningful progress toward industrial application, but the degree of technological maturity varies substantially among technologies and applications. Further research should prioritize industrial-scale validation, process standardization, techno-economic assessment, regulatory harmonization, and consumer acceptance to facilitate broader commercial adoption. Full article

Underwater images are frequently degraded by wavelength-dependent absorption and scattering, resulting in color casts, low contrast, blurred textures, and loss of structural details. Existing enhancement networks may struggle to balance global context modeling, local detail recovery, and computational efficiency. To address this problem, we propose MambaUNet, an efficient U-shaped state-space network for underwater image enhancement. Its core VMEC pipeline integrates visual state-space scanning to capture long-range spatial dependencies, multi-scale alignment and adaptive aggregation to improve skip-feature coherence, efficient channel attention to recalibrate feature responses, and cross-channel state-space modeling to represent channel-dependent degradation. These components are assigned stage-specific roles within the U-shaped network, forming a spatial–scale–response–channel restoration pipeline. By coordinating these components within an encoder–decoder architecture, MambaUNet improves global tone consistency and structural recovery without relying on computationally expensive self-attention. Experiments on the full-reference LSUI and UIEB benchmarks and the no-reference C60 and S16 test sets show that the proposed network achieves competitive or superior restoration quality compared with representative conventional, CNN- or GAN-based, Transformer-based, and recent Mamba-based methods. Ablation and complexity analyses further demonstrate the complementary roles of the VMEC components and the favorable balance between enhancement quality, model size, and inference speed. MambaUNet can therefore serve as a lightweight preprocessing component for underwater imaging and vision-based applications. Full article

The sustainability of Coffea arabica L. production in Oaxaca, Mexico, is increasingly threatened by climate change, biodiversity loss, and pest pressure, along with other factors that undermine the responsiveness of small-scale producers in the state, such as trade restrictions and coffee-related regulatory frameworks. These challenges are particularly acute in mountainous regions like Oaxaca, where coffee cultivation plays a central role in rural livelihoods, cultural identity, and territorial development. Studies demonstrate that localized models are vital for rural improvement, reducing the carbon footprint, and maintaining economic viability. Despite extensive research on agronomic, environmental, and market factors, sustainability strategies for coffee production often remain fragmented and insufficiently integrated. This systematic review used the Scopus Review database, searching by title, abstract, and keywords such as “sustainable coffee production” from 2015 to 2026. A total of 1085 documents were retrieved and processed using VOSviewer software to generate a bibliographic map in which frequently used words are grouped by color to show their relationships. Using Oaxaca as a regional case study, the article synthesizes the key factors influencing coffee productivity and quality, examines the main socio-environmental challenges, and proposes a conceptual framework that integrates agroecosystem management with socioeconomic processes under external climate and market pressures. The proposed framework highlights the central role of agroforestry systems and ecosystem services in improving climate resilience, conserving biodiversity, and supporting quality-oriented value chains. These processes generate feedback loops that influence farmers’ livelihoods, food security, and territorial sustainability. While grounded in the context of Oaxaca, the socio-ecological review and conceptual framework presented here are applicable to other Arabica-producing regions facing similar challenges, providing a structured basis for future research, policy design, and integrated sustainability strategies. Full article

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions. Full article

Red sour soup dumpling filling, a prepared meat product from Guizhou, China, requires validated shelf-life control strategies for industrial-scale commercialization. This study investigated a composite preservation system of sodium D-isoascorbate, phospholipids, and sodium lactate using a Box–Behnken design, and evaluated its effects on color, lipid oxidation (PV, TBARS), TVB-N, texture, sensory scores, APC, and coliforms during accelerated shelf-life testing. Key indicators were identified by correlation analysis, and shelf life was predicted using log-logistic AFT and temperature-state-dependent kinetic models. The optimized formulation (0.27% sodium D-isoascorbate, 0.11% phospholipids, and 0.05% sodium lactate) significantly inhibited lipid oxidation, retarded color deterioration, maintained texture, and delayed sensory decline. Sensory shelf life (SSL₅₀) at 5 °C, −5 °C, and −18 °C extended from 4.44 d to 6.49 d, 16.70 d to 22.90 d, and 108.99 d to 136.76 d, whereas PV-based shelf life extended from 11.0 d to 14.5 d, 15.8 d to 18.8 d, and 124.3 d to 224.3 d. The relative extension of sensory shelf life decreased as storage temperature decreased, whereas PV shelf-life extension surged at low temperatures, indicating a decoupling of lipid oxidation from textural deterioration under frozen conditions. Therefore, shelf-life prediction for dumpling fillings should move away from arbitrary relative thresholds and instead establish sensory-calibrated absolute chemical thresholds, thereby helping align chemical shelf-life indicators with consumer-relevant quality endpoints. Full article

Categorical raster maps derived from remote sensing classifications are widely used to describe land cover, landforms, and other environmental characteristics. However, these products are usually analysed at the pixel level, which limits the identification of larger spatial structures and coherent landscape units. The J-image Segmentation (JSEG) algorithm provides a promising framework for region delineation, but it was originally developed for natural color imagery and relies on fixed scale assumptions that are poorly suited to thematic geospatial data. To address these limitations, we developed GeoJSEG, a modified version of JSEG designed for remote sensing categorized spatial datasets. The method operates directly on classified raster layers, introduces user-defined scale parameters, and employs Jensen–Shannon Divergence during region merging, enabling segmentation that better reflects the spatial organization of geographic phenomena. GeoJSEG was evaluated using synthetic categorical maps, natural RGB images, orthophoto-derived data, land-cover maps, and geomorphon representations of terrain forms. For the synthetic class map, the segmentation quality measure $\overline{J}$ decreased from 0.088 to 0.012 (better quality), while for orthophoto data, it decreased from 0.059 to 0.025 (better quality). Improvements were also observed for land-cover data and most natural-image datasets. The results demonstrate that GeoJSEG extends JSEG toward scale-aware regionalization of thematic raster data and provides a practical tool for post-classification analysis of remote sensing products. Full article

Restoring images captured in low-light rainy scenes is challenging because brightness degradation and rain corruption are strongly coupled. Enhancing visibility may amplify hidden rain streaks and noise, whereas aggressive deraining can suppress already weak scene structures. Existing cascaded pipelines and general restoration models often struggle to handle this interaction effectively. In this paper, we present the Degradation-Robust Hue Prior Network (DHP-Net), a single-stage framework for low-light rainy image restoration that combines degradation-robust hue prior guidance with perturbation-aware feature modulation. Specifically, DHP-Net extracts multi-scale hue priors to provide stable structural and color cues under coupled degradations, and it injects them into a hierarchical Transformer restoration backbone. To further improve interaction among entangled feature responses, we introduce a Channel-adaptive Attention Perturbation Module that reorganizes intermediate representations before cross-channel aggregation. In this way, the proposed model jointly promotes visibility enhancement, rain removal, and structure preservation within a unified architecture. Extensive experiments on the Low-Light Rain (LLR) benchmark show that DHP-Net achieves 33.14 dB Peak Signal-to-Noise Ratio (PSNR) and 0.9252 Structural Similarity Index Measure (SSIM) on synthetic data and also delivers superior perceptual quality on real-world low-light rainy images, consistently outperforming existing state-of-the-art restoration models. Full article

City image links perceptions of urban form and environmental quality to place identity and sociocultural sustainability. However, city-scale research rarely integrates visible streetscape characteristics with visitor narratives. Using Wuhan, China, as a case study, this study integrates panoramic streetscape imagery with online visitor reviews. Semantic segmentation was employed to quantify nine streetscape visual indicators, such as building interfaces, visible greenery and water, color characteristics, and visual disturbances. High-frequency term extraction and thematic coding identified four dimensions of visitor perception: cultural, aesthetic, leisure, and scientific–educational perceptions. Statistical and spatial analyses were subsequently conducted to examine the relationships between the visual indicators and these perceptual dimensions. Leisure and aesthetic perceptions emerged as the most prominent dimensions. Color harmony, water visibility, and visible greenery were positively associated with cultural, aesthetic, and leisure perceptions. Dense building interfaces and visual disturbances were negatively associated with aesthetic and leisure perceptions but positively associated with scientific–educational perception. Hotspots of cultural, aesthetic, and leisure narrative intensity were concentrated in core Wuchang and the East Lake area, whereas scientific–educational perception showed weaker overall spatial clustering, with localized hotspots in central urban areas. These findings suggest that integrating street-view imagery with visitor narratives can help identify spatially differentiated relationships between visible urban environments and city image. The proposed framework may facilitate the identification of sustainability-relevant visual resources and provide diagnostic evidence that may inform urban design and public-space management. Full article

Multi-scale rendering exposes scale-dependent reconstruction errors in 3D Gaussian splatting (3DGS) when the rendering resolution differs from the training resolution. Existing anti-aliasing and multi-scale optimization methods reduce these artifacts, but each Gaussian usually keeps a fixed color representation and a fixed spatial allocation after densification. This fixed representation is restrictive at coarse scales because one rendered pixel combines multiple Gaussian contributions into an average color that can differ from the color assigned to each Gaussian. In addition, Gaussians allocated by fine-scale reconstruction criteria may provide insufficient coverage for regions with persistent coarse-scale errors. This paper proposes adaptive-scale color offset and error-guided Gaussian reallocation for multi-scale 3DGS. The adaptive-scale color offset applies a scale-specific correction to the shared base color of each Gaussian while keeping its geometry unchanged. Error-guided Gaussian reallocation shifts low-contribution Gaussians toward high-error regions under a fixed primitive budget and preserves Gaussians that support coarse-scale rendering. Experimental results show that the proposed method improves multi-scale rendering quality across rendering scales on Mip-NeRF 360, Tanks and Temples, and Deep Blending, with larger gains at coarse scales. These results demonstrate that adapting Gaussian color and primitive allocation together improves scale-consistent 3DGS rendering without modifying the inference-stage rendering process. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Quality inspection of imported saw-ginned cotton mainly involves the determination of color grade and impurity grade. Traditional manual grading and High Volume Instrument (HVI) testing are limited by subjectivity, insufficient accuracy, single-indicator measurement, and long inspection cycles. To address these limitations, this study developed a machine vision-based intelligent identification system for saw-ginned cotton quality grading. The system integrates a portable image acquisition box, a cloud-based intelligent recognition service, and a HarmonyOS-based mobile application. A total of 6363 saw-ginned cotton images were collected using the self-developed image acquisition device for model training and testing. Based on U-Net background segmentation, a dual-branch parallel recognition framework was established for cotton color grading and impurity grading. The CA-ResNet50 model, integrating ResNet50, the Efficient Channel Attention (ECA) mechanism, and the AdamW optimization strategy, was constructed for cotton color grade recognition. The AD-UNet model, incorporating Atrous Spatial Pyramid Pooling (ASPP)-based multi-scale contextual modeling and the DySample adaptive upsampling mechanism, was developed for impurity segmentation. In addition, the HarmonyOS-based mobile application supports information query, image acquisition, intelligent recognition, and data traceability. Experimental results showed that the CA-ResNet50 color grading model achieved an F₁-Score of 94.8%. Based on the AD-UNet impurity segmentation results and the calculated impurity area ratio, the accuracy of impurity grade determination reached 97.3%. The average cloud-based inference time for a single image was approximately 0.8 s, and the complete workflow, including sample flattening, image acquisition, and recognition, required approximately 10 min. The proposed system improves the accuracy, efficiency, objectivity, and traceability of saw-ginned cotton quality identification, providing technical support for rapid inspection and quality supervision of imported cotton. Full article

Front-of-pack nutrition labeling systems have become important tools for improving consumer understanding of nutritional information and supporting healthier food choices. Among these systems, Nutri-Score has emerged as one of the most widely implemented nutrient profiling models in Europe. Based on the Food Standards Agency Nutrient Profiling System, Nutri-Score summarizes selected nutritional characteristics of foods through a simplified five-level color-coded scale. A growing body of evidence indicates that Nutri-Score improves consumers’ ability to compare products, supports healthier purchasing decisions, and may encourage product reformulation. Epidemiological studies have reported associations between dietary patterns characterized by less favorable nutrient profile scores and increased risks of chronic diseases and mortality. Despite these strengths, important scientific and conceptual questions remain. This review examines the principles of nutrient profiling and assesses how effectively food quality can be represented by a limited set of nutritional criteria. Particular attention is given to nutrient reductionism, the use of the standardized 100 g reference, food matrix effects, the relationship between nutrient profiling and food processing, the influence of hedonic drivers and health claims on consumer behavior, and the growing importance of environmental contaminants as dimensions of food quality not captured by current algorithms. Future food evaluation systems may benefit from integrating complementary information related to food structure, processing characteristics, dietary context, and emerging insights from systems nutrition. Full article

Strawberry ripeness estimation is essential for determining optimal harvest timing and supporting quality management in smart greenhouse environments. However, since strawberry ripening is a continuous process rather than a transition between discrete visual boundaries, standard RGB-based methods often struggle to distinguish subtle color transitions during intermediate ripeness levels, and misclassifications among adjacent ripeness stages can substantially affect harvest decisions. This makes it important to not only achieve high classification accuracy but also to minimize the ordinal deviation between predicted and actual ripeness stages. To address these limitations, this study presents a two-stage vision-based framework for five-class strawberry ripeness grading. In the first stage, a YOLO11m-based detector localizes strawberry regions within greenhouse images and generates regions of interest (ROIs). In the second stage, each extracted ROI is classified using a five-channel input representation that combines RGB, Excess Green (ExG), and Hue information to enhance color-sensitive feature extraction. In addition, a specialized backbone, termed CP2-A2C2F, is designed to preserve high-resolution P2 features and integrate them with deeper P4 semantic representations through a dual-scale fusion strategy. This structure enables the model to capture localized chromatic variations and contextual maturity information simultaneously. Experimental results show that the CP2-A2C2F model achieved the best overall classification accuracy of 87.7% and a weighted F1 score of 0.877, together with the lowest mean absolute error (MAE) of 0.162 among models equipped with an auxiliary regression head, indicating the smallest average ordinal deviation between predicted and ground-truth ripeness stages. The ablation study further confirmed that adding ExG and Hue channels moderately reduced ordinal prediction error compared with the RGB-only configuration, and that the proposed P2 + P4 fusion achieved the best accuracy, weighted F1 score, and MAE among the evaluated fusion configurations, indicating that direct fusion of high-resolution P2 features with deeper P4 semantic features provides a more effective balance than including the intermediate P3 level. These results indicate that the combination of explicit color representation and dual-scale P2 + P4 feature fusion can improve five-class strawberry ripeness grading and help reduce ordinal prediction errors under the tested greenhouse conditions. Full article