Search Results (467)

Frankincense is a natural oleo-gum resin obtained from several Boswellia tree species, playing important roles in supporting the spiritual, cultural, and socioeconomic livelihoods of communities across East Africa. Despite their cultural and economic value, the Ethiopian market still lacks scientifically based criteria to evaluate and properly classify this raw material, with traditional grading relying on gum size, color, collection area, and impurity content. Frankincense-derived essential oil value is much higher than that of gum, making this valorization route very enticing. This work compares the extraction potential and chemical profiles of hydrodistilled essential oils from various commercial grades and also different Ethiopian harvest locations (Afar, Humera, Assosa, Shire, Metema, South Omo, Borena and Jigjiga). The essential oils were extracted using hydrodistillation with a Clevenger-type apparatus, and their chemical composition was identified with GC-MS. The results revealed no substantial quantitative and qualitative differences among commercial grades, showing that essential oils can be obtained indiscriminately from classification. As for harvesting locations, both the extraction yield and essential oil compositions varied substantially. With the economic value of frankincense essential oil around six times that of the raw resin required to obtain it, these results show the importance of revising the commercial grading system to reflect chemical composition and promote the value-added processing of both black and white frankincense, rather than relying mainly on raw resin exports. Full article

This study proposes a vision-based framework for automated inspection and quality grading of beef steaks by integrating fat marbling distribution analysis and lean-meat color evaluation. In frozen beef products, surface frost often generates specular reflections that resemble both fat and lean regions, thereby reducing segmentation accuracy. To address this challenge, a sequential and interpretable analytical framework is developed. First, homomorphic filtering is applied to suppress frost-induced illumination artifacts, followed by curvelet transform combined with square-ring filtering to separate fat and lean regions based on their multi-scale and directional characteristics. For marbling analysis, the convex hull, skeleton, and principal axis of the steak are extracted, and a chi-square goodness-of-fit test is performed within eight predefined regions to quantitatively evaluate marbling distribution uniformity and identify localized fat accumulation. For lean-meat evaluation, RGB color features are extracted and classified using a Support Vector Machine (SVM) to determine redness levels. The resulting marbling and color information are subsequently integrated through a weighted grading strategy to estimate the final quality grade. Experimental results demonstrate a fat detection rate of 92.68%, a false-positive rate of 4.97%, and a correct classification rate of 94.09% for fat segmentation, while the SVM-based lean-meat color classifier achieves an accuracy of 96.67%. Furthermore, the proposed grading framework attains an overall grading accuracy of 90.38%, showing strong agreement with human evaluation. Full article

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms. Full article

Niacin contents vary significantly among fresh-cut potato cultivars with different browning sensitivities, whereas its role as a browning inhibitor for fresh-cut produce has not been previously reported. In this study, potato slices were soaked in distilled water (control) or 1% food-grade niacin solution for 5 min, then stored at 4 ± 1 °C for 8 days with sampling every 2 days for physiological and molecular analyses. In particular, the optimal niacin (1%) treatment showed higher brightness and lower color change than the control. The activities of polyphenol oxidase (PPO), peroxidase (POD), and phenylalanine ammonia lyase (PAL), and phenol content were reduced. Higher activities of superoxide dismutase (SOD) and catalase (CAT), and greater glutathione accumulation, were observed following niacin treatment. Meanwhile, lower levels of malondialdehyde and reactive oxygen species (ROS), and lower nicotinamide adenine dinucleotide phosphate oxidase (NOX) activity, indicated lower oxidant damage. The contents of NADP and NAD, and activities of nicotinamide adenine dinucleotide kinase (NADK) and glucose-6-phosphate dehydrogenase (G6PDH) were improved. Furthermore, the gene expression patterns of StRBOH, StPPO, and StG6PDH also supported the hypothesis that niacin regulates pyridine nucleotide and ROS homeostasis. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article

The color origin of precious coral, a highly valued biogenic polycrystalline gemstone, has long remained elusive. In this study, an integrated approach employing spectrophotometry, Raman, FTIR, and UV-Vis spectroscopy, coupled with Spearman correlation analysis, was utilized to investigate a color-graded series of precious coral samples ranging from white to red. The results demonstrate that the calcareous composition of the samples tested in our study consists exclusively of calcite. The actual chromophores are identified as a blend of multiple distinct polyene species, characterized by Raman shifts at 1126 and 1515 cm⁻¹, with density functional theory (DFT) calculations determining the number of conjugated (C=C) bonds in the polyene chain to be 10–11. Inherently exhibiting a red-orange hue, the progressive accumulation of these polyenes drives a systematic color transition from orange to red. Both absorption bands at 314 nm and 532 nm in the UV-Vis spectra are attributed to the polyene pigment molecules. Specifically, the broad 532 nm band is dominated by π-π* electronic transitions, while the 314 nm band likely arises from terminal benzene rings and their derivatives. As the pigment concentration increases, this band exhibits pronounced broadening and an increase in absorbance, accompanied by a redshift in the maximum absorption peak. This spectral evolution leads to an intensified absorption in the yellow-orange region, elucidating the intrinsic mechanism underlying the color transition of precious coral from orange to red with increasing pigment content. This work lays a solid foundation for the non-destructive identification of precious corals and future research on their color genesis. Full article

Automated and objective grading of cocoa (Theobroma cacao L.) fermentation remains a major challenge because the conventional cut test relies on subjective visual inspection and is difficult to scale. In this study, we develop and evaluate a deep learning pipeline for classifying cocoa bean fermentation levels from expert-annotated cut-test images acquired under controlled conditions, enabling the systematic evaluation and comparison of multiple convolutional and transformer-based architectures under consistent preprocessing, training, and evaluation protocols. The dataset comprises 4347 segmented cocoa bean images distributed across four severely imbalanced classes, namely fermented, under-fermented, slaty, and violet. Representative architectures, including EfficientNet-B0, MobileNetV3-Large, ConvNeXt-XLarge, ViT-Base, and ViT-Large, are benchmarked to analyze the effects of class imbalance, RGB versus HSV color representation, training duration, and label-space formulation. The results show that severe class imbalance strongly degrades performance in direct four-class classification. A hierarchical binary-to-multiclass strategy significantly improves balanced recognition by separating fermented from unfermented beans prior to subclass discrimination, increasing macro-F1 scores from approximately 80–83% to 89–91%. Among the evaluated models, ViT-Base emerges as the most stable architecture across experimental settings and offers the best balance between classification performance, training stability, and computational cost. Although larger models achieve slightly higher peak performance under balanced conditions, ViT-Base provides more consistent results under realistic constraints. The proposed framework enables near-real-time inference on segmented single-bean images and supports objective, reproducible, and scalable fermentation assessment. These findings demonstrate that performance in cocoa fermentation grading is determined not only by model capacity, but also by imbalance-aware label-space design and evaluation protocols aligned with real-world cut-test conditions. Full article

Dalbergia odorifera is an economically valuable timber species. While its plantation-grown stock displays unique juvenile wood characteristics, current drying research is insufficient and lacks direct applicability to the heartwood. This study investigated the drying characteristics of a 17-year-old plantation-grown Dalbergia odorifera from Hainan using the 100 °C drying test method. Conventional drying technology was optimized by adjusting key parameters, including drying rate, moisture content variation, residual stress, and drying defects. The results showed that the overall drying defect grade of 25 mm-thick Dalbergia odorifera specimens were Grade 3, and the superior drying quality was achieved under relatively low-temperature and high-humidity conditions. The optimal drying schedule was comprehensively determined as follows: an initial temperature of 48 °C with a wet-bulb depression of 3 °C, a final temperature of 68 °C with a wet-bulb depression of 12 °C, and a drying duration of approximately 12 days to reduce the moisture content (MC) from 50% to 10%. Under this schedule, all drying quality indicators of the sawn timber met Grade 2 standards, and the color of wood has been well preserved. These results provided a theoretical and technical reference for improving the utilization efficiency of wood and the industrial drying of plantation-grown Dalbergia odorifera. Full article

In the field of traditional aging state evaluation of paper materials, traditional detection technologies such as ink drop method and chemical analysis have inherent limitations including sample damage, strong subjectivity, and inability to realize large-area detection. To address these problems, a non-contact and non-destructive testing method based on air-coupled ultrasonic technology was developed in this study, to achieve objective and quantitative characterization of paper roughening degree and ink wettability. The system adopted a LabVIEW-based host computer to control scanning and signal acquisition. Based on the propagation and scattering mechanism of ultrasound in the porous fiber structure of paper, the amplitude difference and pixel distribution of C-scan images were extracted as core characteristic parameters. The experimental results show that, with a 400 kHz air-coupled probe and 200 mm/s scanning speed, the roughening degree of paper can be quantitatively characterized by the amplitude difference of ultrasonic transmission signals. The amplitude difference increases significantly with the rise of water content, and the difference in roughening characteristics between newsprint and Xuan paper can be clearly distinguished. The ink wettability can be judged by the pixel distribution of the C-scan image: the higher the proportion of intermediate color pixels, the closer the ink circularity is to 1, and the better the ink wettability. All test results are highly consistent with the national standard GB/T 18739-2008. The constructed air-coupled ultrasonic testing system can provide reliable technical support for quality control and aging evaluation of paper cultural relics and high-grade paper by characterizing both surface roughening and internal porous structure (which are coupled during paper aging), without any contact or damage to the samples. Full article

Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems. Full article

This study evaluated how two commercial egg types (free-range and barn-laid) influence the physical, compositional, and rheological properties of egg white and yolk in raw and grilled states. Free-range eggs showed stronger correlations between external dimensions and internal composition, suggesting potential for nondestructive grading, whereas barn eggs exhibited heavier shells but weaker morphometric–composition relationships. Haugh units differentiated production systems, and yolk redness was the only color parameter clearly associated with free-range origin. Mechanical tests revealed that barn eggs had shells capable of absorbing more energy during rupture. Rheological measurements showed matrix-dependent behaviors: in raw samples, egg white behaved as a weakly structured viscoelastic fluid, while yolk exhibited characteristics of a concentrated lipoprotein emulsion. Stress, frequency, and temperature sweeps revealed contrasting behaviors between the two commercial egg types: barn-laid eggs displayed a stronger egg-white protein network, whereas free-range eggs showed a more reinforced yolk lipoprotein matrix under the conditions evaluated. Yolk behavior fitted the weak gel model with excellent accuracy (R² ≈ 1), while egg white did not. Steady shear and three-step tests confirmed pronounced shear thinning and thixotropic behavior in both matrices, with barn eggs showing higher viscosities but lower structural recovery. Thermal treatment reduced the strong rheological differences between raw egg white and yolk, yet production system effects persisted. All grilled samples behaved as weak gels, with barn egg whites forming stiffer networks and free-range yolks generating more elastic, cohesive, and energy-absorbing gels. A trend toward higher MUFA levels was observed in raw free-range yolks. Microscopy further clarified how production system shapes the structural and functional behavior of egg matrices. Full article

Two wild Saccharomyces cerevisiae strains, 9-3 and 10-2, isolated from nectarine and apple leaves, show high fermentation performance in standard breadmaking but their utility in enriched-dough products remained untested. This study evaluated their performance in kouglof production against a commercial baker’s yeast as a control. Fermentation was monitored by weight loss (reflecting CO₂ production) and pH changes. Finished loaves were evaluated for macroscopic traits (weight, height, appearance), crust/crumb color, texture, and microstructure via 3D/2D analysis of gas-cell morphology. Sensory acceptance was also assessed. While size-related traits were similar across all samples, strain 10-2 significantly outperformed 9-3. Kouglof made with 10-2 exhibited commercial-grade softness, characterized by lower hardness and gumminess, lower residual sugar content, and a finer, more uniform gas-cell structure with a darker, well-developed crust, whereas 9-3 yielded a firmer crumb with fewer, larger gas cells. Sensory acceptance was comparable across variants, with all samples rated as highly acceptable. Strain 10-2 demonstrated high sucrose tolerance and performed comparably to or better than the control across multiple product-quality metrics in enriched-dough kouglof. These findings support the potential of wild yeast 10-2 for sweet bread production with high-sucrose and high-fat formulations, though further optimization is warranted. Full article

Ganoderma lucidum spore powder is widely recognized as a high-grade Ganoderma product and is extensively consumed as a functional food and dietary supplement in China. To compare quality differences, nine batches of sporoderm-broken G. lucidum spore powders (DX, SD, FJ, JL, XZ, LQ, AH, LN, and GZ) were collected from representative producing regions across China. Their physicochemical properties, antioxidant activities, and enzyme inhibition capacities were analyzed in this work. The results revealed varying degrees of differences in color, chemical composition, antioxidant activity, and metabolic enzyme inhibitory effects among the samples. Notably, sample GZ contained the highest levels of total sugar, polysaccharides, lipids, protein, total phenolics, and total triterpenoids; sample XZ had the highest ergosterol content; and sample LN exhibited the highest levels of reducing sugar and nucleosides. GZ demonstrated the strongest radical scavenging activity, ferric-reducing antioxidant power (FRAP), cupric ion-reducing capacity, and inhibitory effects against α-glucosidase, α-amylase, lipase, acetylcholinesterase, and xanthine oxidase. Sample AH showed the greatest Fe²⁺-chelating capacity. Principal component analysis indicated that GZ, AH, and LN exhibited stronger antioxidant and metabolic enzyme inhibition activities, whereas LQ and FJ showed lower activities. These findings confirm significant quality differences among G. lucidum spore powders sourced from different geographical regions. Full article

Diabetic retinopathy (DR) is a leading cause of vision impairment and permanent blindness worldwide, requiring accurate and automated systems for multi-grade severity classification. However, standard Convolutional Neural Networks (CNNs) often struggle to capture fine, high-frequency microvascular patterns critical for diagnosis. This study proposes a Robust Intelligent CNN Model (RICNN) that integrates Gabor-based feature extraction with deep learning to improve DR classification. Specifically, Gabor filters are applied during preprocessing to extract orientation- and frequency-sensitive texture features, which are transformed into feature maps and concatenated with CNN feature representations at the fully connected layer (feature-level fusion). The model also incorporates the Synthetic Minority Oversampling Technique (SMOTE) for data balancing and the Adam optimizer for efficient convergence. This integration enhances sensitivity to microvascular structures such as microaneurysms and hemorrhages. The proposed RICNN was evaluated on the Messidor dataset (1200 images) across four severity levels: Mild, Moderate, Severe, and Proliferative DR. The model achieved an accuracy of 89%, a precision of 88.75%, a recall of 89%, and an F1-score of 89%, with AUCs of 97% for Severe DR and 99% for Proliferative DR. Comparative analysis confirms that the proposed texture-aware Gabor enhancement significantly outperforms LBP and Color Histogram approaches, indicating its potential for reliable clinical decision support. Full article

Addressing the challenges of complex background interference, low lighting conditions, small target recognition, and difficulty in maturity grading in the automated detection of Pleurotus ostreatus, this study proposes a lightweight improved scheme based on color feature enhancement. By collecting 4779 images from five developmental stages in three typical planting environments, including greenhouses and mushroom houses, an HSV hue analysis database was established to determine key hue intervals [4°, 38°] or [110°, 155°] for different environments. Secondly, based on the hue interval distribution of Pleu-rotus ostreatus, YOLOv13 was used as the base model, with the addition of an HSV hue mask as the fourth channel to improve the input layer. The custom ColorWeight module was used to enhance color feature expression; the hypergraph computation module was improved to enhance feature correlation; and the neck network incorporated the StockenAttention module to improve the ability to capture maturity features. The accuracy of the improved model was increased to 89.5% in mAP@0.5 (+3.3%), surpassing the mainstream YOLOv8n-12n series. Efficiency optimization achieved real-time detection at 12.58 FPS on the RTX3090Ti platform. In practical applications, the accuracy of maturity recognition was significantly improved, with a 73.6% decrease in the misclassification rate of maturity and a reduction in missed detections, achieving an F1 score of 0.91. In conclusion, through the deep integration of Hue features and deep learning models, while ensuring lightweight deployment (with only a 10.5% increase in parameter count), the accuracy and practicality of Pleurotus ostreatus detection were significantly improved, providing an effective solution for intelligent mushroom house management. Full article