Search Results (3,096)

Polysaccharide-based edible coatings are increasingly explored as sustainable strategies for maintaining quality of fresh produce, acting as barriers to gas exchange while improving mechanical and optical properties. However, their effectiveness depends not only on the intrinsic features but also on the structural and physiological diversity of fruits and vegetables, which vary in peel composition, hydrophobicity, and texture. This study investigated plant-derived polysaccharide films (cassava starch, potato starch, corn starch, carboxymethylcellulose, hydroxypropylmethylcellulose, and pectin) characterized for moisture resistance, solubility, permeability, thermal stability, hydrophilicity, opacity, gloss, and mechanical strength. Concurrently, different fruits and vegetables (fruit, root, and tubers) were analyzed for their surface hydrophilicity to establish correlations between film properties and peel characteristics. The findings emphasize that no single polymer can be universally applied. In addition, the choice of matrix must be guided by both film functionality and produce surface traits. Starch-based films presented high hydrophilicity, suggesting better wettability, while pectin and cellulose derivatives presented distinct advantages for less hydrophilic peels. This work highlights the importance of tailoring edible coatings according to the physicochemical compatibility between films and fresh produce surfaces, providing insights for improving post-harvest preservation strategies and guiding the development of effective, sustainable coatings for diverse horticultural commodities. Full article

This study aimed to evaluate the effect of rosemary and oregano extracts on the chemical composition, physicochemical parameters, and sensory characteristics of pork pâtés under cold storage for 1, 7, and 14 days. Five different experimental variants were developed: “k”—control sample (no extract added), “rr”—with 50% rosemary extract added, “rs”—with 100% rosemary extract added, “oo”—with 50% oregano extract added, and “os”—with 100% oregano extract added. The study showed that rosemary and oregano extracts did not cause significant changes in the basic chemical composition and pH. However, they significantly affected the oxidative stability, color characteristics, texture, and sensory acceptance of the pâtés. TBARS (lipid oxidation rate) values systematically increased during storage, with the lowest lipid oxidation rate observed in samples with rosemary extract. The extracts also limited the increase in oxidation-reduction potential compared to the control sample. Changes in texture parameters were also observed, but the additives significantly reduced their unfavorable character, particularly in terms of hardness and chewiness. Sensory evaluation results confirmed the positive impact of the extracts, particularly in terms of odor and taste, which were rated significantly higher than in the control sample. The conducted studies indicate that rosemary and oregano extracts may be a natural source of compounds with antioxidant properties and stabilize the quality of pork pâtés. Their use may provide an effective and consumer-acceptable alternative to synthetic preservatives, supporting the development of meat products aligned with the “clean label” trend. Full article

In recent years, social media-related sentiment classification has been researched extensively and is applied in various fields such as opinion mining, commodity feedback, and market analysis. Therefore, it is important to understand and analyse the opinions of the public, their feedback, and data related to social media. Consumers continue to face challenges in accessing review-based sentiment classification expressed by their peers, and the existing method does not provide satisfactory results. Hence, an innovative sentiment classification method, the Convoluted Graph Pyramid Attention (CGPA) model, combined with the Updated Greater Cane Rat Algorithm (UGCRA), is proposed. This method improves sentiment classification by optimizing accuracy and efficiency while addressing inherent uncertainties, allowing for precise sentiment intensity evaluation across multiple dimensions. Explainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAPs), enhance the model’s transparency and interpretability. This approach enables the final ranking of classified reviews, predicts ratings on a scale of one to five stars, and generates a recommendation list based on the predicted user ratings. Comparison between other traditional existing methods and the result indicates that the proposed method achieves superior performance. From the experimental results, the proposed approach achieves an accuracy of 99.5% in the Restaurant Review dataset, 99.8% in the Edmund Consumer Car Ratings Reviews dataset, 99.9% in the Flipkart Cell Phone Reviews dataset, and 99.7% in the IMDB Movie database, showing its effectiveness in analysing sentiments with an increase in performance. Full article

Aflatoxin B1 (AFB1) is a highly toxic and carcinogenic compound produced by certain fungi (e.g., Aspergillus flavus and Aspergillus parasiticus). Rapid and ultra-sensitive detection methods for AFB1 in various commodities are in high demand. This study aimed to enhance the sensitivity of a competitive lateral flow immunoassay (LFIA) for AFB1 detection by leveraging a previously developed experimental design strategy, named 4S. This approach comprises four phases—START, SHIFT, SHARPEN, and STOP—and involves the analysis of two reference conditions: NEG (0 ng/mL AFB1) and POS (1 ng/mL AFB1). By generating and overlaying response surfaces, regions of optimal NEG signal and POS/NEG signal ratio (IC%) were identified. Four variables were optimized: two related to the labeled antibody (its concentration and antibody-to-label ratio) and two to the competitor antigen (its concentration and hapten-to-protein ratio). An initial design defined the parameter space, while three subsequent designs did not yield further improvements in sensitivity. A strong anti-correlation was observed between the IC% and competitor parameters. The optimized LFIA-1 exhibited enhanced sensitivity, achieving a limit of detection of 0.027 ng/mL compared to 0.1 ng/mL for the original device. Additionally, the amount of expensive antibody required for device fabrication was reduced by around a factor of four. Full article

The contents of total polyphenols, flavonoids, phenolic acids, anthocyanins, and carotenoids were determined using spectrophotometric and chromatographic methods, alongside antioxidant activity: 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Power (FRAP), Cupric Reducing Antioxidant Capacity (CUPRAC), and hydroxyl radical scavenging assays). Additionally, the levels of antinutritional compounds (tannins, phytates, oxalates, alkaloids, and saponins) were assessed in the flowers of five pumpkin species: giant pumpkin, summer squash, butternut squash, fig-leaf gourd, and cushaw squash (Cucurbita maxima, C. pepo, C. moschata, C. ficifolia, and C. argyrosperma). The results revealed significant interspecific variation in both bioactive and antinutritional compounds. Giant pumpkin flowers exhibited the highest content of polyphenols and phenolic acids, fig-leaf gourd flowers were the richest in carotenoids, whereas butternut squash flowers had the highest anthocyanin levels. The strongest antioxidant activity was observed in giant pumpkin flowers, which can be attributed to their high phenolic and flavonoid content. Despite the presence of moderate amounts of antinutritional compounds, pumpkin flowers can be considered a valuable edible raw material with nutraceutical potential. Full article

Understanding how conservation practices involving tillage and straw return practices affect the soil organic carbon (SOC) in farmland is important for soil carbon sequestration and climate change mitigation. However, limited studies have been conducted to investigate and compare the magnitude and variability of the main conservation practices concentrated in grain-producing regions. In this study, we evaluated the SOC response to the main practices (e.g., no tillage, reduced tillage, deep tillage, and straw return) in the Mollisol region of Northeast China based on collected field data (871 observations) using a combination of meta-analysis and random forest (RF) methods. The results show that the SOC change rate significantly increased from 1980 to 2022, with an average annual increase rate of 0.19–14.92%. Straw return had maximum effects on SOC of 17.44% when the soil pH > 7.5 and 15.22% when the initial SOC < 10 g kg⁻¹. The RF results indicate that the initial SOC is the most important factor for SOC, with relative importance values of 33.4%, 29.4%, 29.0%, and 34.1% for SOC under the four practices, respectively. These findings are essential for the implementation of conservation practices to improve carbon sequestration and grain production in eco-agricultural regions. Full article

The synergistic application of biochar and straw could improve soil properties and influence soil microbial community. However, its impacts on microbial community interactions and functions within various aggregate fractions remain unclear. We conducted a three-year field trial in black soil in northeastern China, under the restoration measures of biochar application (BR, 30 t ha⁻¹ once), straw return (SR, 5 t ha⁻¹ year⁻¹), and the combination of BR and SR (BS, BR at 30 t ha⁻¹ once and SR at 5 t ha⁻¹ year⁻¹). Utilizing high-throughput sequencing, we assessed the influence of different straw-returning methods on the structure and function of microbial communities in the mega-aggregates (ME, >2 mm), macroaggregates (MA, 0.25–2 mm), and microaggregates (MI, <0.25 mm). Relative to the control (CK), the BR, SR and BS treatments significantly decreased the bacterial Shannon index, mainly dependent on ME (p < 0.05). Conversely, compared with the CK and SR treatments, both BR and BS treatments notably reduced the fungal Shannon index, largely influenced by MI (p < 0.05). Moreover, the BS treatment significantly increased the relative abundance (RA) of Mortierellomycota (p < 0.05) compared to the CK, BR and SR treatments. Meanwhile, the SR and BS treatments substantially reduced the RA of Nitrospirae (p < 0.05) in comparison to the CK and BR treatments. Furthermore, compared with the CK, the BR and SR treatments enhanced microbial network connectivity, while the BS treatment diminished it, especially in ME and MI. Concurrently, the keystone of co-occurrence networks shifted from Phycisphaeraceae, Blastocatellaceae, and Glomeraceae in the CK treatment to uncultured_bacterium_c_JG37-AG-4 and DA111 in the BS treatment. Additionally, BR and SR exhibited synergistic effects on most microbial community functions (e.g., enhanced chitinolysis and carbon fixation but reduced nitrogen-cycling functions), but they also possessed distinct differential functions. In short, the combined application of biochar and straw adversely impacted soil microbial community diversity and stability, especially in ME and MI. Full article

Background: Parents’ dietary strategies shape children’s eating habits. This study investigated socio-demographic determinants of maternal feeding practices among school-aged children in the Pomeranian province of Poland. Using a cross-sectional survey conducted in July 2025, we compared feeding strategies based on family structure, maternal employment, and number of children, and identified distinct parenting profiles through cluster analysis. Methods: A cross-sectional survey was conducted in July 2025 among 719 mothers of elementary school children in Pomeranian Voivodeship, using a convenience sampling design. An abbreviated version of the Comprehensive Feeding Practices Questionnaire (CFPQ) with 16 items across eight subscales was used. ANOVA compared feeding strategies between groups, Spearman correlations examined associations, and k-means cluster analysis identified maternal parenting profiles. Results: Encouragement and modeling were the most frequent strategies, while monitoring was least common. Mothers raising children with a partner and those employed used monitoring, modeling, and encouragement more often. Single or non-working mothers relied more on food as a reward and for emotion regulation. Mothers of only children applied control and monitoring less intensively than mothers with multiple children. All strategies were positively correlated. Cluster analysis identified three parenting profiles: intensely directive, moderate, and emotional-supportive. Conclusions: Maternal feeding strategies vary with socio-demographic factors. Educational interventions promoting healthy eating should be tailored to family structure and mothers’ employment status. Full article

The use of Artificial Intelligence (AI) in healthcare has significantly advanced early disease detection, enabling timely diagnosis and improved patient outcomes. This work proposes an end-to-end machine learning (ML) model for predicting diabetes based on data quality by following key steps, including advanced preprocessing by KNN imputation, intelligent feature selection, class imbalance with a hybrid approach of SMOTEENN, and multi-model classification. We rigorously compared nine ML classifiers, namely ensemble approaches (Random Forest, CatBoost, XGBoost), Support Vector Machines (SVM), and Logistic Regression (LR) for the prediction of diabetes disease. We evaluated performance on specificity, accuracy, recall, precision, and F1-score to assess generalizability and robustness. We employed SHapley Additive exPlanations (SHAP) for explainability, ranking, and identifying the most influential clinical risk factors. SHAP analysis identified glucose levels as the dominant predictor, followed by BMI and age, providing clinically interpretable risk factors that align with established medical knowledge. Results indicate that ensemble models have the highest performance among the others, and CatBoost performed the best, which achieved an ROC-AUC of 0.972, an accuracy of 0.968, and an F1-score of 0.971. The model was successfully validated on two larger datasets (CDC BRFSS and a 130-hospital dataset), confirming its generalizability. This data-driven design provides a reproducible platform for applying useful and interpretable ML models in clinical practice as a primary application for future Internet-of-Things-based smart healthcare systems. Full article

Against the accelerating of global climate change and carbon neutrality transitions, energy price volatility exerts complex effects on the employment dimension of economic sustainability through both industrial and agricultural channels as intermediaries. This study employed a mixed-frequency vector autoregression model to statistically analyze the weekly prices of four major industries and 24 sub-markets in China. The main outcome was the urban unemployment rate in China, and it was verified against the urban unemployment rates in 31 cities and the unemployment rates by age group (YUR/LUR). The study investigated the employment dimension of economic sustainability. Energy and energy metal prices represent the energy transition, while food and industrial goods prices characterize the intermediary linkages. Unemployment rates serve as the employment dimension of economic sustainability. The findings reveal bidirectional interactions and heterogeneous transmission mechanisms between prices and unemployment: energy prices exhibit weaker direct links to unemployment, partly influenced by demand inelasticity and policy adjustments; agricultural products face more persistent impacts, reflecting policy interventions and demand constraints; chemical products demonstrate the highest sensitivity and fastest response to unemployment shocks; metals show significant internal variations, with sub-market-level impacts being more pronounced yet shorter-lived. These insights advance climate and energy economics by guiding low-carbon transition policies, optimizing resource allocation, and managing energy market risks for resilient economic sustainability. Full article

The present study demonstrates encapsulation of Coriandrum sativum essential oil in chitosan nanoemulsion and its effectiveness against fungal infestation and fumonisin B₁ (FB₁)- and B₂ (FB₂)-mediated biodeterioration of stored rice samples. Mycoflora analysis of different rice varieties revealed fungal occurrence and Fusarium proliferatum-BRC-R2 as the most toxigenic strain with highest FB₁- and FB₂-producing potentiality. GC-MS analysis of Coriandrum sativum essential oil (CEO) revealed linalool as the major component. The CEO-loaded chitosan nanoemulsion (Ne-CEO) was characterized by Scanning electron microscopy, X-ray diffractometry, Dynamic light scattering, and Fourier transform infrared spectroscopy. The Ne-CEO showed better antifungal and anti-fumonisin effectiveness as compared to unencapsulated CEO. The antifungal mechanism was associated with reduced ergosterol content, efflux of ions, proteins, nucleic acids, and destruction of plasma membrane integrity. The in silico interaction of linalool with Fum 1 protein confirmed the molecular action of anti-fumonisin activity. Additionally, the Ne-CEO displayed improved antioxidant activity and promising antifungal and anti-fumonisin activity during in situ investigation in rice samples (Gobindobhog variety) along with inhibition of the deterioration of carbohydrate, protein content, and lipid peroxidation without altering organoleptic properties and seed germination potentiality. Overall, the investigation strengthens the potentiality of Ne-CEO as a novel preservative of stored food commodities. Full article

Some special volatile organic compounds (VOCs) that significantly induce female oviposition preferences may be utilized to disrupt oviposition behavior and to enhance trapping strategies; such approaches offer a promising avenue for reducing insect infestations in stored commodities. Based on the significant differences in the oviposition preference of P. interpunctella among six normal-oleic varieties (NOPs), the key VOCs involved were further explored. Seventeen VOCs that may contribute the oviposition preference and that exhibited a high content in the peanut varieties were measured through electroantennogram (EAG) response measurements of female moths. The VOCs that produced significant EAG responses by the females were further assayed for behavioral responses by the Y-tube olfactometer method, wind tunnel tests, and a multiple-choice device for female oviposition. Heptanal, acetophenone, nonanal, hexanal, benzaldehyde, octanal, hexanoic acid, decanal, phenylacetaldehyde, and 1-octen-3-ol from peanuts elicited strong antennal EAG responses. These VOCs (especially heptanal, nonanal, hexanal, octanal, and decanal) attracted more females in both Y-tube olfactometer and wind tunnel assays and increased oviposition rates in oviposition tests. The results indicate that heptanal, decanal, octanal, nonanal, and hexanal may be utilized to develop oviposition attractants for female moths further. Full article

A life cycle assessment was conducted to compare the impacts of peas and lentils produced in Canada, France, and Russia, transported to market in Western Europe, to assess the systems-level sustainability implications of changing production and consumption profiles of internationally traded commodity pulse crops. For all but 1–2 impact categories, imported Canadian peas and lentils outperformed those imported from Russia, due to the lower yields, higher levels of tillage, higher field-level emissions, and higher distances of truck transportation for Russian pulses. French peas had higher impacts of production than Canadian peas, for all categories but land use, due to higher levels of fertilizer inputs, irrigation, field activities, and field-level emissions. However, for 7 out of 12 impact categories, the impacts of the transportation of Canadian peas to Western Europe outweighed the higher impacts of the production of French peas. This demonstrates potential sustainability benefits of Canadian pulses, with some trade-offs from the additional impacts of transportation to market, adding nuance to the discussion around the importance of “food miles” in agricultural sustainability. Compared to previous studies, this demonstrates the importance of multi-criteria and regionalized assessments. Full article

In the digital era, commodity barcodes serve as a bridge between the physical and digital worlds and are widely used in retail checkout systems. To meet the broader application demands for product identification, this paper proposes a method for locating, semantically segmenting barcodes in complex backgrounds, decoding hidden information, and recovering these barcodes in wide field-of-view images. This method integrates self-attention mechanisms and reparameterization techniques to construct a RepSAU-Net model. Specifically, this paper first introduces a barcode image dataset synthesis strategy adapted for deep learning models, constructing the SBS (Screen Stego Barcodes) dataset, which comprises 2000 wide field-of-view background images (Type A) and 400 information-hidden barcode images (Type B), totaling 30,000 images. Based on this, a network architecture (RepSAU-Net) combining a self-attention mechanism and RepVGG reparameterization technology was designed, with a parameter count of 32.88 M. Experimental results demonstrate that this network performs well in barcode segmentation tasks, achieving an inference speed of 4.88 frames/s, a Mean Intersection over Union (MIoU) of 98.36%, and an Accuracy (Acc) of 94.96%. This research effectively enhances global information capture and feature extraction capabilities without significantly increasing computational load, providing technical support for the application of data-embedded barcodes. Full article

The systematic failure of standard Value-at-Risk (VaR) models for the Tokyo Commodity Exchange (TOCOM) rubber futures contract poses significant challenges for risk management. This study addresses the issue by examining the market’s split trading sessions, which induce distinct overnight and intraday volatility regimes. We decompose daily returns into these two components and apply tailored Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models. Our empirical results, strengthened by extensive robustness checks using EGARCH, IGARCH, and GJR-GARCH specifications, reveal that intraday volatility is persistent and influenced by leverage effects, whereas overnight volatility behaves as a jump-driven process unaccounted for by conventional models. Comprehensive VaR backtesting confirms that while traditional models accurately capture intraday risk, all standard daily models—including asymmetric variants—systematically and severely underestimate overnight risk. These findings demonstrate that aggregating returns into a single daily series conflates different volatility dynamics, leading to model failures. We propose a two-tiered risk management framework that separately applies conventional models to intraday risk and jump-aware measures for overnight risk. This approach aligns risk assessment with underlying market microstructure, improving model validity and capital adequacy for TOCOM rubber futures. Full article