Search Results (114)

In complex multi-criteria decision-making scenarios, the inherent ambiguity of evaluation data and the frequent unavailability of complete attribute weight information pose significant challenges for domain experts. To address these methodological limitations, this study proposes a novel TOPSIS-based decision-making framework that integrates optimization algorithms with probabilistic linguistic term sets (PLTSs). Specifically, a distance measurement optimization model is constructed to objectively resolve the issue of incomplete attribute weight information. This mathematical approach enables the seamless fusion of qualitative expert judgments with quantitative metrics, effectively managing uncertainty and information deficiency in the decision-making process. To validate the practical viability and superiority of the proposed methodology, it is applied to an empirical case study of supplier selection in the cold chain logistics sector for fresh and perishable commodities. The evaluation encompasses three core dimensions: (i) environmental sustainability and energy efficiency, (ii) quality assurance and operational control, and (iii) supply chain collaboration and resilience. Empirical findings demonstrate that the proposed methodological framework substantially strengthens the robustness and reliability of selection outcomes under information-deficient conditions. Relative to conventional approaches, the developed framework demonstrates superior mathematical adaptability and effectively captures decision distortions, thereby offering rigorous theoretical contributions to decision-making under uncertainty and providing actionable practical guidance for complex supply chain evaluations. Full article

Accurate demand forecasting across multiple aggregation levels is essential for managing complex supply networks, where operations must balance inventory costs, service levels, and resource coordination under non-stationary and heterogeneous demand patterns. Existing spatiotemporal models typically treat all forecasting units at a single resolution, obscuring inherent hierarchical structures and often producing inconsistent predictions across levels. This study proposes a Hierarchical Hybrid Spatio-Temporal Demand Forecasting (H2SDF) architecture that formulates multi-granularity forecasting as a coupled system-of-systems problem. H2SDF decomposes the task into three coordinated layers. At the macro layer, a frequency-aware model extracts global trends and multi-scale periodicities from aggregate demand, providing a stable system-level reference. At the meso layer, a Transformer-based multi-task learner disaggregates the macro signal into location-specific forecasts while learning dynamic inter-location dependencies via self-attention, avoiding reliance on predefined static graphs. At the micro layer, gradient-boosted tree models refine category-level predictions by fusing upstream signals with contextual covariates to correct residual errors. A top-down coupling mechanism propagates forecasts and consistency constraints across layers. Experiments on a 2976 h real-world dataset with 18 locations and 8 product categories demonstrate that H2SDF reduces RMSE and improves R² compared with state-of-the-art baselines across all three granularities. The results confirm that hierarchical decomposition with heterogeneous model synergy effectively mitigates demand uncertainty and strengthens decision support for inventory, logistics, and workforce planning. Full article

Lactic acid bacteria (LAB) are valuable natural bio-preservatives due to their ability to produce antimicrobial compounds such as organic acids, hydrogen peroxide, and bacteriocins. This study aimed to isolate and characterize LAB from Moroccan camel meat and evaluate their antimicrobial potential against major foodborne pathogens. From 2304 isolates obtained from fresh, fermented, and dried camel meat, 115 exhibited antimicrobial activity against Listeria monocytogenes, Salmonella enterica Enteritidis, and Staphylococcus aureus. Seven isolates demonstrated broad-spectrum activity with inhibition zones ranging from 15 to 30 mm. Physiological and biochemical tests, combined with API 20 Strep identification, revealed that most isolates belonged to Enterococcus faecium. These isolates are promising candidates for natural preservation of camel meat, offering a sustainable alternative to synthetic preservatives. These findings highlight the potential of camel-meat-associated lactic acid bacteria as natural, clean-label bio-preservatives, particularly in arid regions where camel meat serves as a vital protein source and limited cold-chain infrastructure increases the risk of spoilage. Full article

The primary shelf life (PSL) of fresh vegan spinach gnocchi packaged under a modified atmosphere (MAP) was investigated. Microbiological, physicochemical, and sensory properties were monitored during storage at three temperatures (4, 8, and 12 °C). The microbial load remained below the limit considered safe (3 log CFU g⁻¹) in all samples during storage at all tested temperatures. Storage time significantly increased the hardness of uncooked gnocchi (p < 0.05) and the water absorption index (p < 0.05). Moreover, at higher storage temperatures, the kinetic rate of hardness decreased in uncooked gnocchi (0.29 N day⁻¹ at 12 °C vs. 0.35 N day⁻¹ at 4 °C). Conversely, in cooked gnocchi, as the storage temperature increased, the rate of hardness acceleration increased. The sensory analysis results varied according to storage temperature, and the Overall Quality Index (OQI), combined with principal component analysis (PCA), was used to determine PSL values. The Arrhenius relationship successfully described the temperature dependence of reaction rate constants, and the calculated Q₁₀ value (3.0) confirmed hardness as the quality attribute most affected by temperature. OQI showed a strong correlation with cooked-gnocchi hardness, and a sensory cutoff of 6.5 was established and confirmed by the sensory panel. The corresponding hardness rejection value was 12.1 N. The PSL was estimated based on sensory and texture criteria, as microbial quality was not a limiting factor. Under non-isothermal cold-chain conditions, PSL was predicted using the time–temperature tolerance (TTT) approach, yielding a value of 42 ± 3 days. Full article

This study compares two policy instruments for decarbonizing China’s seafood exports to the EU and UK over 10 years using a recursive dynamic computable general equilibrium model. One instrument applies tariff-like carbon surcharges on embedded emissions at the border. The other recognises certified low-carbon production through tiered rate reductions or exemptions. The model constructs product-level carbon cost wedges for processing electricity, aluminium packaging, and cold-chain operations, then transmits them to border prices through pass-through and to import volumes through Armington demand. These mechanisms operate inside a dynamic setting with capital accumulation, learning, and technology adoption. We evaluate processed tuna, shrimp, whitefish, and fresh tilapia to reflect differences in energy use, packaging intensity, and cold-chain reliance. Results show that certification, especially when paired with targeted domestic green finance or tax offsets, speeds adoption of cleaner power and refrigerants and preserves market share better than uniform surcharges. Effects differ between coastal and inland production hubs, supporting location-specific policy bundles. Sensitivity analysis varies carbon prices, adoption speeds, and certification coverage within stated parameter ranges. We report trade, export revenue, emissions, investment, and welfare outcomes and identify product and channel drivers of exposure. Full article

In many developing regions, the lack of a continuous cold chain poses a significant challenge for the preservation of table eggs. This study developed bioactive coatings based on citric pectin enriched with Cordia verbenacea DC aqueous extract to maintain egg quality under non-refrigerated conditions (25 days). A total of 144 fresh eggs were divided into a Control group and five treatment groups with increasing extract concentrations (0% to 100%). Quality was assessed through physical, chemical, and microbiological parameters, supported by principal component analysis (PCA) and random forest (RF) modeling. The results showed that all coated eggs maintained significantly higher Haugh units (classified as Grade B) compared to the control (grade C) (p < 0.05). The microbial load on the shell, a fundamental indicator of sanitary-hygienic conditions, was reduced from 70.0 ± 5.8 CFU/egg in the control to zero in the 100% extract treatment. The RF model achieved 97.06% accuracy in classifying the treatments, identifying microbial load and Haugh unit as the primary predictors of quality. This bioactive coating represents a sustainable and low-cost technology to enhance the shelf life and safety of eggs in markets without refrigeration infrastructure. Full article

Ensuring product integrity across Malaysia’s East Malaysian states (Sabah and Sarawak) requires a cold chain that is resilient to tropical heat, long multimodal routes, intermittent power, and dispersed rural populations. This paper proposes a sustainability-first architecture for vaccine and blood component logistics that combines World Health Organization and the United Nations International Children’s Emergency Fund Effective Vaccine Management (EVM 2.0) criteria with energy-aware transport planning, solar-hybrid edge refrigeration, phase-change materials, and digital temperature monitoring compliant with ISO 23412 for temperature-controlled delivery services. In this study, a mixed-methods methodology was employed, including (1) route and mode optimization under temperature risk and carbon intensity constraints; (2) equipment right-sizing using duty-cycle energy models and IEC 60068 environmental tests as design baselines; (3) governance with real-time earned value management (EVM) and key performance indicators (KPIs); and (4) scenario analysis for riverine, road, air, and drone last-mile segments relevant to remote East Malaysian communities. Results from realistic logistic scenarios indicate a 45–65% reduction in dose-weighted temperature-excursion minutes, 28–41% reduction in CO₂e per successful dose delivered, and 35–52% reduction in product loss compared with status quo planning. For blood components, solar-hybrid storage and mixed-mode routing reduced breach risk by 37% while maintaining red cells (2–6 °C), platelets (20–24 °C, continuous agitation surrogate), and fresh frozen plasma (≤−18 °C) requirements aligned with WHO guidance and Malaysia’s national transfusion policies. We provide a reference architecture, implementation bill of materials, and an EVM-aligned KPI dashboard to guide scale-up. Full article

Fresh agricultural products are highly perishable, and inadequate preservation leads to food loss and supply chain inefficiency, undermining sustainability. This study develops a continuous-time differential game model to analyze dynamic pricing and cold-chain investment decisions in a two-echelon fresh agricultural produce supply chain under government intervention. Two subsidy regimes are examined: one targeting suppliers’ cold-chain investments and another supporting the retailer based on sales volume. By explicitly modeling the dynamic evolution of product freshness, we analyze how subsidy intensity and allocation influence firms’ strategies, market outcomes, and social welfare over time. The results show that when initial freshness is low, firms consistently adopt a penetration pricing strategy and increase cold-chain investment irrespective of subsidy intensity. In contrast, when initial freshness is high, a critical subsidy threshold emerges: Below this threshold, firms employ skimming pricing and reduce investment, whereas above it, they switch to penetration pricing and raise investment. Under equal government expenditure, supplier subsidies achieve higher product freshness but raise retail prices, while retailer subsidies lower prices and stimulate demand, albeit with more modest freshness improvements. Welfare effects are non-linear: supplier subsidies are more effective at low intensities, whereas retailer subsidies become superior beyond a specific threshold. These findings provide actionable insights for designing sustainable, targeted subsidy policies in fresh agricultural supply chains. Full article

With rising customer demands for the timeliness and quality of refrigerated goods, the efficiency and fluidity of cold chain logistics remain inadequate, resulting in a notable imbalance between supply and demand in the cold chain market. To reduce the damage of fresh produce and lower logistics costs, this paper introduces multimodal transportation into the cold chain market and performs an analysis of optimizing multimodal transportation routes for refrigerated goods. This study constructs a mixed-integer programming model for cold chain multimodal transportation, aiming to minimize total costs while considering carbon emissions and uncertain demand. An improved adaptive large neighborhood search (ALNS) algorithm is developed to solve the mathematical model, featuring improved adaptive scoring and operator selection mechanisms. The algorithm’s performance is validated through a real-world multimodal transportation network in China. Furthermore, a sensitivity analysis is performed on rail freight rates, confidence levels, and ambient temperature, from which we derive managerial insights with practical significance. Full article

Monitoring the freshness of Salmo salar within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of modern industry. Here, we present a novel detection framework synergizing hyperspectral imaging (400–1000 nm) with the Transformer deep learning architecture. Through a rigorous comparative analysis of twelve preprocessing protocols and four feature wavelength selection algorithms (Lasso, Genetic Algorithm, Successive Projections Algorithm, and Random Frog), prediction models for Total Volatile Basic Nitrogen (TVB-N) and Total Viable Count (TVC) were established. Furthermore, the capacity of the Transformer to capture long-range spectral dependencies was systematically investigated. Experimental results demonstrate that the model integrating Savitzky-Golay (SG) smoothing with the Transformer yielded optimal performance across the full spectrum, achieving determination coefficients (R²) of 0.9716 and 0.9721 for the Prediction Sets of TVB-N and TVC, respectively. Following the extraction of 30 characteristic wavelengths via the Successive Projections Algorithm (SPA), the streamlined model retained exceptional predictive precision (R² ≥ 0.95) while enhancing computational efficiency by a factor of approximately six. This study validates the superiority of attention-mechanism-based deep learning algorithms in hyperspectral data analysis. These findings provide a theoretical foundation and technical underpinning for the development of cost-effective, high-efficiency portable multispectral sensors, thereby facilitating the intelligent transformation of the aquatic product supply chain. Full article

This study investigated fresh pork and pork subjected to repeated freeze–thaw cycles. The effects of freeze–thaw treatments on water status, WHC, and quality attributes of pork were systematically analyzed, and a nondestructive prediction method for WHC based on magnetic resonance imaging (MRI) was developed. The results showed that increasing freeze–thaw cycles significantly reduced moisture content and increased drip loss, indicating a continuous deterioration of overall WHC. Texture parameters and shear force values decreased markedly, suggesting that muscle structure was progressively damaged by ice crystal formation and recrystallization. T₂-weighted MRI pseudo-color scans clearly reflected changes in internal water distribution, with high-signal regions gradually decreasing as freeze–thaw cycles increased, which was consistent with the experimentally measured trends in moisture content and WHC. Based on MRI features, principal component regression (PCR) and partial least squares regression (PLSR) models were established to predict pork WHC. The PCR model extracted 16 principal components (cumulative contribution rate of 96.394%), with calibration set results of Rc² = 0.8825 and RMSEC = 1.7959, and validation set results of Rp² = 0.8856 and RMSEP = 2.0284. The optimal number of latent variables for the PLSR model was six, yielding calibration set results of Rc² = 0.9634 and RMSEC = 1.0026, and validation set results of Rp² = 0.9656 and RMSEP = 1.1119, with all residuals less than 1. Overall, the combination of MRI and chemometric methods, particularly the PLSR model, enables rapid, nondestructive, and accurate prediction of pork WHC, providing a useful tool for quality evaluation under repeated freeze–thaw conditions and for quality control in pork processing, storage, and cold-chain management. Full article

Frozen vegetables are increasingly valued for their nutritional stability and year-round availability. This study provides a comprehensive assessment of twenty commercially available frozen vegetable blends obtained from retail markets in Poland. Analyses included physicochemical parameters, instrumental measurements of texture, color (CIEL*a*b*), and evaluation of technological quality. The pH values ranged from 4.40 to 7.46, total acidity from 0.034 to 0.322 g/100 g, and dry matter content from 5.02 to 42.97%. The observed variability was mainly attributable to vegetable type and remained consistent with values reported for fresh produce, indicating that industrial freezing largely preserves chemical characteristics. Texture differed markedly between vegetable types, with hardness values ranging from 6 to nearly 100 N, while color parameters remained within typical ranges for blanched and frozen vegetables, suggesting effective pigment stability and enzyme inactivation. In contrast, substantial variability was observed in technological quality. Mechanical fragmentation exceeded acceptable limits in 30% of samples, and complete clumping of vegetable pieces (100%) was observed. Additional defects included frostbite and color deviations, and health-condition defects were observed. These results highlight considerable heterogeneity in frozen vegetable blends and emphasize the need for stricter control of raw materials, processing conditions, and cold-chain management to ensure consistent quality and consumer safety. Full article

Fresh fruits are inherently prone to postharvest deterioration due to loss of moisture, respiration, mechanical damage, and microbial decay, making quality preservation a persistent challenge across fresh fruit supply chains. While conventional plastic packaging offers barrier protection and cost-efficiency, its environmental footprint, particularly poor biodegradability and increasing incidence of plastic waste necessitates a transition toward more sustainable alternatives. Among these, the use of edible coatings, primarily based on natural biopolymers, have emerged as a versatile strategy capable of modulating transpiration, gas exchange, microbial activity, and sensory quality while addressing environmental concerns. Unlike biodegradable plastic films, edible coatings directly interface with the fruit surface and offer multifunctional roles extending beyond passive protection. This review synthesizes recent advances in edible coatings for fresh fruits, with emphasis on material classes, functional performance, optimization strategies, and analytical evaluation methods. Key findings indicate that polysaccharide-based coatings provide adequate gas permeability but limited moisture resistance, while nanocomposite and multi-component systems enhance water-vapor barrier performance without compromising respiration compatibility. Incorporation of bioactive agents such as essential oils, nanoparticles, and plant extracts further extends shelf life through antimicrobial and antioxidant mechanisms, though formulation trade-offs and sensory constraints persist. The review also highlights critical limitations, including variability in barrier and mechanical properties, challenges in industrial-scale application, insufficient long-term validation under commercial cold-chain conditions, and regulatory uncertainty for active formulations. Future research priorities are identified, including mechanistic transport–physiology integration, standardized performance metrics, scalable application technologies, and life-cycle-informed material design. Addressing these gaps is essential for transitioning edible coatings from experimental sustainability concepts to robust, function-driven solutions for fresh-fruit preservation. Full article

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article