Search Results (149)

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

Food loss and waste (FLW) is a chronic problem across food systems worldwide, with meat being one of the most resource-intensive and perishable categories. The perishable character of meat, combined with complex cold chain requirements and consumer behavior, makes the sector particularly sensitive to inefficiencies and loss across all stages from production to consumption. This review synthesizes the latest advancements in new preservation technologies and supply chain efficiency strategies to minimize meat wastage and also outlines current challenges and future directions. New preservation technologies, such as high-pressure processing, cold plasma, pulsed electric fields, and modified atmosphere packaging, have substantial potential to extend shelf life while preserving nutritional and sensory quality. Active and intelligent packaging, bio-preservatives, and nanomaterials act as complementary solutions to enhance safety and quality control. At the same time, blockchain, IoT sensors, AI, and predictive analytics-driven digitalization of the supply chain are opening new opportunities in traceability, demand forecasting, and cold chain management. Nevertheless, regulatory uncertainty, high capital investment requirements, heterogeneity among meat types, and consumer hesitancy towards novel technologies remain significant barriers. Furthermore, the scalability of advanced solutions is limited in emerging nations due to digital inequalities. Convergent approaches that combine technical innovation with policy harmonization, stakeholder capacity building, and consumer education are essential to address these challenges. System-level strategies based on circular economy principles can further reduce meat loss and waste, while enabling by-product valorization and improving climate resilience. By integrating preservation innovations and digital tools within the framework of UN Sustainable Development Goal 12.3, the meat sector can make meaningful progress towards sustainable food systems, improved food safety, and enhanced environmental outcomes. Full article

When new purchasers or products are added in the supply chain management system, the recommendation system will face severe challenges of data sparsity and cold start. A knowledge graph that can enrich the representations of both procurement managers and products offers a promising pathway to mitigate the challenges. This paper proposes a knowledge-aware recommendation network for supply chain management, called KnoChain. The proposed model refines purchaser representations through outward propagation along knowledge graph links and enhances product representations via inward aggregation of multi-hop neighbourhood information. This dual approach enables the simultaneous discovery of purchasers’ latent preferences and products’ underlying characteristics, facilitating precise and personalised recommendations. Extensive experiments on three real-world datasets demonstrate that the proposed method consistently outperforms several state-of-the-art baselines, achieving average AUC improvements of 9.36%, 5.91%, and 8.81%, and average accuracy gains of 8.56%, 6.27%, and 8.67% on the movie, book, and music datasets, respectively. These results underscore the model’s potential to enhance recommendation robustness in supply chain management. The KnoChain framework proposed in this article combines purchaser-aware attention with knowledge graphs to improve the accuracy of purchaser SKU matching. The method can help enhance supply chain resilience and reduce returns caused by over-ordering, inventory backlog, and incorrect procurement. In addition, the model provides interpretable recommendation paths based on the knowledge graph, which improves trust and auditability for procurement personnel and helps balance environmental and operational costs. Full article

Berries—including strawberries, blueberries, raspberries, blackberries, cranberries, and several less commonly cultivated berry species—are highly valued for their sensory quality and rich content of bioactive compounds, yet they are among the most perishable horticultural products. Their soft texture, high respiration rate, and susceptibility to fungal pathogens lead to rapid postharvest deterioration and significant economic losses. This review synthesizes advances in berry postharvest management reported between 2010 and 2025. Conventional strategies such as rapid precooling, cold-chain optimization, controlled and modified atmospheres, and edible coatings are discussed alongside emerging non-thermal technologies, including UV-C light, ozone, cold plasma, ultrasound, biocontrol agents, and intelligent packaging systems. Particular emphasis is placed on the instability of anthocyanins and other phenolic compounds, microbial spoilage dynamics, and the influence of cultivar genetics and preharvest factors on postharvest performance. The review also highlights opportunities for circular-economy applications, as berry pomace, seeds, and skins represent valuable sources of polyphenols, dietary fiber, and seed oils for use in food, nutraceutical, cosmetic, and bio-based packaging sectors. Looking ahead, future research should prioritize integrated, multi-hurdle, low-residue postharvest strategies, the scale-up of non-thermal technologies, and data-driven cold-chain management. Overall, coordinated physiological, technological, and sustainability-oriented approaches are essential to maintain berry quality, reduce postharvest losses, and strengthen the resilience of berry value chains. Full article

Postharvest technologies and supply chain management are critical to improving food security, reducing losses, and advancing sustainability in global agri-food systems. Nearly one-third of global food is lost after harvest, particularly in developing regions, underscoring the urgent need for efficient postharvest handling, cold chain integration, and sustainable logistics systems. This paper explores key components of effective postharvest systems, including harvesting, treatments, storage, and value-added processing. It highlights digital innovations IoT sensors, blockchain, AI, and digital twins that enhance traceability, forecasting, and operational efficiency. Case studies from South Asia, Africa, Europe, and North America emphasize region-specific solutions, highlighting low-cost technologies for smallholders and advanced systems for export chains. Sustainable practices such as renewable-powered cold chains, circular economy models, and eco-friendly packaging align with Sustainable Development Goals (SDGs) on zero hunger, responsible consumption, and climate action. This paper concludes that while technology is vital, systemic transformation requires inclusive policies and collaboration among governments, private sectors, researchers, and farming communities to build resilient, equitable food systems. 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

Digital transformation (DT) is reshaping cold chain operations through technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. However, evidence remains fragmented, and a systematic synthesis focused on how these technologies affect cold chain performance, sustainability, and cost-efficiency is limited. This PRISMA-based systematic literature review analyzes 107 studies published between 2009 and 2025 to examine enabling technologies and application areas, operational and sustainability impacts, and the main adoption challenges. The reviewed evidence suggests that digitalization can improve real-time visibility, temperature control, traceability, and energy management, supporting waste reduction and improved quality assurance. Key challenges include high implementation costs and uncertain returns on investment, interoperability constraints, data governance and cybersecurity concerns, and organizational readiness gaps. The paper concludes with implications for managers and policymakers and a future research agenda emphasizing integrated multi-technology solutions, standardized sustainability assessment, and rigorous validation through pilots, testbeds, and real-world deployments to enable scalable and resilient cold chain digitalization. Full article

Accurate real-time prediction of labor productivity is crucial for the successful management of construction projects. However, it remains a significant challenge due to the dynamic and uncertain nature of construction environments. Existing models, while valuable for planning and post-analysis, often rely on historical data and static assumptions, rendering them inadequate for providing actionable, real-time insights during construction. This study addresses this gap by suggesting a novel hybrid AI-stochastic framework that integrates a Long Short-Term Memory (LSTM) network with Markov Chain modeling for dynamic productivity forecasting in repetitive construction activities. The LSTM component captures complex, long-term temporal dependencies in productivity data, while the Markov Chain models probabilistic state transitions (Low, Medium, High productivity) to account for inherent volatility and uncertainty. A key innovation is the use of a Bayesian-adjusted Transition Probability Matrix (TPM) to mitigate the “cold start” problem in new projects with limited initial data. The framework was rigorously validated across four distinct case studies, demonstrating robust performance with Mean Absolute Percentage Error (MAPE) values predominantly in the “Good” range (10–20%) for both the training and test datasets. A comprehensive sensitivity analysis further revealed the model’s stability under data perturbations, though performance varied with project characteristics. By enabling more efficient resource utilization and reducing project delays, the proposed framework contributes directly to sustainable construction practices. The model’s ability to provide accurate real-time predictions helps minimize material waste, reduce unnecessary labor costs, optimize equipment usage, and decrease the overall environmental impact of construction projects. Full article

Liner shipping companies commonly pursue strategies such as forming strategic alliances and attracting new customers to strengthen competitiveness and improve operational performance. However, in the shipping of perishable goods, inadequate ship scheduling and bunker management can result in substantial customer loss and increased operational costs. This paper examines a scenario in which a large volume of perishable goods is shipped by liner ships. The specific demand characteristics of perishable goods—requiring rapid port handling and expedited shipping—are analyzed. To address these challenges, we propose a mixed-integer nonlinear programming (MINLP) model to optimize ship scheduling and refueling decisions for liner cold chain services under cooperative agreements. The model minimizes total liner shipping service costs while explicitly accounting for the decay of perishable goods. Nonlinear elements are linearized using a piecewise linear secant approximation, enabling efficient solution of the model with commercial solvers. Numerical experiments based on the AEU6 route operated by China COSCO Shipping Group validate the model and provide practical managerial insights. The results indicate that: (1) incorporating collaborative agreements can reduce total route service costs by 4.5% and total port handling costs by 7.5%, while also lowering late arrival penalties and losses from perishable goods decay; (2) joint consideration of refueling strategies and collaborative agreements improves both decision flexibility and solution accuracy; (3) the shipping of perishable goods has differentiated effects across voyage legs, highlighting the need for liner shipping companies to enhance cooperation with ports and refine bunker fuel procurement planning; and (4) it is essential to improve ship performance and appropriately design bunker fuel tank capacity to respond to dynamic changes in the shipping market. Full article

Frozen storage modulates the progression of key oxidative and nitrogenous reactions within fish muscle. We therefore identify the drivers of quality degradation in filleted whiting (Merlangius merlangus) and Atlantic bonito (Sarda sarda) during 10-month frozen storage at −12, −18, and −24 °C, and to integrate state-of-the-art machine learning architectures to predict deterioration kinetics and shelf-life trajectories. To this end, following blast freezing at −30 °C for 6 h, samples were periodically (0, 2, 4, 6, 8, and 10 months) assessed for biochemical indices—total volatile base nitrogen (TVB-N), trimethylamine nitrogen (TMA-N), thiobarbituric acid (TBA), and free fatty acids (FFA)—in which proximate composition and pH were determined solely on the same day (Day 0). Whiting displayed progressive increases in all indices, yet values at −24 °C remained within regulatory acceptability, supporting a safe storage period of up to nine months. By contrast, Atlantic bonito retained TVB-N and TMA-N values below regulatory thresholds across storage, but TBA exceeded acceptability limits from the second month onward, and FFA rose after month four. Complementing these findings, machine learning (ML) approaches, including Naïve Bayes, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Extreme Gradient Boosting, were implemented to classify species and predict spoilage kinetics, with Extreme Gradient Boosting achieving the highest accuracy (98.9%, κ = 0.978) and Random Forest providing superior regression performance (R² = 0.986, RMSE = 0.392). ML models consistently identified TVB-N as the dominant predictor for whiting and TBA for Atlantic bonito, correctly capturing the critical time points of 9 months and 2 months, respectively, and highlighting −24 °C as the most reliable condition for preserving quality. These results underscore the potential of ML as a transformative tool for accurate shelf-life prediction and smarter cold-chain management in frozen fish products. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article

Understanding and controlling thermal rectification is pivotal for designing phononic devices that guide heat flow in a preferential direction. This study investigates one-dimensional atomic chains with binary mass asymmetry and nonlinear interatomic potentials, focusing on how energy propagates under thermal and wave excitation. Two potential models—the β-FPU and Morse potentials—were employed to examine the role of nonlinearity and bond softness in energy transport. Simulations reveal strong directional energy transport governed by the interplay of mass distribution, nonlinearity, and excitation type. In FPU chains, pronounced rectification occurs: under “cold-heavy” conditions, energy in the left segment increases from ~1% to over 63%, while reverse (“hot-heavy”) cases show less than 4% net transfer. For wave-driven excitation, the rectification coefficient reaches ~0.58 at 100:1. In contrast, Morse-based systems exhibit weaker rectification (∆E < 1%) and structural instabilities at high asymmetry due to bond breaking. A comprehensive summary and heatmap visualization highlight how system parameters govern rectification efficiency. These findings provide mechanistic insights into nonreciprocal energy transport in nonlinear lattices and offer design principles for nanoscale thermal management strategies based on controlled asymmetry and potential engineering. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

Microbial contamination in frozen beverages threatens public safety and food quality. By systematically analyzing safety incidents, potential microbial hazards can be identified. This study reviewed 155 microbial safety incidents related to frozen beverages reported by nine international regulatory agencies from January 2015 to December 2024, as well as 903 incidents published by the State Administration for Market Regulation of China. The results indicate a higher risk in Southeast Asia, particularly in Malaysia (16.13%) and Thailand (11.61%). In China, the risks are concentrated in South China (Guangdong, 14.52%), Northeast China (Liaoning, 10.20%; Heilongjiang, 9.87%), and the Huang-Huai-Hai region (Henan 6.87%; Shandong 5.99%). Statistical analysis reveals that non-compliance incidents related to coliforms account for 67.7% globally, while incidents involving pathogens such as Listeria monocytogenes, Staphylococcus aureus, Salmonella, and Norovirus account for 6.4%. The characteristics in the Chinese market align with global trends, with the highest proportion of coliform exceedance (41%), while the incidence of pathogenic contamination remains relatively low (0.6%). Further analysis of different types of frozen beverages (ice cream, ice milk, ice frost, ice lolly, sweet ice, edible ice, and others) and their association with microbial hazards reveals significant issues with ice cream products globally; however, in the Chinese market, the contamination problems with ice milk and ice lolly are more severe. This study provides regional and category-specific data for the microbial risk assessment of frozen beverages and offers guidance for regulatory agencies and enterprises to implement targeted control measures, including optimizing sampling plans, enhancing hygiene controls during production processes, and promoting compliance in cold chain management. Consequently, this approach effectively reduces the risk of foodborne diseases and enhances the overall safety level of the industry, demonstrating significant practical application value and public health significance. Full article