Search Results (133)

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories. Full article

Production of dairy products is a crucial component of food security. The situation in the dairy sector affects not only the supply of the population with dairy products but also the overall sustainable development of the country. The main purpose of this publication is to determine the forecasted need for investment in innovations for Ukrainian enterprises engaged in the production of milk and cream in order to achieve sustainable development goals. The study employed the following economic research methods: the inductive method—for collecting, systematizing, and processing information; the deductive method—for theoretical interpretation of the problem; analysis and synthesis—for assessing the investment attractiveness of dairy enterprises and examining the components of sustainable development and their interrelationships. The primary data on enterprises engaged in the production of milk and cream were collected and systematized for large, medium, and small enterprises based on the information from the State Statistics Service of Ukraine. The study substantiates the impact of investments in the modernization of the dairy industry on achieving sustainable development goals. Integral indicators of the investment attractiveness of Ukrainian milk and cream producers were calculated, revealing that large enterprises are the most suitable for absorbing investments aimed at production modernization. An analysis of milk and cream production volumes by large enterprises in Ukraine for 2014–2024 was conducted, demonstrating that in 2023–2024, production began to grow after the crisis of 2021–2022. Based on historical production dynamics, a forecast for 2026–2030 was developed. It was determined that under the pessimistic scenario, production will reach 291.79 thousand tons in 2030, under the realistic scenario, 349.84 thousand tons, and under the optimistic scenario, 407.88 thousand tons. The key factors influencing the pessimistic, realistic, and optimistic projections were identified. Since the realistic scenario enables the most comprehensive consideration of influencing factors, the calculation of investment needs for the modernization of large milk and cream producers was based on this scenario. It was established that to meet EU product quality standards, comply with sustainable development goals, and accommodate the projected increase in production, the total investment required for the modernization of large enterprises engaged in the production of milk and cream in Ukraine should amount to 126 million euros by 2030. Full article

Financialisation has an increasing influence on the functioning of non-financial enterprises. It is therefore important to examine whether and to what extent food sector enterprises are subject to the process of financialisation. The research objective was to determine the level of financialisation of food industry enterprises in Poland in relation to the whole industry sector. To achieve this objective, the following research hypothesis was formulated: the process of financialisation of food industry enterprises proceeds similarly to the analogous process undergoing in industrial enterprises but varies across different sectors of the food industry. The research was conducted on the basis of statistical data from Statistics Poland (SP) published in various statistical studies. Financial data from 2010 to 2023 were analysed. For this purpose, research tools used in the paper are referred to in the literature as measures of the level of financialisation, so-called balance sheet indicators. The main limitation of the research is that the results can only be applied to countries with similar economic conditions, especially post-communist countries, and that balance sheet indicators are used to measure financialisation, which, although widely used, are limited in their effectiveness because they focus only on balance sheet data. The results support the research hypothesis. The companies in the analysed industries are characterised by a low level of financialisation. The process of financialisation of food industry companies is similar to the one in industrial companies and is more intense in beverage production than in other food industry sectors. There is room for a sustainable financing policy. The results indicate that there is room for higher financing of food industry enterprises in Poland, but excessive financing may lead to excessive concentration and monopolisation of enterprises and even to speculation on agricultural markets. To maintain financial stability, it will be important to pursue a stable monetary policy, limit the risk of food price volatility, improve communication and coordination in international monetary policy, and increase national food self-sufficiency. This study fills a research gap in understanding the process of financialisation, assessing its degree of advancement and diversity in the main sectors of food processing enterprises. Full article

Industrial heat exchangers are widely used in industries such as petrochemicals, energy and power, and food processing, making them one of the most important pieces of heat and mass transfer equipment in industry. During operation, a layer of fouling often adheres to the heat transfer surfaces, which reduces the heat transfer coefficient of the equipment and increases the thermal resistance of the surfaces. Additionally, fouling can corrode the material of the heat transfer surfaces, compromise their integrity, and even lead to perforations and leaks, severely impacting equipment operation and safety while increasing energy consumption and costs for enterprises. The application of anti-fouling coatings on surfaces is a key technology to address fouling on heat transfer surfaces. This paper focuses on introducing major types of anti-fouling coatings, including polymer-based coatings, “metal material + X”-type coatings, “inorganic material + X”-type coatings, carbon-based material coatings, and other varieties. It analyzes and discusses the current research status and hotspots for these coatings, elaborates on their future development directions, and proposes ideas for developing new coating systems. On the other hand, this paper summarizes the current research on the main factors—surface roughness, surface free energy, surface wettability, and coating corrosion resistance—that affect the anti-fouling performance of coatings. It outlines the research hotspots and challenges in understanding the influence of these three factors and suggests that future research should consider the synergistic effects of multiple factors, providing valuable insights for further studies in the field of anti-fouling coatings. Full article

Aquaculture is the fastest-growing food production sector globally. As its largest producer, China plays a pivotal role in ensuring aquatic food supply and supporting the blue economy. Despite its massive scale, a systematic understanding of the geographic distribution, structural composition, and drivers of China’s aquaculture value chain remains limited. We comprehensively characterized the sector’s composition, spatiotemporal evolution, and structural dynamics. We compiled and analyzed over 2.85 million enterprise registration records from the TianYanCha database, applying rigorous industry classification, spatial mapping, correlation analysis, and bottleneck assessment with natural and socioeconomic variables. Results show that policy reforms, notably the 2013 Company Law amendment and 2016 aquaculture certification measures, drove sharp increases in enterprise registrations, particularly in retail and farming. Enterprises are highly clustered in the Yangtze River Basin, Pearl River Delta, and southeastern coast, with inland expansion along major river systems. Strong interdependencies exist among sectors, while wholesale remains numerically scarce, forming a structural bottleneck. Standardization levels are low. Foreign investment, though under 5%, concentrated in processing and distribution, contributed to advanced technologies in the 1990s–2000s. These findings highlight rapid formalization, regional clustering, and structural imbalances, suggesting that enhancing formalization and addressing intermediary bottlenecks could improve sector resilience and efficiency. Full article

This study constructs a Stackelberg differential game model for green technology invest-ment in the food industry under a governmental coordination mechanism. The optimal dynamic strategies for local governments and enterprises are derived using Pontryagin’s maximum principle. The backward differential equation method is employed in this study. It is used to analyze the impact of shadow prices on the optimal decisions of both parties. Furthermore, the study examines how social welfare benefits influence the food quality levels within the jurisdiction of local governments. Based on these findings, optimal strategy pathways are proposed to achieve social welfare and enterprise profit maximization in the green transition process of both government and enterprises. The results indicate that a local government’s investment in food quality improvement significantly enhances the food quality levels within their jurisdictions—greater government investment leads to higher food quality. At the same time, food quality levels are positively correlated with the enterprises’ green technology capital investment. Additionally, consumer price sensitivity and sensitivity to price differences have a notable impact on product pricing. As consumers become more price-sensitive, product prices decrease accordingly, which, in turn, helps increase the market share of the enterprises’ products. Full article

The increasing demand for halal-certified food has intensified the need for micro, small, and medium enterprises (MSMEs) to adopt performance frameworks that capture both operational efficiency and halal compliance. Although previous studies have applied Balanced Scorecard (BSC) to evaluate supply chain (SC) performance, the incorporation of halal compliance into this framework has not been examined, especially in the context of MSMEs. In this study, we developed and tested a BSC-based framework that embeds halal principles to assess SC performance. The research involved 130 MSMEs and 130 consumers engaged in the fried chicken sector in Indonesia. An exploratory mixed-methods design was applied, combining qualitative interviews with quantitative analysis using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The findings revealed customer perspective (CP) as the strongest driver in SC performance (β = 0.496, p < 0.001), whereas financial, business process, and growth perspectives demonstrated comparatively weaker or statistically insignificant effects. Furthermore, the findings underscore that consumer trust and halal adherence represent strategic assets for MSMEs, enabling stronger competitiveness and sustainable operations. By incorporating halal compliance with performance measurement theory, this study provides novel insights and practical guidelines for MSMEs and policymakers to strengthen halal food supply chains in emerging markets. Full article

This research investigates the synergistic relationships between Green Supply Chain Management (GSCM) practices and product innovation in marketing performance and organizational sustainability within Thailand’s processed food industry. Building upon Resource-Based View theory and Stakeholder Theory, this study addresses a critical gap in understanding how environmental practices interact with innovation strategies to create sustainable competitive advantages in emerging markets. The research employs a comprehensive mixed-methods approach, integrating qualitative insights from industry expert interviews with quantitative analysis through Structural Equation Modeling (SEM). Primary data were systematically collected from 300 strategically selected enterprises representing small (≤50 employees), medium (51–200 employees), and large-scale (>200 employees) operations across diverse product categories within Thailand’s processed food sector. The analytical framework examines three core GSCM dimensions—green purchasing, green production, and green distribution—alongside three innovation aspects—quality innovation, safety innovation, and sustainability innovation. Eleven hypothesized relationships were rigorously tested to examine direct and indirect effects on marketing performance indicators (sales growth, market share expansion, brand enhancement, customer satisfaction, and cost optimization) and organizational sustainability metrics (environmental impact reduction, regulatory compliance, competitive positioning, and resource efficiency). SEM results revealed that Green Production practices significantly enhance marketing performance (β = 0.16, p < 0.01), demonstrating the strategic value of environmentally responsible production processes in achieving market success. Conversely, Green Distribution exhibited negative effects on both marketing performance (β = −0.106, p < 0.10) and organizational sustainability (β = −0.152, p < 0.05), indicating potential operational trade-offs and infrastructure limitations that require strategic optimization. The model demonstrated excellent fit indices (GFI = 0.929, CFI = 1.000, TLI = 1.000, RMSEA = 0.000, RMR = 0.034), validating the theoretical framework’s robustness. However, modest explanatory power (R² MP = 0.050, R² OS = 0.029) suggests that additional contextual factors, firm-specific capabilities, and market dynamics significantly influence these outcomes, warranting future investigation of mediating and moderating variables. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

Amidst global climate warming and increasingly severe food security challenges, the agroforestry economy, a green ecological industry that balances ecological conservation and economic development, has attracted widespread attention. This study constructs a theoretical analytical framework based on the diamond model to systematically identify key factors influencing the development of the agroforestry economy. Using 56 practical cases from the agroforestry economy in China as samples, the study applies Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to further explore the multiple driving paths of agroforestry economic development and their supporting elements. The research findings show that (1) forest resources, technological innovation, market demand, enterprise forms, related industries, and government support do not constitute necessary conditions for the development of the agroforestry economy. The path to the development of the agroforestry economy exhibits complex and concurrent multi-faceted characteristics. (2) Technological innovation has always been at the core of all configurations, and strengthening technological innovation plays a universal role in enhancing the level of agroforestry economic development. The role of government support in the process of the development of the agroforestry economy is limited. (3) The system identified four driving paths, including the endogenous type, characterized by resource technology enterprises; the collaborative type, characterized by a resource technology market with light promotion by the government; the external expansion type, characterized by market technology enterprises; and the linkage type, characterized by market technology enterprises assisted by related industries. The consistency level of the overall solution reached 0.91, and the coverage was 0.54. It reveals the different driving mechanisms with different combinations of elements for the development of the agroforestry economy. Therefore, each region should strengthen scientific and technological research, innovation, and the transformation and application of research outcomes. It should promote the coordinated development of diverse factors, establish tailored regional development models, and explore suitable pathways for developing the agroforestry economy based on its unique resource endowments. Full article

Ensuring food safety in complex supply chains requires evaluation frameworks that integrate multiple indicators, account for their interdependencies, and incorporate historical performance. This study proposes a novel RM–Shapley–FAHP framework that combines the Fuzzy Analytic Hierarchy Process, Shapley value contribution analysis, and a reputation decay mechanism to construct a dynamic, multi-year assessment model. The framework evaluates six governance subsystems, mitigates indicator redundancy, and links past performance to current risk posture. Applied to a leading food enterprise over three years, the method demonstrated superior consistency, interpretability, and operational relevance compared to FAHP, entropy weighting, and equal-weight baselines. The results demonstrate that RM–Shapley–FAHP framework effectively supports balanced development in food safety governance by capturing temporal dynamics and interdependencies, offering interpretable and operationally relevant guidance for decision makers. In future work, this framework may be extended with machine learning to improve adaptability for multi-dimensional and time-series evaluations, noted here as a research prospect rather than a present contribution. Full article

The REPowerEU plan is aimed at a target of 35 bcm of biomethane annually by 2030, up from 4 bcm in 2023, requiring about EUR 37 billion in investment. Food waste is identified as a key feedstock, characterized by discrete homogeneity, although its availability may vary seasonally. In Italy, the Emilia-Romagna region generates approximately 450 kt/y of industrial waste from the food and beverage sector, primarily originating from meat processing (NACE 10.1), fruit and vegetable processing (NACE 10.3), and the manufacture of vegetable and animal oils and fats (NACE 10.4). Of this amount, food and beverage processing waste (EWC 02) accounts for about 302 kt from NACE 10 (food, year 2019) and 14 kt from NACE 11 (beverage, year 2019). This study provides a comprehensive screening of waste streams generated by the local food and beverage industry in Emilia-Romagna, evaluating the number of enterprises, their value added, and recorded waste production. The screening led to the identification of suitable streams for further valorization strategies: a total of ~93 kt/y was selected for the preliminary conceptual design of an integrated process combining anaerobic digestion with hydrothermal treatment, aimed at supporting national biomethane production targets while maximizing material recovery through hydrochar production. Preliminary estimations indicate that the proposed process may achieve a biochemical methane potential of approximately 0.23 Nm³/kg_VS, along with a hydrochar yield of about 130 kg/t_waste. 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

This paper focuses on the challenges in food safety governance in megacities, taking Shanghai as the research object. Aiming at the pain points in food sampling inspections, it proposes a risk prediction and regulatory optimization scheme combining text mining and machine learning. First, the paper uses the LDA method to conduct in-depth mining on over 78,000 pieces of food sampling data across 34 categories in Shanghai, so as to identify core risk themes. Second, it applies SMOTE oversampling to the sampling data with an extremely low unqualified rate (0.5%). Finally, a machine learning prediction model for food safety risks is constructed, and predictions are made based on this model. The research findings are as follows: ① Food risks in Shanghai show significant characteristics in terms of time, category, and pollution causes. ② Supply chain links, regulatory intensity, and consumption scenarios are among the core influencing factors. ③ The traditional “full coverage” model is inefficient, and resources need to be tilted toward high-risk categories. ④ Public attention (e.g., the “You Order, We Inspect” initiative) can drive regulatory responses to improve the qualified rate. Based on these findings, this paper suggests that relevant authorities should ① classify three levels of risks for categories, increase inspection frequency for high-risk products in summer, adjust sampling intensity for different business entities, and establish a dynamic hierarchical regulatory mechanism; ② tackle source governance, reduce environmental pollution, upgrade process supervision, and strengthen whole-chain risk prevention and control; and ③ promote public participation, strengthen the enterprise responsibility system, and deepen the social co-governance pattern. This study effectively addresses the risk early warning problems in food safety supervision of megacities, providing a scientific basis and practical path for optimizing the allocation of regulatory resources and improving governance efficiency. Full article

Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article