Food Quality Management Research Articles

Radioecological monitoring as a key factor in food quality management

Radioecological monitoring as a key factor in food quality management

Assessment of HACCP plans and Colombian regulations in municipal cattle slaughterhouses for the assurance of standardised food safety and quality management systems

Assessment of HACCP plans and Colombian regulations in municipal cattle slaughterhouses for the assurance of standardised food safety and quality management systems

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

QUALITY AND SAFETY MANAGEMENT OF FERMENTED MILK PRODUCTS FROM CAMEL MILK BASED ON HACCP PRINCIPLES

Currently, the main task of domestic producers of milk and dairy products is to provide consumers with high-quality and safe dairy products. At the same time, the main guarantee of safe production is the introduction of the HACCP system at enterprises.The main advantage of the HACCP system is error detection, namely prevention and prevention, through step-by-step control of the entire food production chain.The research work was carried out on the basis of the national standard SS RK 1179-2003 «Quality systems. Food quality management based on the principles of HACCP. General requirements». At the first stage, the necessary information about the fermented milk product from camel milk was collected (composition, organoleptic characteristics, indicators characterizing quality and safety, application, type of packaging, storage and delivery conditions) and a flowchart of its production process was compiled. At the second stage, the results of scientific research by other authors and regulatory documents related to the quality and safety of camel milk and products from it were examined to analyze chemical, biological and physical risks. Then an assessment of the probability of the identified dangerous risks was carried out. Further, using the Decision Tree tool, critical control points in the technology of the research object were identified, allowing to manage safety and improve quality in their production.

ASSESSMENT AND CURRENT TRENDS OF ENSURING FOOD SECURITY IN UKRAINE

The research on Ukraine's food security is of paramount importance, as it directly impacts the national agro-food systems. The article delves into the current state of internal food self-sufficiency in the Ukrainian population, conducting an analytical assessment of export-import transactions with agricultural raw materials and food products. The findings reveal a significant trend of increasing food exports in recent years, along with an evaluation of the export-import coverage for the main types of food products. Ukraine's place in the world rating of food security based on the Global Food Security Index was studied. The fundamental problems and reasons that determined Ukraine's decrease in the rating positions and the level of food security of its population were identified. In addition to the military conflict, Ukraine has a low economic availability of food products for the population and a low level of sustainability in the domestic agro-food system. Despite the war's consequences, the agricultural business remains one of the most profitable economic activities in Ukraine. This analysis of individual indicators of the effectiveness of the country's agrarian sector as a basis for ensuring food security underscores the sector's potential. The modern production structure of the main types of agricultural products by categories of producers was studied. The key trends accompanying ensuring food security in Ukraine under modern conditions are summarized. It was revealed that food security in the crop sector is achieved at the expense of the public sector agrarian formations, mainly at the expense of households in the livestock sector. Based on the average values ​​of the actual consumption indicators, the state of providing the population with the main types of food was analyzed. The results showed problematic aspects in providing the country's population with meat and dairy products, fish, fruits, berries, and grapes. The priority directions for ensuring and strengthening food security on a national scale are substantiated. Among the main ones, the following are highlighted: the need to implement sustainable farming and animal husbandry practices, further improvement of the food quality management and control system, protection of biodiversity of ecological agrosystems, optimization and reduction of value chains, increasing the level of social responsibility of food producers and consumers.

A bibliometric analysis of quality and food safety management in the food development industry

Quality and food safety management (QFSM) presents significant concerns for developing countries worldwide. Researchers have not yet reached a consensus on the most complicated QFSM practices in the food industry. Our study aims to fill the research gap by visualising and analysing 537 articles on QFSM using Biblioshiny. Relevant publications from Dimensions, Scopus, and the Web of Science databases expose the current research status from 1982 to January 2024, without bias. We classified publications about Food Safety Management (FSM) and Food Quality Management (FQM) into four stages: the appearance stage (1982–1995), the stage towards increase (1996–2005), a rapid rise phase (2006–2014), and stable, rapid growth between 2015 and 2024. The review of QFSM recorded 5714 paper references and 1677 authors from 57 countries or regions. The most popular keywords are food quality, food industry, quality, and food safety management. China became the first in the group with 84 publications, while Belgium had the highest citations, with 738 total citations and 49.20 average article citations. The most productive institution is “Abu Dhabi Food Control Authority, Abu Dhabi, United Arab Emirates,” while Food Control is the most locally cited source. This study is the first to analyse QFSM resilience using biblioshiny. It is necessary to continue studying the strength of the food industry supply chain related to QFSM. The report identifies critical areas for improvement and future research to assist governments and businesses in developing adaptable food supply networks.

NMR Immunosensor Based on a Targeted Gadolinium Nanoprobe for Detecting Salmonella in Milk.

Detecting harmful pathogens in food is not only a crucial aspect of food quality management but also an effective way to ensure public health. In this paper, a complete nuclear magnetic resonance biosensor based on a novel gadolinium (Gd)-targeting molecular probe was developed for the detection of Salmonella in milk. First, streptavidin was conjugated to the activated macromolecular polyaspartic acid (PASP) via an amide reaction to generate SA-PASP. Subsequently, the strong chelating and adsorption properties of PASP toward the lanthanide metal gadolinium ions were exploited to generate the magnetic complex (SA-PASP-Gd). Finally, the magnetic complex was linked to biotinylated antibodies to obtain the bioprobe and achieve the capture of Salmonella. Under optimal experimental conditions, the sensor we have constructed can achieve a rapid detection of Salmonella within 1.5 h, with a detection limit of 7.1 × 103 cfu mL-1.

Bi2WO6/TiO2-based visible light-driven photoelectrochemical enzyme biosensor for glucose measurement.

Nowadays, the frequent occurrence of food adulteration makes glucose detection particularly important in food safety and quality management. The quality and taste of honey are closely related to the glucose content. However, due to the drawbacks of expensive equipment, complex operating procedures, and time-consuming processes, the application scope of traditional glucose detection methods is limited. Hence, this study developed a photoelectric chemical (PEC) sensor, which is composed of a photoactive material of bismuth tungstate (Bi2WO6) with titanium dioxide (TiO2) and glucose oxidase (GOD), for simple and rapid detection of glucose. Notably, the composites' absorption prominently increased in the visible light region, and the photo-generated electron-hole pairs were efficiently separated by virtue of the unique nanostructure system, thus playing a crucial role in facilitating PEC activity. In the presence of dissolved oxygen, the photocurrent intensity was enhanced by H2O2 generated from glucose under electro-oxidation specifically catalyzed by GOD fixed on the modified electrode. When the working potential was 0.3V, the changes of photocurrent response indicated that the PEC enzyme biosensor providesalow detection limit (3.8µM), and a wide linear range (0.008-8mM). This method has better selectivity in honey samples and broad application prospects in clinical diagnosis for future.

Flow control by circular cavities in lateral flow porous membranes.

This research explores the flow penetration in porous media by virtue of capillary action and geometric control of the liquid imbibition rate in microfluidic paper-based analytical devices (μPADs) having applications in food quality management, medical diagnostics, and environmental monitoring. We examine changes in flow resistance and membrane geometry, aiming to understand factors influencing capillary penetration rates for various practical applications. We conducted experiments and simulations using lateral porous membranes and altered the flow resistance by changing the liquids or the paper channel geometry by adding cavities. From experiments, it was revealed that by creating a circular cavity in the paper channel, the penetration rate was sufficiently altered. Moreover, increasing the cavity size and type of liquid (w.r.t. viscosity) also caused a decrease in the flow rate. Imbibition rates were also influenced by the position of the cavities in the paper channel. The maximum delay for water was almost 2 times with a 16 mm circular cavity located at 3 cm from strip bottom edge. Overall, we attained a maximum delay in the case of castor oil which was almost 85 times slower than water and 3.7 times slower than olive oil. A good agreement was observed with CFD analysis. We believe that this research would help in developing advance techniques to enhance the flow control strategies in μPADs and indicators.

Frontiers of machine learning in smart food safety.

Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.

Relevansi Budaya Keamanan Pangan dengan Implementasi Teknologi Industri 4.0 di Industri Pangan Indonesia

The application of food safety culture (FSC) which is influenced by human behavior with the development of industrial technology (IT) 4.0 with less human resources provides a challenge to understand the relationship between them. The research objective was to provide quantitative data and suggestions for improving the implementation of the FSC dimensions in synergy with the progress of IT 4.0. This research involved 35 participants from 18 local companies and 17 multinational food companies (MFC/PMA) for the survey and invited 7 selected participants from both of them to join the FGD. The FSC survey showed that the gap organizational maturity in FSC implementation between local companies (2.93) and PMA (3.62) in Indonesia was 0.7 (world best practice 4.0 – 5.0). The three main benefits and opportunities for implementing IT 4.0 were effectiveness and efficiency, safe and quality products, and early detection to prevent non-conformities or product recalls. There were three main IT 4.0 application areas, namely production, quality, and engineering. The three main forms of IT 4.0 implementation were advanced robotics, big data, and internet of things (IoT). The FGD results showed the relevance between FSC and IT 4.0. Activities in IT 4.0 helped the food industry to manage food safety and quality management system better, because technical problems that previously took time and thought can be implemented effectively and efficiently. Data analysis can be carried out more in-depth, actual, and accurate. Further research is recommended to see the strength of the relationship between FSC and IT 4.0 parameters to determine critical areas with quantitative research methods and advanced statistical data processing.

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks.

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of storage conditions on the quality of dates (Khalas and Sukary cultivars) at the Tamer stage and predict their quality attributes during storage using artificial neural networks (ANN). The studied storage conditions were the modified atmosphere packing (MAP) gases (CO2, O2, and N), packaging materials, storage temperature, and storage time, and the evaluated quality attributes were moisture content, firmness, color parameters (L*, a*, b*, and ∆E), pH, water activity, total soluble solids, and microbial contamination. The findings demonstrated that the storage conditions significantly impacted (p < 0.05) the quality of the two stored date cultivars. The use of MAP with 20% CO2 + 80% N had a high potential to decrease the rate of color transformation and microbial growth of dates stored at 4 °C for both stored date cultivars. The developed ANN models efficiently predicted the quality changes of stored dates closely aligned with observed values under the different storage conditions, as evidenced by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. In addition, the reliability of the developed ANN models was further affirmed by the linear regression between predicted and measured values, which closely follow the 1:1 line, with R2 values ranging from 0.766 to 0.980, the ANN models demonstrate accurate estimating of fruit quality attributes. The study's findings contribute to food quality and supply chain management through the identification of optimal storage conditions and predicting the fruit quality during storage under different atmosphere conditions, thereby minimizing food waste and enhancing food safety.

Antipathogenic potentials of exopolysaccharides produced by lactic acid bacteria and their food and health applications

In the recent years, there has been increased interest in the exopolysaccharides (EPS) of microbial origin, especially the EPS produced by lactic acid bacteria (LAB). These EPS exhibit considerable structural and physico-chemical diversities with numerous potential applications. The biological and functional potentials of LAB EPS include antioxidant, anti-microbial, anti-cancer, wound healing, immunomodulatory and prebiotic properties. With increased interest in natural and eco-friendly biomolecules as tools in the management of food quality and safety, biomolecules such as EPS of microbial origin are being evaluated. Although there are many reviews on the biological and functional properties of EPS, in this review we particularly focused on the anti-bacterial, anti-fungal, anti-viral and anti-biofilm properties of the EPS of LAB origin. Modes and mechanisms of action by which EPS interacts with the pathogens (bacteria, bacterial biofilm, fungi, and virus) to inhibit or kill them are also discussed. The anti-microbial effects of EPS have the potential to play a vital role in food preservation as well as protection which can be exploited effectively. Interestingly EPS has beneficial effects on gut homeostasis through modulating gut microbiota, inhibiting pathogens, and preventing bacterial as well as viral infections. The role of LAB EPS in food safety and human health vis-à-vis their applications in food and pharmaceutical industries are discussed in this review. However, more studies are required for maximum utility of these bio-functional EPS potentials for food preservation, food safety and other health claims.

Total Quality Management and Corporate Sustainability of Food and Beverage Companies in Lagos State, Nigeria

Total Quality Management and Corporate Sustainability of Food and Beverage Companies in Lagos State, Nigeria

Global trends and research hotspots on HACCP and modern quality management systems in the food industry

HACCP (Hazard Analysis and Critical Control Points) and modern quality management systems have a significant impact on public health in the food industry. These systems ensure that food products are safe for consumption by identifying and managing potential hazards at every stage of the production process. To stimulate ongoing studies in both developing and underexplored areas of inquiry, this research synthesizes and organizes the contributions made in this field. It examines more than 40 years of studies from Scopus data base on HACCP and modern quality management systems in the food industry using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. This represents, to the authors’ knowledge, the first bibliometric analysis undergone in this direction. The graphical framework demonstrates the highest developments in research and the literature review investigates barriers and opportunities of implementing HACCP in food industry organizations. Findings indicate that until the beginning of the 1990s, there was not a large number of scientific production in the field of HACCP and modern quality management systems in the food industry. The USA were the most prolific affiliation terms of scientific production until 2012, when studies from Italy, the United Kingdom, China and Greece intensified. Currently, the most prolific country in terms of publications is Italy. In terms of global cooperation, the United Kingdom, The United States and The Netherlands represent most active nations on this topic Motor themes that reflect the main interest of the researchers include food diseases, quality control, hazards or food supply. The study also provides future research directions regarding food quality and safety management. These should be focused on improving the safety, quality, and sustainability of food products, while also adapting to changing consumer demands, emerging risks, and regulatory requirements.

Quantitative microbiological spoilage risk assessment (QMSRA) of fresh poultry fillets during storage at retail

A quantitative microbiological spoilage risk assessment model (QMSRA) of aerobically stored fresh poultry fillets was developed based on pseudomonads growth and metabolic activity. Simultaneous microbiological and sensory analyses were performed in poultry fillets to evaluate the relation between pseudomonads concentration and sensory rejection due to spoilage. The analysis showed no organoleptic rejection at pseudomonads concentrations less than 6.08 log CFU/cm2. For higher concentrations, a “spoilage-response” relationship was developed using a beta-Poisson model. The above relationship was combined with a stochastic modeling approach for pseudomonads growth by taking into account both variability and uncertainty of factors affecting spoilage. To enhance the reliability of the developed QMSRA model, uncertainty was quantified and separated from variability using a second order Monte Carlo simulation. For a batch of 10,000 units, the QMSRA model predicted a median number of 11, 80, 295, 733 and 1,389 spoiled units for retail storage times of 6,7, 8, 9 and 10 days, respectively, while no spoiled units were predicted for storage time of up to 5 days at retail. Scenario analysis showed that a reduction of 1 log in the pseudomonads concentration at the time of packaging or 1 °C in retail storage temperature results in up to 90% reduction of the spoiled units while the combination of the above interventions can reduce the risk of spoilage by up to 99%, depending on the storage time. The poultry industry can utilize the QMSRA model as a transparent scientific basis to support food quality management decisions in determining appropriate expiration dates which maximize the utilization of the product’s “true” shelf life while minimize the risk of spoilage to an acceptable level. Furthermore, the scenario analysis can provide the necessary components for an effective cost-benefit analysis, enabling the identification and comparison of appropriate strategies for extending the shelf life of fresh poultry products.

Miniature spectrometer data analytics for food fraud

Machine learning has been extensively used for analyzing spectral data in food quality management. However, collecting high-quality spectral data from miniature spectrometers outside the laboratory is challenging due to various factors such as distortions, noise, high dimensionality, and collinearity. This paper presents an in-depth analysis of food datasets collected from miniature spectrometers to evaluate the data quality and characteristics, by focusing on a case study of olive oil quality check, where various machine learning models were applied to differentiate pure and adulterated olive oil. Furthermore, the impact of pre-processing techniques on data distortions was studied. It presents a comprehensive pipeline, including data pre-processing, dimension reduction, classification, and regression analysis, and deploys different algorithms for comparative classification and regression analysis. The model performances were assessed using 2 separate methods: tenfold cross-validation on an entire dataset with 10% random testing, and an entire test set collected in different environments (multi-session validation). The first validation approach reached classification rates of up to 96.73%, while the second achieved 83.32%. These results demonstrate that cost-effective miniature spectrometers augmented with a suitable machine learning pipeline could execute classification tasks on par with non-portable and more expensive spectrometers. Furthermore, the study highlights the requirement of specialized algorithms to handle different ambient conditions affecting data acquisition and to eliminate performance gaps, making miniature spectrometers suitable for in situ scenarios. This work extends previous research to enable consumers becoming the first line in the defense against food fraud.

Dietary exposure and risk assessment of polybrominated diphenyl ethers in the Republic of Korea: A nationwide study

In this study, the dietary exposure to seven polybrominated diphenyl ethers (PBDEs) congener groups, including 22 types of PBDEs compounds, through consumption of 80 food items in Korean diets along with their risk assessment has been investigated. To process this, the concentrations of the target PBDEs in samples of food items were measured. The consumption amounts of the target food items were derived from the results of the 24 h food recall interview of subjects who participated in the Korean National Health and Nutrition Examination Survey (KNHANES) from 2015 to 2019. Thereafter, the estimated daily intake and risk of exposure for each PBDEs congener group were assessed. Results indicate that although exposure to the target PBDEs was not significant enough to cause a potential health risk, deca-BDE (BDE-209) was the dominant congener in terms of both exposure and risk for consumers in all age groups. Furthermore, while eating seafood was the dominant path for dietary exposure to PBDEs, exposure to octa-BDEs was mostly through livestock products. Accordingly, to protect all consumers, especially those aged below 2 years and over 65 years, more accurate food quality management is required for controlling the dietary intake of PBDEs.

Food supply chain risk of community group buying in the post-epidemic era

Due to the outbreak of COVID-19 at the beginning of 2020, community group buying is booming again, but at the same time, it will bring new risks. Combining with the characteristics of food quality and community group purchase management, from food procurement, supply, distribution, sales link of food supply chain risk is analyzed, by using analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method in the food supply chain risk factors, the result shows that in the community group to the importance of the food supply chain risk for commodity procurement risk &gt; External environmental risk &gt; Logistics distribution risk &gt; Information Technology Risk &gt;Mission risk, By analyzing these five risks, preventive measures are provided for food supply chain risk management in community group buying.

Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics

Traditional fruit freshness prediction and modeling heavily rely on various physicochemical indicators (such as water loss rate, pH, and VC content), which is facing predicaments of time-consuming, laborious, destructive, and low prediction accuracy. To this end, this paper proposes a new method for fruit freshness prediction based on multi-sensing technology and machine learning algorithm, thereby improving the automation, intelligentialize, and high accuracy of fruit freshness prediction. The critical control points of blueberry cold chain logistics were analyzed firstly based on the HACCP method, identifying the key gas parameters (O2, CO2, and C2H4) and interaction mechanisms of gas and blueberry freshness. Then the blueberry cold chain microenvironment monitoring platform (BCCMMP) was developed for critical gas content monitoring at different temperatures (0 °C, 5 °C, and 22 °C). It was demonstrated that gas information can replace quality information to characterize blueberry freshness, and further emerging machine learning (ML) algorithms (BP, RBF, SVM, and ELM) were constructed for blueberry freshness prediction using critical gas information, and the results showed prediction accuracies of 90.87% (BP), 92.24% (RBF), 94.01% (SVM), and 91.31% (ELM). By contrast, the 85.10% prediction accuracy was achieved by the traditional Arrhenius equation method based on temperature and quality parameters. Through the automatic non-destructive acquisition of sensing data and emerging machine learning algorithms, this paper provides a new approach to improving the freshness prediction accuracy and food quality management level during fruit cold chain logistics.