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... the years, newer filling equipment has been introduced (e.g., hydraulic stuffers, automated twisting, and clipping) that helped the industry reduce downtime. A more recent gamechanger has been the introduction of the co-extrusion technology, where the casing is produced on top of the product as it is coming out from the stuffing horn (Figure 7). This allows the producer to use a higher level of automation and move away from the traditional batch type operation to a continuous process. ...
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... [15] facilitated real-time monitoring of processing equipment like ovens, mixers, dryers, and conveyors through IoT technology. However, compared to other food processing areas, the application of IoT in the meat processing industry is still in the stage of innovation and change, and lacks an ideal, comprehensive solution [16]. ...
The complex multi-stage process of meat processing encompasses critical phases, including slaughtering, cooling, cutting, packaging, warehousing, and logistics. The quality and nutritional value of the final meat product are significantly influenced by each processing link. To address the major challenges in the meat processing industry, including device heterogeneity, model deficiencies, rapidly increasing demands for data analysis, and limitations of cloud computing, this study proposes an Internet of Things (IoT) architecture. This architecture is centered around an intelligently decoupled gateway design and edge-cloud collaborative intelligent meat inspection. Pork freshness detection is used as an example. In this paper, a high-precision and lightweight pork freshness detection model is developed by optimizing the MobileNetV3 model with Efficient Channel Attention (ECA). The experimental results indicate that the model’s accuracy on the test set is 99.8%, with a loss function value of 0.019. Building upon these results, this paper presents an experimental platform for real-time pork freshness detection, implemented by deploying the model on an intelligent gateway. The platform demonstrates stable performance with peak model memory usage under 600 MB, average CPU utilization below 20%, and gateway internal response times not exceeding 100 ms.
... Several Industry 4.0 technologies offer exciting opportunities to achieve intelligent cutting in fish and meat processing. For example, robots are being increasingly adopted and implemented with various cutting equipment in meat and seafood industry to handle high-speed automated cutting tasks [63,73]. ...
The fourth industrial revolution (Industry 4.0) is driving significant changes across multiple sectors, including the food industry. This review examines how Industry 4.0 technologies, such as smart sensors, artificial intelligence, robotics, and blockchain, among others, are transforming unit operations within the food sector. These operations, which include preparation, processing/ transformation, preservation/stabilization, and packaging and transportation, are crucial for converting raw materials into high-quality food products. By incorporating advanced digital, physical, and biological innovations, Industry 4.0 technologies are enhancing precision, productivity , and environmental responsibility in food production. The review highlights innovative applications and key findings that showcase how these technologies can streamline processes, minimize waste, and improve food product quality. The adoption of Industry 4.0 innovations is increasingly reshaping the way food is prepared, transformed, preserved, packaged, and transported to the final consumer. The work provides a valuable roadmap for various sectors within agriculture and food industries, promoting the adoption of Industry 4.0 solutions to enhance efficiency, quality, and sustainability throughout the entire food supply chain.
... The economic impact of the above-mentioned myopathies was estimated to be 200 million USD in North America (Owens, 2014), and later, when prevalence increased, at over 1 billion dollars (Barbut, 2020). ...
This review is a summary of a Poultry Science Association symposium addressing myopathies in broilers’ breast meat, focusing on the interactions between genetics, nutrition, husbandry, and meat processing. The Pectoralis major myopathies (woody breast [WB]; white striping [WS]; spaghetti meat [SM]) and Pectoralis minor (“feathering”) are described, followed by discussing their prevalence, potential causes, current and future ways to mitigate, as well as detection methods (in live birds and meat) as well as ways to utilize affected meat. Overall, breast myopathies remain an important focus across the poultry industry and whilst a lot of data and knowledge has been gathered, it is clear that there is still a lot to understand. As there are multiple factors impacting the occurrence of breast myopathies, their reduction relies on a holistic approach. Ongoing balanced breeding strategies by poultry breeders is targeting the longer-term genetic component but comprehending the significant influence from nongenetic factors (short-term solutions such as nutrition) remains a key area of opportunity. Consequently, understanding the physiology and biological needs of the muscle through the life of the bird is critical to reduce the myopathies (e.g., minimizing oxidative stress) and gain more insight into their etiology.
... It has the capability of carrying out 700 cuts per minute with a remarkable accuracy of ±0.5% that surpasses manual work, enhancing precision, speed, and hygiene levels. Meanwhile, meat processing systems, including the beef carcass cutting system, which combines sensors to produce precise carcass images with computer-controlled cutting, and the water jet knife automatic skin trimming system for fish fillets, where a computer algorithm ascertains the optimum trimming pattern to reduce waste, demonstrate the advanced level of automation and intelligence in the primary processing section of meat processing plants (Barbut, 2020). ...
The field of meat processing plays a critical role in the food industry and has seen increasing adoption of artificial intelligence (AI) technology with rapid technological advancements. AI technology has tremendous potential for enhancing production efficiency and product quality in meat processing. However, further research and exploration are necessary to tackle the challenges posed by the use of AI technology. This article details the implementation of AI technology in meat processing, focusing on carcass classification, automation and intelligent processing, and meat‐quality detection. We aim to provide inspiration to researchers and industry professionals and promote the advancement of AI technology in the meat processing sector.
Practical applications
Our review article showcases the potential industrial applications of artificial intelligence (AI) techniques in the meat processing industry. AI technology can greatly improve production efficiency and product quality in meat processing. By implementing AI algorithms, meat processors can accurately classify carcasses, automate various processing tasks, and detect meat quality with higher accuracy. These advancements can lead to increased profitability and improved food safety in the industry. We hope to provide valuable insights for researchers and industry professionals, encouraging them to further explore and adopt AI technology in the meat processing sector.
... Bacterial and metabolic growth during the shelf-life of poultry meat can lead to changes in meat color, odor, and taste, eventually leading to waste and loss of poultry products (Rouger et al. 2017). In the US poultry industry, about 9 billion chickens were processed yearly, of which 80% were sold as fresh in the market, while 2-4% were lost as a result of spoilage (Barbut 2020; Barbut & Leishman 2022;Russell 2009), resulting in significant economic losses in poultry meat industries (Soladoye et al. 2024). Given an ever-growing high demand for high-quality poultry products, there is a need for the recognition of microbiologically spoiled or contaminated meat to ensure the supply of fresh, high-quality poultry products to consumers (Archer 1996). ...
Meat quality has gained ample attention owing to increased consumer awareness and competition among poultry processors to deliver premium quality products. Nevertheless, chicken breast meat is susceptible to microbial spoilage resulting in economic and product losses. Conventional approaches such as organoleptic, aerobic plate count (APC), and molecular methods have been employed for assessing the microbiological quality of meat products but suffer various shortcomings. This study was a proof-of-concept evaluation of emerging structured illumination reflectance imaging (SIRI) as a non-destructive, objective means to evaluate microbial spoilage in chicken breast meat. The experimental chicken breast samples were kept on a retail tray for 1–13 days at 3-day intervals and subjected to image acquisition by broadband SIRI at varied spatial frequencies of sinusoidally-modulated structured illumination (0.05–0.40 cycles mm⁻¹). The chicken samples were categorized into fresh and spoiled classes using the APC threshold of 5 log10 CFU g⁻¹. Acquired pattern images were demodulated into amplitude component (AC) and direct component (DC) images (corresponding to uniform illumination). Three pre-trained deep learning models, including VGG16, EfficientNetB6, and ResNeXt101, were employed to extract the features from the demodulated images, followed by principal component analysis (PCA) to reduce feature redundancy. The selected PCs were used to build classification models using linear discriminant analysis (LDA) and support vector machine (SVM) separately to distinguish between fresh and spoiled samples. AC images consistently outperformed DC images in the resultant classification performance. When the LDA classifier was used, AC images yielded maximum accuracy improvements of 3.6%–6%, depending on feature type and spatial frequency; with the SVM classifier, AC images achieved maximum improvements of 4.4% to 6.4%. The SVM model with the features extracted by ResNeXt101 from AC images at 0.25 cycles mm⁻¹ achieved the best overall classification accuracy of 76% in differentiating fresh and spoiled samples. This study shows that the SIRI technique is effective for enhanced assessment of microbial spoilage in broiler breast meat, but more dedicated efforts are needed to improve both hardware and software for practical application.
... By making use of laser scanners enabling a 3D cutting image, with the help of weight control (to adjust volume and density) of a computer and machine vision techniques, the cutting process can be implemented with higher precision. For this specific process, the cutting knife managed to perform 700 cuts/min with an accuracy of ±0.5% [101]. ...
The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.
... Eating habits have changed and there has been a greater demand for prepared and processed products. This has led to changes in working methods and in the way industries operate [15]. Associated with the rapid growth of sectors and markets were outbreaks of animal diseases such as African swine fever, avian influenza, and bovine spongiform encephalopathy (BSE), or the presence of chemicals in feed [16,17]. ...
The globalization of food markets has led companies to buy products not only locally, but also from other corners of the world. This has introduced complexity into supply chains, as products have to move longer distances and pass through more companies before reaching the end consumer. The meat industry has been no different. Events such as animal disease outbreaks have diminished consumer confidence in the industry and the supply chain. Coupled with this, consumers started demanding "more transparent" meat products. This has led companies to think about new traceability systems, which continue to enforce food safety and health rules, but at the same time enhance and make transparent to the consumer the origin and constitution of their products. This article proposes a traceability system in the agri-food (meat industry) with a multi-chain architecture, among them, blockchain. The use of blockchain in the traceability system helped to mitigate the omission of relevant data for the traceability process, allowing us to guarantee the immutability, reliability, and transparency of the data along the value chain. At the same time, the system was able to reduce the time of the traceability process by giving the user the possibility to access the traced information via a unique product identifier.
... W ocenie badaczy i ekspertów Przemysł 4.0 i jego technologie promują większą automatyzację i cyfryzację, co prowadzi do koncepcji inteligentnej fabryki, charakteryzującej się lepszą wydajnością, wyższą jakością żywności, mniejszymi stratami żywności i mniejszymi kosztami i skróceniem czasu [3,11]. Niedobory pracowników i inne zakłócenia ...
Przemysł mięsny stoi obecnie przed wielkimi wyzwaniami związanymi z potrzebami zrównoważonej produkcji oraz wytwarzaniem wysokiej jakości, bezpiecznych artykułów spożywczych o potwierdzonej autentyczności, aby zaspokoić popyt konsumentów i zwiększyć ich zaufanie do sektora mięsnego. Na tym tle, przeprowadzony przegląd literatury i dostępnych rozwiązań innowacji wykazał, że pojawienie się i wdrożenie technologii 4.0 (takich jak automatyzacja i robotyzacja, Internet rzeczy (IoT), Big Data Analyses (BDA), rzeczywistość rozszerzona (AR), blockchain, technologie obrazowania i inteligentne czujniki) w przemyśle mięsnym mogą być skutecznymi narzędziami, wspierającymi niezawodność produkcji oraz jakość, bezpieczeństwo i autentyczność mięsa i produktów mięsnych, ponieważ zapewniają istotne innowacyjne rozwiązania, których dodatkowym efektem mogą być aspekty globalnej polityki jak: ochrona klimatu, wpływ na środowisko i model gospodarki zrównoważonej
The meat industry is currently facing great challenges related to the needs of sustainable production and high-quality production
quality, safe and authentic food products to meet consumer demand and increase their confidence in the meat sector. Against this background, a review of the literature and available innovation solutions showed that the emergence and implementation of 4.0 technologies (such as automation and robotization, Internet of Things (IoT), Big Data Analyses (BDA), augmented reality (AR), blockchain, imaging technologies and intelligent sensors) in the meat industry can be effective tools supporting production reliability and the quality, safety and authenticity of meat and meat products, because they provide important innovative solutions, the additional effect of which can be aspects of global policy such as: climate protection, impact on environment and sustainable economy model
... Many studies reported the benefits of optical sensing and computer vision technologies, such as hyperspectral imaging (HSI) and spectroscopic techniques and, accurate and non-destructive optical sensing technologies that have been used for the assessment of egg freshness (Giunchi et al., 2008;Karoui et al., 2006a;Liu et al., 2020), gender (Corion et al., 2022;Khaliduzzaman et al., 2019;Rahman et al., 2021a), fertility detection (Adegbenjo et al., 2020; Liu & Ngadi, 2013;Smith et al., 2008), size (Narushin et al., 2004;Suktanarak & Teerachaichayut, 2017), gas composition (Zhang et al., 2022a), proximate composition (Zhao et al., 2018), and fabrication (Chen et al., 2019;Joshi et al., 2022). Although several studies have been published on the use of I4.0 technologies in agriculture (Araújo et al., 2021;Bernhardt et al., 2021;Liu et al., 2021;Patil & Shekhawati, 2019), the food and beverage industry (Akyazi et al., 2020;Demir & Dincer, 2020;Konur et al., 2021;Luque et al., 2017), the meat industry (Barbut, 2020;Echegaray et al., 2022;Kamruzzaman, 2023), supply chains (Ghadge et al., 2020;Mukherjee et al., 2021), resources nexus (David et al., 2022), and traceability (Hassoun et al., 2022c(Hassoun et al., , 2023c, no review has been published on I4.0 applications for the egg industry. Therefore, this review article has focused on those I4.0 technologies for which contemporary research exists related to the egg industry. ...
The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.
... Big Data -Improve yield -Minimize waste and food loss -Improved customer service (Ahearn et al., 2016;Engelseth et al., 2019;Rejeb et al., 2021;Margaritis et al., 2022;Rejeb et al., 2022 (Barbut, 2020;Duong et al., 2020;Hassoun et al., 2022) -Reduce manual labor ...
... Autonomous robots are a necessary tool in industry 4.0 dealing with difficult, costly, and time-consuming labour operations (Hassoun et al., 2022). In the meat supply chain, automation increase efficiency, minimize manual operations, handle labor shortage and deal with skilled employees' availability (Barbut, 2020). According to Duong et al. (2020), waste and service time are reduced because of the application of autonomous robots in prediction and production management. ...
Though the pandemic has created an imbalance and disrupted the economy in the food industry, it has had a positive impact on speeding the acceptance of the industry towards digital innovations (DI). The shift toward digitalization is leading the food industry to leverage innovations that can serve the dual purpose of safer and sustainable food operations. This review synthesizes the rapidly growing literature on digital technology used as the response to the emergence of food safety and sustainability issues during the COVID-19 pandemic. Opportunities to improve thirteen food safety management system components and three sustainability components including economics, environmental and social were identified. The review determined that blockchain and IoT have the most prominent role in improving food safety, especially the component of traceability and monitoring and inspection.