1
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Chen J, Zhang J, Wang N, Xiao B, Sun X, Li J, Zhong K, Yang L, Pang X, Huang F, Chen A. Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring. Food Chem 2024; 445:138755. [PMID: 38387318 DOI: 10.1016/j.foodchem.2024.138755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.
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Affiliation(s)
- Jiaci Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Juan Zhang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Nan Wang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Bin Xiao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Xiaoyun Sun
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Jiapeng Li
- China Meat Research Center, Beijing, China.
| | - Ke Zhong
- Shandong Academy of Grape, Jinan, China.
| | - Longrui Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Xiangyi Pang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Fengchun Huang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Ailiang Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
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2
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Zhang L, Yang Q, Zhu Z. The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants. Foods 2024; 13:1936. [PMID: 38928877 PMCID: PMC11203047 DOI: 10.3390/foods13121936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/12/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people's pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice.
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Affiliation(s)
- Longlong Zhang
- Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou 515063, China
- College of Electronic Engineering, Southwest University, Chongqing 400715, China
| | - Qiuping Yang
- College of Electronic Engineering, Southwest University, Chongqing 400715, China
- Hubei Key Laboratory of Food Nutrition and Safety, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhiyuan Zhu
- College of Electronic Engineering, Southwest University, Chongqing 400715, China
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3
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Nakrosis A, Paulauskaite-Taraseviciene A, Raudonis V, Narusis I, Gruzauskas V, Gruzauskas R, Lagzdinyte-Budnike I. Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification. Animals (Basel) 2023; 13:3041. [PMID: 37835647 PMCID: PMC10571708 DOI: 10.3390/ani13193041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/17/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.
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Affiliation(s)
- Arnas Nakrosis
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania; (A.N.); (I.N.); (I.L.-B.)
| | - Agne Paulauskaite-Taraseviciene
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania; (A.N.); (I.N.); (I.L.-B.)
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania; (V.R.); (V.G.); (R.G.)
| | - Vidas Raudonis
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania; (V.R.); (V.G.); (R.G.)
- Faculty of Electrical and Electronics, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
| | - Ignas Narusis
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania; (A.N.); (I.N.); (I.L.-B.)
| | - Valentas Gruzauskas
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania; (V.R.); (V.G.); (R.G.)
- Institute of Computer Science, Vilnius University, 08303 Vilnius, Lithuania
| | - Romas Gruzauskas
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania; (V.R.); (V.G.); (R.G.)
| | - Ingrida Lagzdinyte-Budnike
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania; (A.N.); (I.N.); (I.L.-B.)
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4
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Pinto-Pinho P, Ferreira AF, Pinto-Leite R, Fardilha M, Colaço B. The History and Prospects of Rabbit Sperm Sexing. Vet Sci 2023; 10:509. [PMID: 37624296 PMCID: PMC10459625 DOI: 10.3390/vetsci10080509] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/26/2023] Open
Abstract
Sperm sex selection is a longstanding challenge in the field of animal reproduction. The cuniculture industry, in particular producers of males or females for breeding purposes, would greatly benefit from the pre-selection of the offspring's sex. This review article overviews the current and future developments in rabbit sperm sexing technologies, as well as the implications of implementing these methodologies in cuniculture. The first attempts of sperm sexing were performed in rabbits; however, a both efficient and cost-effective methodology was not yet developed for this species. Those included sperm sexing according to differences in sperm density, surface electric charge, pH susceptibility, antisera reaction, and flow cytometry. Separation by flow cytometry has proven to be efficient in rabbits, yielding fractions with approximately 81% and 86% purity for X- and Y-sperm, respectively. However, it is not cost-effective for cuniculture and decreases sperm quality. The advantages, limitations, and practical considerations of each method are presented, highlighting their applicability and efficiency. Furthermore, herein we explore the potential of immunological-based techniques that overcome some of the limitations of earlier methods, as well as recent advancements in sperm sexing technologies in other animal models, which could be applied to rabbits. Finally, the challenges associated with the development and widespread implementation of rabbit sperm sexing technologies are addressed. By understanding the advantages and limitations of existing and emerging methods, researchers can direct their efforts towards the most promising directions, ultimately contributing to a more efficient, profitable, and sustainable cuniculture.
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Affiliation(s)
- Patrícia Pinto-Pinho
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal;
- Laboratory of Signal Transduction, Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
- Laboratory of Genetics and Andrology, Hospital Center of Trás-os-Montes and Alto Douro, E.P.E, 5000-508 Vila Real, Portugal;
- Experimental Pathology and Terapeutics Group, IPO Porto Research Center, Portuguese Institute of Oncology of Porto Francisco Gentil, E.P.E., 4200-072 Porto, Portugal
| | - Ana F. Ferreira
- Animal and Veterinary Research Centre, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal;
| | - Rosário Pinto-Leite
- Laboratory of Genetics and Andrology, Hospital Center of Trás-os-Montes and Alto Douro, E.P.E, 5000-508 Vila Real, Portugal;
- Experimental Pathology and Terapeutics Group, IPO Porto Research Center, Portuguese Institute of Oncology of Porto Francisco Gentil, E.P.E., 4200-072 Porto, Portugal
| | - Margarida Fardilha
- Laboratory of Signal Transduction, Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Bruno Colaço
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal;
- Animal and Veterinary Research Centre, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal;
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5
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Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Domínguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:672. [PMID: 36679464 PMCID: PMC9860605 DOI: 10.3390/s23020672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.
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Affiliation(s)
- Paulo E. S. Munekata
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sarah Finardi
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Carolina Krebs de Souza
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Caroline Meinert
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Mirian Pateiro
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Tuany Gabriela Hoffmann
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
| | - Rubén Domínguez
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sávio Leandro Bertoli
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR–Central Institute for Research on Cotton Technology, Mumbai 400019, India
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
- Facultade de Ciencias, Universidade de Vigo, Área de Tecnoloxía dos Alimentos, 32004 Ourense, Spain
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6
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Hyperspectral Imaging Coupled with Multivariate Analyses for Efficient Prediction of Chemical, Biological and Physical Properties of Seafood Products. FOOD ENGINEERING REVIEWS 2023. [DOI: 10.1007/s12393-022-09327-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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7
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Kamruzzaman M. Optical sensing as analytical tools for meat tenderness measurements - A review. Meat Sci 2023; 195:109007. [DOI: 10.1016/j.meatsci.2022.109007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 09/11/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
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8
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Jo K, Lee S, Jeong HG, Lee DH, Yoon S, Chung Y, Jung S. Utilization of Electrical Conductivity to Improve Prediction Accuracy of Cooking Loss of Pork Loin. Food Sci Anim Resour 2023; 43:113-123. [PMID: 36789198 PMCID: PMC9890360 DOI: 10.5851/kosfa.2022.e64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/29/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022] Open
Abstract
This study investigated the predictability of cooking loss of pork loin through relatively easy and quick measurable quality properties. The pH, color, moisture, protein content, and cooking loss of 100 pork loins were measured. The explanatory variables included in all linear regression models with an adjust-r2 value of ≥0.5 were pH and the protein content. In the linear regression model predicting cooking loss, the highest adjust-r2 value was 0.7, with pH, CIE L*, CIE b*, moisture, and protein content as the explanatory variables. In 30 pork loins, electrical conductivity was additionally measured, and as a result of linear regression analysis for predicting cooking loss, the highest adjust-r2 value was 0.646 with electrical conductivity measured at 40 Hz, with pH and color as the explanatory variables. Ordinal logistic regression analysis was performed to predict the three grades (low, middle, and high) of loin cooking loss using pH, color, and 40 Hz electrical conductivity as the explanatory variables, and the percent concordance was 93.8%. In conclusion, the addition of electrical conductivity as an explanatory variable did not increase the prediction accuracy of the linear regression model for predicting cooking loss; however, it was demonstrated that it is possible to predict and classify the cooking loss grade of pork loin through quality properties that can be measured quickly and easily.
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Affiliation(s)
- Kyung Jo
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea
| | - Seonmin Lee
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea
| | - Hyun Gyung Jeong
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea
| | - Dae-Hyun Lee
- Department of Biosystems Machinery
Engineering, Chungnam National University, Daejeon 34134,
Korea
| | - Sangwon Yoon
- Fish Genetics and Breeding Research
Center, National Institute of Fisheries Science, Geoje 53334,
Korea,Department of Bio-AI Convergence, Chungnam
National University, Daejeon 34134, Korea
| | - Yoonji Chung
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea
| | - Samooel Jung
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea,Corresponding author: Samooel
Jung, Division of Animal and Dairy Science, Chungnam National University,
Daejeon 34134, Korea, Tel: +82-42-821-5774, Fax: +82-42-825-9754,
E-mail:
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9
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Hennessey E, DiFazio M, Hennessey R, Cassel N. Artificial intelligence in veterinary diagnostic imaging: A literature review. Vet Radiol Ultrasound 2022; 63 Suppl 1:851-870. [PMID: 36468206 DOI: 10.1111/vru.13163] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/05/2022] [Accepted: 07/07/2022] [Indexed: 12/09/2022] Open
Abstract
Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.
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Affiliation(s)
- Erin Hennessey
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA.,Army Medical Department, Student Detachment, San Antonio, Texas, USA
| | - Matthew DiFazio
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA
| | - Ryan Hennessey
- Department of Computer Science, College of Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Nicky Cassel
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA
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10
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Perception of Stakeholders for Meat Qualities among Value Chain Actors in Ethiopia. Vet Med Int 2022; 2022:1247459. [DOI: 10.1155/2022/1247459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/17/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022] Open
Abstract
Perception is the knowledge of an individual/group gained from experience and impressions of the ideal situation. Ethiopian livestock marketing has a different stage and large actors. However, in the flow chain, there is less communication. Because they only perceive for their benefit rather than care for a quality product. This happened because the production mapping was not understood among them. The majority of producers fetch too old animals after being culled from production and those that might be abnormal due to disease and chronic stress. Traders transport mixed animals through nondedicated vehicles and long trekking without feeding and watering. Abattoir men worked with poor facilities. They perceived that the time stay animals in Lairage, breeding, bleeding, and carcass handling is the major problem in meat quality in Ethiopia. The slaughtering has been conducted in brutal ways of stunning using either a hammer or knife at the atlanto-occipital space of the animal on the floor side by side. The majority of butchers in Ethiopia are located on the main road for their products to be easily displayed to clients, and they hang meat on the open shelf without packing, which exposes the product to aerobic spoilage by bacteria and yeasts. Traders/brokers are promoting the product based on the commission they earn rather than the quality and health of the animals. However, as a principle, each actor has a responsibility to manage risk as they benefit socially and economically from firms. Government entities should play an important role in shaping actors’ perceptions and understanding of biosecurity measures. Mainly, the interventions should focus on improving business models and technological adoption. This model is used for improving vertical relationships among operational actors, horizontal relationships with logistics providers, and market promotion measures to attract foreign direct investors and importers, transforming traditional practices of animal husbandry into commercial ones. Because a key activity for each value chain actor is availing of the final product safely at the right place and time. The review was designed to convey information to enhance the linkage between meat value chain actors and optimize management skills in Ethiopia.
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11
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Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:foods11223713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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12
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Pu H, Wei Q, Sun DW. Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Crit Rev Food Sci Nutr 2022; 63:1297-1313. [PMID: 36123794 DOI: 10.1080/10408398.2022.2121805] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China.,Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Ireland
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13
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Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146986] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.
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14
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Akhtar J, Abrha MG, Teklehaimanot K, Gebrekirstos G. Cold plasma technology: fundamentals and effect on quality of meat and its products. FOOD AGR IMMUNOL 2022. [DOI: 10.1080/09540105.2022.2095987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Javeed Akhtar
- Department of Chemical Engineering, College of Engineering and Technology, Adigrat University, Adigrat, Ethiopia
| | - Mebrhit Gebremariam Abrha
- Department of Chemical Engineering, College of Engineering and Technology, Adigrat University, Adigrat, Ethiopia
| | - Kiros Teklehaimanot
- Department of Chemical Engineering, College of Engineering and Technology, Adigrat University, Adigrat, Ethiopia
| | - Gebremeskel Gebrekirstos
- Department of Chemical Engineering, College of Engineering and Technology, Adigrat University, Adigrat, Ethiopia
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15
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Huang H, Zhan W, Du Z, Hong S, Dong T, She J, Min C. Pork primal cuts recognition method via computer vision. Meat Sci 2022; 192:108898. [DOI: 10.1016/j.meatsci.2022.108898] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/18/2022] [Accepted: 06/20/2022] [Indexed: 12/20/2022]
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16
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Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses. Sci Rep 2022; 12:2392. [PMID: 35165330 PMCID: PMC8844077 DOI: 10.1038/s41598-022-06379-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/25/2022] [Indexed: 12/26/2022] Open
Abstract
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.
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