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Zhang S, Chen Y, Liu W, Liu B, Zhou X. Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones. SENSORS (BASEL, SWITZERLAND) 2023; 23:5135. [PMID: 37299862 PMCID: PMC10255884 DOI: 10.3390/s23115135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (R2 = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry.
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Affiliation(s)
- Shufeng Zhang
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
| | - Yuxi Chen
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
- Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China
| | - Weizhen Liu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
- Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China
| | - Bang Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xiang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518038, China
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2
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de Sousa Reis VC, Ferreira IM, Durval MC, Antunes RC, Backes AR. Measuring water holding capacity in pork meat images using deep learning. Meat Sci 2023; 200:109159. [PMID: 36934522 DOI: 10.1016/j.meatsci.2023.109159] [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: 05/16/2022] [Revised: 12/01/2022] [Accepted: 03/02/2023] [Indexed: 03/17/2023]
Abstract
Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle.
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Affiliation(s)
| | | | - Mariah Castro Durval
- School of Veterinary Medicine, Federal University of Uberlândia, Uberlândia, MG, Brazil
| | - Robson Carlos Antunes
- School of Veterinary Medicine, Federal University of Uberlândia, Uberlândia, MG, Brazil.
| | - Andre Ricardo Backes
- Department of Computing, Federal University of São Carlos (UFSCar), São Carlos, SP, Brazil.
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3
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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] [Grants] [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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4
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Image based beef and lamb slice authentication using convolutional neural networks. Meat Sci 2023; 195:108997. [DOI: 10.1016/j.meatsci.2022.108997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/11/2022] [Accepted: 09/30/2022] [Indexed: 11/09/2022]
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5
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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; 11:3713. [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] [Grants] [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
| | - 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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6
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Mishra M, Sarkar T, Choudhury T, Bansal N, Smaoui S, Rebezov M, Shariati MA, Lorenzo JM. Allergen30: Detecting Food Items with Possible Allergens Using Deep Learning-Based Computer Vision. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02353-9] [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]
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7
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Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Tan WK, Husin Z, Yasruddin ML, Ismail MAH. Recent technology for food and beverage quality assessment: a review. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2022; 60:1681-1694. [PMID: 35463865 PMCID: PMC9014778 DOI: 10.1007/s13197-022-05439-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 03/13/2022] [Accepted: 03/16/2022] [Indexed: 12/02/2022]
Abstract
Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.
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Affiliation(s)
- Wei Keong Tan
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
| | - Zulkifli Husin
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
| | - Muhammad Luqman Yasruddin
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
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9
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Xie L, Qin J, Rao L, Tang X, Cui D, Chen L, Xu W, Xiao S, Zhang Z, Huang L. Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs. Anim Genet 2021; 52:633-644. [PMID: 34291482 DOI: 10.1111/age.13121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 11/30/2022]
Abstract
Intramuscular fat (IMF) content is a critical indicator of pork quality that affects directly the purchasing desire of consumers. However, to measure IMF content is both laborious and costly, preventing our understanding of its genetic determinants and improvement. In the present study, we constructed an accurate and fast image acquisition and analysis system, to extract and calculate the digital IMF content, the proportion of fat areas in the image (PFAI) of the longissimus muscle of 1709 animals from multiple pig populations. PFAI was highly significantly correlated with marbling scores (MS; 0.95, r2 = 0.90), and also with IMF contents chemically defined for 80 samples (0.79, r2 = 0.63; more accurate than direct analysis between IMF contents and MS). The processing time for one image is only 2.31 s. Genome-wide association analysis on PFAI for all 1709 animals identified 14 suggestive significant SNPs and 1 genome-wide significant SNP. On MS, we identified nine suggestive significant SNPs, and seven of them were also identified in PFAI. Furthermore, the significance (-log P) values of the seven common SNPs are higher in PFAI than in MS. Novel candidate genes of biological importance for IMF content were also discovered. Our imaging systems developed for prediction of digital IMF content is closer to IMF measured by Soxhlet extraction and slightly more accurate than MS. It can achieve fast and high-throughput IMF phenotype, which can be used in improvement of pork quality.
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Affiliation(s)
- L Xie
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - J Qin
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Rao
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - X Tang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - D Cui
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Chen
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - W Xu
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - S Xiao
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - Z Zhang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Huang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
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10
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Shi Y, Wang X, Borhan MS, Young J, Newman D, Berg E, Sun X. A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies. Food Sci Anim Resour 2021; 41:563-588. [PMID: 34291208 PMCID: PMC8277176 DOI: 10.5851/kosfa.2021.e25] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/09/2022] Open
Abstract
Increasing meat demand in terms of both quality and quantity in conjunction with
feeding a growing population has resulted in regulatory agencies imposing
stringent guidelines on meat quality and safety. Objective and accurate rapid
non-destructive detection methods and evaluation techniques based on artificial
intelligence have become the research hotspot in recent years and have been
widely applied in the meat industry. Therefore, this review surveyed the key
technologies of non-destructive detection for meat quality, mainly including
ultrasonic technology, machine (computer) vision technology, near-infrared
spectroscopy technology, hyperspectral technology, Raman spectra technology, and
electronic nose/tongue. The technical characteristics and evaluation methods
were compared and analyzed; the practical applications of non-destructive
detection technologies in meat quality assessment were explored; and the current
challenges and future research directions were discussed. The literature
presented in this review clearly demonstrate that previous research on
non-destructive technologies are of great significance to ensure
consumers’ urgent demand for high-quality meat by promoting automatic,
real-time inspection and quality control in meat production. In the near future,
with ever-growing application requirements and research developments, it is a
trend to integrate such systems to provide effective solutions for various grain
quality evaluation applications.
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Affiliation(s)
- Yinyan Shi
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.,College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Md Saidul Borhan
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Jennifer Young
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - David Newman
- Department of Animal Science, Arkansas State University, Jonesboro, AR 72467, USA
| | - Eric Berg
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Xin Sun
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
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11
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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12
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Uttaro B, Zawadski S, Larsen I, Juárez M. An image analysis approach to identification and measurement of marbling in the intact pork loin. Meat Sci 2021; 179:108549. [PMID: 33984801 DOI: 10.1016/j.meatsci.2021.108549] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/03/2021] [Accepted: 05/05/2021] [Indexed: 11/17/2022]
Abstract
An image analysis method for identifying marbling on exposed boneless pork loin surfaces was developed, solving the problem of influences from uneven lean colour. Comparing results from RAW and JPEG images of the mid-loin pork chop showed that, except at very high marbling levels, estimation of %IMF (intramuscular fat) was similar with both image types (R2 = 0.72 and 0.74 respectively). Marbling calculated from JPEGs was positively related to %IMF, but also diverged as %IMF increased. Estimations of %IMF at the mid-loin chop from JPEGs of external loin sites showed marbling on the posterior face to be the most strongly related (r = 0.72, P < 0.0001), followed by the ventral surface (r = 0.62-0.64, P < 0.0001), and the anterior face (r = 0.56, P < 0.0001). For on-line application, computing speed is important. This study suggested that an image capture system could be reduced to the capture of a cyan-filtered monochrome JPEG image for analysis.
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Affiliation(s)
- Bethany Uttaro
- Agriculture and Agri-Food Canada, Lacombe Research Centre, Lacombe, Alberta T4L 1W1, Canada.
| | - Sophie Zawadski
- Agriculture and Agri-Food Canada, Lacombe Research Centre, Lacombe, Alberta T4L 1W1, Canada
| | - Ivy Larsen
- Agriculture and Agri-Food Canada, Lacombe Research Centre, Lacombe, Alberta T4L 1W1, Canada
| | - Manuel Juárez
- Agriculture and Agri-Food Canada, Lacombe Research Centre, Lacombe, Alberta T4L 1W1, Canada
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13
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Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed. Foods 2021; 10:foods10010143. [PMID: 33445660 PMCID: PMC7828069 DOI: 10.3390/foods10010143] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 11/16/2022] Open
Abstract
This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups-0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model's polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.
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14
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Liu JH, Newman DJ, Young JM, Sun X. Prediction of Whole Pork Loin and Individual Chops’ Intramuscular Fat Using Computer Vision System Technology. MEAT AND MUSCLE BIOLOGY 2020. [DOI: 10.22175/mmb.11127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The objective of this study was to compare different methods of evaluating intramuscular fat (IMF) in pork and test the accuracy of using a computer vision system (CVS) on different locations of the loin. Whole pork loins (n = 1,400) were obtained from 6 pork processing plants. Subjective marbling scores and CVS IMF percentage (CVS IMF%) were assessed on the ventral lean surface of the whole loin and the 3rd (A) and 10th (B) rib chops. Additionally, the A and B chops were evaluated for crude fat percentage (CF%) using ether extract. The CF% of the whole loin was represented by using the average CF% of A and B chops. A combination of the bootstrap method and stepwise regression models was used to increase prediction and robustness for CF% prediction. To better understand whether plants played an effect, models for individual plants and using all plants together were built, tested, and compared. Results were that subjective marbling score had stronger correlations with CF% compared to CVS IMF% for the whole loin (0.70 vs. 0.58), A chop (0.79 vs.0.62), and B chop (0.74 vs. 0.61). When using the stepwise regression models to predict CF%, B chop (71.8%) had the highest prediction accuracy (estimates within 0.5% residual compared to CF% were considered accurate) followed by A chop (58.1%) and whole loin (48.2%). When comparing individual plant models and overall models, the overall accuracy improved; however, this improvement in accuracy was not consistent through every single plant. In conclusion, CVS has shown potential to estimate pork IMF on all locations, especially the posterior pork chop.
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Affiliation(s)
- Jeng-Hung Liu
- North Dakota State University Department of Animal Sciences
| | | | | | - Xin Sun
- North Dakota State University Department of Agricultural and Biosystems Engineering
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15
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Shi Y, Wang X, Sun X(R. WITHDRAWN: Development and application of an online grading system for pork loin quality based on computer vision technology. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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16
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Muñoz I, Gou P, Fulladosa E. Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks. Food Control 2019. [DOI: 10.1016/j.foodcont.2019.06.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Taheri-Garavand A, Fatahi S, Omid M, Makino Y. Meat quality evaluation based on computer vision technique: A review. Meat Sci 2019; 156:183-195. [PMID: 31202093 DOI: 10.1016/j.meatsci.2019.06.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/30/2019] [Accepted: 06/04/2019] [Indexed: 01/11/2023]
Abstract
Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.
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Affiliation(s)
- Amin Taheri-Garavand
- Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran.
| | - Soodabeh Fatahi
- Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran
| | - Mahmoud Omid
- Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Yoshio Makino
- Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-Ku, Tokyo 113-8657, Japan
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Sun Y, Wang S, Liu H, Ren R, Dong Q, Xie J, Cao J. Profiling and characterization of miRNAs associated with intramuscular fat content in Yorkshire pigs. Anim Biotechnol 2019; 31:256-263. [PMID: 31018763 DOI: 10.1080/10495398.2019.1573191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
miRNAs are short noncoding RNAs that post-transcriptionally regulate gene expression by binding to complementary regions of the target mRNA. The miRNAs associated with the deposition of intramuscular fat (IMF) content in pigs, which is an important meat quality trait, still remain to be investigated. In this study, the longissimus dorsi muscles (LDMs) from 234 individuals were collected from Yorkshire pigs at 90 kg body weight and the miRNA deep sequencing was conducted by using two tailed groups which were taken five individuals each from high (2.94 ± 0.04%) and low (1.62 ± 0.02%) IMF samples. The results showed that total 268 mature miRNAs were identified, of which 70 were previously known, 162 were conserved among species and 36 were identified specifically in pigs. Moreover, 28 miRNAs involved in adipogenesis were differentially expressed in the two groups, and five out of 16 miRNAs were validated by quantitative PCR (qPCR) using stem loop primers. Our results may serve as a fundamental basis for understanding the roles of miRNA in IMF development in pigs. The miRNAs identified in our study can be utilized for research IMF trait in pig population and will provide further clues to the study of meat quality regulatory mechanisms.
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Affiliation(s)
- Yan Sun
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Sheng Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Huiying Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Ruimin Ren
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Qian Dong
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Junhui Xie
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
| | - Jianhua Cao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, PR China.,College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, PR China
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Udomkun P, Innawong B, Jeepetch K. Computer vision system (CVS) for color and surface oil measurements of durian chips during post-frying. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00128-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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