1
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Wan Z, Tian F, Zhang C. Sheep Face Recognition Model Based on Deep Learning and Bilinear Feature Fusion. Animals (Basel) 2023; 13:1957. [PMID: 37370467 DOI: 10.3390/ani13121957] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/04/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
A key prerequisite for the establishment of digitalized sheep farms and precision animal husbandry is the accurate identification of each sheep's identity. Due to the uncertainty in recognizing sheep faces, the differences in sheep posture and shooting angle in the recognition process have an impact on the recognition accuracy. In this study, we propose a deep learning model based on the RepVGG algorithm and bilinear feature extraction and fusion for the recognition of sheep faces. The model training and testing datasets consist of photos of sheep faces at different distances and angles. We first design a feature extraction channel with an attention mechanism and RepVGG blocks. The RepVGG block reparameterization mechanism is used to achieve lossless compression of the model, thus improving its recognition efficiency. Second, two feature extraction channels are used to form a bilinear feature extraction network, which extracts important features for different poses and angles of the sheep face. Finally, features at the same scale from different images are fused to enhance the feature information, improving the recognition ability and robustness of the network. The test results demonstrate that the proposed model can effectively reduce the effect of sheep face pose on the recognition accuracy, with recognition rates reaching 95.95%, 97.64%, and 99.43% for the sheep side-, front-, and full-face datasets, respectively, outperforming several state-of-the-art sheep face recognition models.
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Affiliation(s)
- Zhuang Wan
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Fang Tian
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, China
| | - Cheng Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, China
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2
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Liu K, Xing R, Sun R, Ge Y, Chen Y. An Accurate and Rapid Way for Identifying Food Geographical Origin and Authenticity: Editable DNA-Traceable Barcode. Foods 2022; 12:17. [PMID: 36613233 PMCID: PMC9818171 DOI: 10.3390/foods12010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022] Open
Abstract
DNA offers significant advantages in information density, durability, and replication efficiency compared with information labeling solutions using electronic, magnetic, or optical devices. Synthetic DNA containing specific information via gene editing techniques is a promising identifying approach. We developed a new traceability approach to convert traditional digitized information into DNA sequence information. We used encapsulation to make it stable for storage and to enable reading and detection by DNA sequencing and PCR-capillary electrophoresis (PCR-CE). The synthesized fragment consisted of a short fragment of the mitochondrial cytochrome oxidase subunit I (COI) gene from the Holothuria fuscogilva (ID: LC593268.1), inserted geographical origin information (18 bp), and authenticity information from Citrus sinensis (20 bp). The obtained DNA-traceable barcodes were cloned into vector PMD19-T. Sanger sequencing of the DNA-traceable barcode vector was 100% accurate and provided a complete readout of the traceability information. Using selected recognition primers CAI-B, DNA-traceable barcodes were identified rapidly by PCR amplification. We encapsulated the DNA-traceable barcodes into amorphous silica spheres and improved the encapsulation procedure to ensure the durability of the DNA-traceable barcodes. To demonstrate the applicability of DNA-traceable barcodes as product labels, we selected Citrus sinensis as an example. We found that the recovered and purified DNA-traceable barcode can be analyzed by standard techniques (PCR-CE for DNA-traceable barcode identification and DNA sequencing for readout). This study provides an accurate and rapid approach to identifying and certifying products' authenticity and traceability.
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Affiliation(s)
- Kehan Liu
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Ranran Xing
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Ruixue Sun
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Yiqiang Ge
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China
- China Rural Technology Development Center, Beijing 100045, China
| | - Ying Chen
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
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3
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Liu Z, Geng N, Yu Z. Does a Traceability System Help to Regulate Pig Farm Households' Veterinary Drug Use Behavior? Evidence from Pig Farms in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11879. [PMID: 36231180 PMCID: PMC9564818 DOI: 10.3390/ijerph191911879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/15/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
In China, there is a renewed interest in traceability systems as an efficient tool to guarantee pork safety. One of the pathways through which a traceability system can benefit consumers is by easing information asymmetry. However, past literature on the traceability system in China pays more attention to theoretical analysis and less to empirical analysis. Using a large-scale survey of pig farms in China, we investigate the effects influencing farmers' participation in the traceability system. Findings show that a traceability system can influence the safety of pork indirectly through its impacts on farmers' production behaviors. Another important finding is that unsafe pork is a result of non-standard use of veterinary drugs, and the traceability system works well for farmers by pushing them to take stricter safety measurements.
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Affiliation(s)
- Zengjin Liu
- Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Ning Geng
- School of Public Administration, Shandong Normal University, Jinan 250014, China
| | - Zhuo Yu
- School of Management, Ocean University of China, Qingdao 266100, China
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4
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Radogna AV, Latino ME, Menegoli M, Prontera CT, Morgante G, Mongelli D, Giampetruzzi L, Corallo A, Bondavalli A, Francioso L. A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability. SENSORS (BASEL, SWITZERLAND) 2022; 22:6509. [PMID: 36080972 PMCID: PMC9459684 DOI: 10.3390/s22176509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/20/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO2. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted.
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Affiliation(s)
- Antonio Vincenzo Radogna
- Institute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
- Department of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | - Maria Elena Latino
- Department of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | - Marta Menegoli
- Department of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | - Carmela Tania Prontera
- Institute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | | | | | - Lucia Giampetruzzi
- Institute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | - Angelo Corallo
- Department of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
| | | | - Luca Francioso
- Institute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy
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5
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [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] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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6
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Wang Q, Liu H, Bai Y, Zhao Y, Guo J, Chen A, Yang S, Zhao S, Tan L. Research progress on mutton origin tracing and authenticity. Food Chem 2021; 373:131387. [PMID: 34742042 DOI: 10.1016/j.foodchem.2021.131387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/06/2021] [Accepted: 10/10/2021] [Indexed: 11/04/2022]
Abstract
With the globalization of the food market and the convenience of food transportation between countries, consumers are increasingly worried about the source and safety of the food they eat. Traceability has been identified as an important tool for ensuring food safety and quality. This review mainly introduces the principles of five food traceability technologies, summarizes the progress in mutton application, comprehensively compares and analyzes the five traceability technologies, and discusses their application prospects, advantages and disadvantages. It is aimed at promoting research and application of traceability technology in mutton safety, promoting establishment and improvement of food traceability system.
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Affiliation(s)
- Qian Wang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Haijin Liu
- Tibet Autonomous Region Agricultural and Livestock Product Quality and Safety Inspection Testing Center, Lhasa 850211, China
| | - Yang Bai
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jun Guo
- College of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, 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 100081, China
| | - Shuming Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Liqin Tan
- Changgao Agricultural Technology Extension Station, Beipiao 122109, China
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7
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Ramadhan A, Arymurthy AM, Sensuse DI, Muladno. Modeling e-Livestock Indonesia. Heliyon 2021; 7:e07754. [PMID: 34458605 PMCID: PMC8379459 DOI: 10.1016/j.heliyon.2021.e07754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/03/2021] [Accepted: 08/09/2021] [Indexed: 10/25/2022] Open
Abstract
The demand for beef resources in Indonesia is always increasing every year. However, Indonesia's national beef supply cannot meet those needs. The import of beef in large numbers likely to remain performed. The government has made various efforts to reduce imports and achieve self-sufficiency in beef. However, the government does not yet have a good identification, registration, documentation, and traceability system, so there is no truly valid data regarding the actual stock condition. Inaccuracy of data can lead to inappropriate policymaking in the livestock sector. Therefore, an e-Government initiative in the form of e-Livestock has been proposed. The definitions and success factors regarding e-Livestock have been revealed in our previous researches. Based on those researches, by using soft system methodology, hermeneutics, focus group discussion, and success factors, the business process models for e-Livestock in Indonesia will be created in this research. Apart from that, various kinds of recommendations for action to solve the problem will also be generated from this research. Those recommendations are about the functional requirement, the identification tool, the location numbering rule, the ownership documentation, the socialization of the e-Livestock, the institutional aspect of e-Livestock, the regulations underlie e-Livestock and the conceptual infrastructure diagram of e-Livestock. All of the business process models produced have been validated and their complexities are also calculated. Most of the business process model is very easy to understand. All the business process models and recommendations generated from this research can be a guide for the government when implementing e-Livestock.
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Affiliation(s)
- Arief Ramadhan
- Computer Science Department, BINUS Graduate Program - Doctor of Computer Science, Bina Nusantara University, Indonesia
| | | | | | - Muladno
- Faculty of Animal Husbandry, Bogor Agricultural University, Bogor, Indonesia
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8
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Zhang X, Guo M, Ismail BB, He Q, Jin TZ, Liu D. Informative and corrective responsive packaging: Advances in farm-to-fork monitoring and remediation of food quality and safety. Compr Rev Food Sci Food Saf 2021; 20:5258-5282. [PMID: 34318596 DOI: 10.1111/1541-4337.12807] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 11/28/2022]
Abstract
Microbial growth and fluctuations in environmental conditions have been shown to cause microbial contamination and deterioration of food. Thus, it is paramount to develop reliable strategies to effectively prevent the sale and consumption of contaminated or spoiled food. Responsive packaging systems are designed to react to specific stimuli in the food or environment, such as microorganisms or temperature, then implement an informational or corrective response. Informative responsive packaging is aimed at continuously monitoring the changes in food or environmental conditions and conveys this information to the users in real time. Meanwhile, packaging systems with the capacity to control contamination or deterioration are also of great interest. Encouragingly, corrective responsive packaging attempting to mitigate the adverse effects of condition fluctuations on food has been investigated. This packaging exerts its effects through the triggered release of active agents by environmental stimuli. In this review, informative and corrective responsive packaging is conceptualized clearly and concisely. The mechanism and characteristics of each type of packaging are discussed in depth. This review also summarized the latest research progress of responsive packaging and objectively appraised their advantages. Evidently, the mechanism through which packaging systems respond to microbial contamination and associated environmental factors was also highlighted. Moreover, risk concerns, related legislation, and consumer perspective in the application of responsive packaging are discussed as well. Broadly, this comprehensive review covering the latest information on responsive packaging aims to provide a timely reference for scientific research and offer guidance for presenting their applications in food industry.
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Affiliation(s)
- Xinhui Zhang
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
| | - Mingming Guo
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China.,Fuli Institute of Food Science, Zhejiang University, Hangzhou, China.,Ningbo Research Institute, Zhejiang University, Ningbo, China
| | - Balarabe B Ismail
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
| | - Qiao He
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
| | - Tony Z Jin
- U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, Wyndmoor, Pennsylvania, USA
| | - Donghong Liu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China.,Fuli Institute of Food Science, Zhejiang University, Hangzhou, China.,Ningbo Research Institute, Zhejiang University, Ningbo, China
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9
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Xu Y, Li X, Zeng X, Cao J, Jiang W. Application of blockchain technology in food safety control:current trends and future prospects. Crit Rev Food Sci Nutr 2020; 62:2800-2819. [PMID: 33307729 DOI: 10.1080/10408398.2020.1858752] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Blockchain technology is a distributed ledger technology and is expected to face some difficulties and challenges in various industries due to its transparency, decentralization, tamper-proof nature, and encryption security. Food safety has been paid increasing attention in recent years with economic development. Based on a systematic literature critical analysis, the causes of food safety problems and the state-of-the-art blockchain technology overview, including the definition of blockchain, development history, classification, structure, characteristics, and main applications, the feasibility and application prospects of blockchain technology in plant food safety, animal food safety, and processed food safety were proposed in this review. Finally, the challenges of the blockchain technology itself and the difficulties in the application of food safety were analyzed. This study contributes to the extant literature in the field of food safety by discovering the excellent potential of blockchain technology and its implications for food safety control. Our results indicated that blockchain is a promising technology toward a food safety control, with many ongoing initiatives in food products, but many food-related issues, barriers, and challenges still exist. Nevertheless, it is expected to provide a feasible solution for controlling food safety risks.
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Affiliation(s)
- Yan Xu
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Xiangxin Li
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Xiangquan Zeng
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Jiankang Cao
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Weibo Jiang
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
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10
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Abstract
Smart packaging is an emerging technology that has a great potential in solving conventional food packaging problems and in meeting the evolving packaged vegetables market needs. The advantages of using such a system lies in extending the shelf life of products, ensuring the safety and the compliance of these packages while reducing the food waste; hence, lessening the negative environmental impacts. Many new concepts were developed to serve this purpose, especially in the meat and fish industry with less focus on fruits and vegetables. However, making use of these evolving technologies in packaging of vegetables will yield in many positive outcomes. In this review, we discuss the new technologies and approaches used, or have the potential to be used, in smart packaging of vegetables. We describe the technical aspects and the commercial applications of the techniques used to monitor the quality and the freshness of vegetables. Factors affecting the freshness and the spoilage of vegetables are summarized. Then, some of the technologies used in smart packaging such as sensors, indicators, and data carriers that are integrated with sensors, to monitor and provide a dynamic output about the quality and safety of the packaged produce are discussed. Comparison between various intelligent systems is provided followed by a brief review of active packaging systems. Finally, challenges, legal aspects, and limitations facing this smart packaging industry are discussed together with outlook and future improvements.
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11
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Wu L, Gong X, Chen X, Hu W. Compromise Effect in Food Consumer Choices in China: An Analysis on Pork Products. Front Psychol 2020; 11:1352. [PMID: 32695046 PMCID: PMC7339376 DOI: 10.3389/fpsyg.2020.01352] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/21/2020] [Indexed: 11/13/2022] Open
Abstract
Compromise effect suggests that a product will have a higher chance to be chosen from a product choice set when its attributes are not the extremes (the best with the highest price or the worst with the lowest price). Few studies have examined compromise effect in food purchase. We investigate consumer pork purchase decision in the context of different decoy information intended to induce behavior and consider different presentation of decoy information. Furthermore, we explore compromise effect in relation to price, quality, and safety, which are directly related to consumer health. Results demonstrate that consumers exhibit significant compromise effects after receiving both low-price and high-price decoy information. However, when decoy information is presented after consumers have made choices without decoy information, their behavior changes systematically with a weakened compromise effect. This study highlights the implications of compromise effect in food marketing and policies related to food traceability and safety.
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Affiliation(s)
- Linhai Wu
- School of Business, Jiangnan University, Wuxi, China
| | - Xiaoru Gong
- School of Business, Jiangnan University, Wuxi, China
| | - Xiujuan Chen
- School of Business, Jiangnan University, Wuxi, China
| | - Wuyang Hu
- Department of Agricultural, Environmental, and Development Economics, The Ohio State University, Columbus, OH, United States
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12
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Qian J, Ruiz-Garcia L, Fan B, Robla Villalba JI, McCarthy U, Zhang B, Yu Q, Wu W. Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.03.025] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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13
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Zhao J, Li A, Jin X, Pan L. Technologies in individual animal identification and meat products traceability. BIOTECHNOL BIOTEC EQ 2020. [DOI: 10.1080/13102818.2019.1711185] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Affiliation(s)
- Jie Zhao
- Department of Agri-food Safety, Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing, PR China
- Department of Agri-food Safety, Risk Assessment Laboratory for Agro-products (Beijing), Ministry of Agriculture, Beijing, PR China
| | - An Li
- Department of Agri-food Safety, Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing, PR China
- Department of Agri-food Safety, Risk Assessment Laboratory for Agro-products (Beijing), Ministry of Agriculture, Beijing, PR China
| | - Xinxin Jin
- Department of Agri-food Safety, Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing, PR China
- Department of Agri-food Safety, Risk Assessment Laboratory for Agro-products (Beijing), Ministry of Agriculture, Beijing, PR China
| | - Ligang Pan
- Department of Agri-food Safety, Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing, PR China
- Department of Agri-food Safety, Risk Assessment Laboratory for Agro-products (Beijing), Ministry of Agriculture, Beijing, PR China
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14
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Liu Z, Mutukumira AN, Chen H. Food safety governance in China: From supervision to coregulation. Food Sci Nutr 2019; 7:4127-4139. [PMID: 31890192 PMCID: PMC6924309 DOI: 10.1002/fsn3.1281] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 08/20/2019] [Accepted: 08/23/2019] [Indexed: 11/12/2022] Open
Abstract
The food control and regulatory system in China is beset by several challenges. While firms have to reduce their costs in pursuit of benefits, customers are increasingly focusing on safety and quality of food products. Although the Chinese government has developed more stringent regulatory measures, food safety incidents still occur, including abuse of food additives, adulterated products as well as contamination by pathogenic microorganisms, pesticides, veterinary drug residues, and heavy metals, and use of substandard materials. A national food safety strategy has been proposed to assure food safety from "farm to table." This paper begins with the analysis of current food regulatory systems and then discusses cogovernance of food safety management in China. We explore the practice in the city of Shenzhen where government intervention has strengthened food control, thereby creating an opportunity to form a coregulatory system. The review highlights that the current food safety regulatory system of multi-agency structure can inevitably lead to insufficient incentives for business entities. Due to asymmetric information, lack of regulatory resources, and consumer advocacy, coregulation has been developed and is increasingly being promoted as an important instrument of food regulation.
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Affiliation(s)
- Zhe Liu
- School of ManagementHenan University of TechnologyZhengzhouChina
| | | | - Hongjun Chen
- School of ManagementHenan University of TechnologyZhengzhouChina
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15
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Larregui JI, Cazzato D, Castro SM. An image processing pipeline to segment iris for unconstrained cow identification system. OPEN COMPUTER SCIENCE 2019. [DOI: 10.1515/comp-2019-0010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractOne of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.
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Affiliation(s)
- Juan I. Larregui
- Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur (UNS), Instituto de Ciencias e Ingeniería de la Computación (ICIC UNS - CONICET), Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), ArgentinaBuenos Aires
| | - Dario Cazzato
- Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, LuxembourgLuxembourg
| | - Silvia M. Castro
- Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur (UNS), Instituto de Ciencias e Ingeniería de la Computación (ICIC UNS - CONICET), ArgentinaBuenos Aires
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16
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Fan B, Qian J, Wu X, Du X, Li W, Ji Z, Xin X. Improving continuous traceability of food stuff by using barcode-RFID bidirectional transformation equipment: Two field experiments. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.12.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Zhao J, Xu Z, You X, Zhao Y, He W, Zhao L, Chen A, Yang S. Genetic traceability practices in a large-size beef company in China. Food Chem 2019; 277:222-228. [PMID: 30502138 DOI: 10.1016/j.foodchem.2018.10.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 09/25/2018] [Accepted: 10/01/2018] [Indexed: 10/28/2022]
Abstract
An effective and trustworthy traceability system is important for food safety and quality; however, traditional traceability systems that only rely on the recording method do not completely prevent food fraud. DNA-based traceability techniques facilitate seamless connectivity within the entire food supply chain. A convenient and low-cost ear tag device was invented for collecting animal blood samples as an identity control, and a panel including 12 single nucleotide polymorphic (SNP) loci was selected to distinguish individuals with a matching probability of 1.70 × 10-5. The exact animal individual was identified by comparing the SNP genotype barcodes between the meat and blood samples derived from the recording system to further validate authenticity of the recording system. These results illustrate that a combination of the genetic traceability method and a traditional recording system can provide trustworthy traceability for consumers.
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Affiliation(s)
- Jie Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, PR China
| | - Zhenzhen Xu
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Xinyong You
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, PR China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Wenjing He
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Luyao Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Ailiang Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China.
| | - Shuming Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agrifood Safety and Quality, Ministry of Agriculture of China, Beijing 100081, PR China; Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China.
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18
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Miceli A, Settanni L. Influence of agronomic practices and pre-harvest conditions on the attachment and development of Listeria monocytogenes in vegetables. ANN MICROBIOL 2019. [DOI: 10.1007/s13213-019-1435-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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19
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Wang X, Fu D, Fruk G, Chen E, Zhang X. Improving quality control and transparency in honey peach export chain by a multi-sensors-managed traceability system. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.01.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Han JW, Ruiz-Garcia L, Qian JP, Yang XT. Food Packaging: A Comprehensive Review and Future Trends. Compr Rev Food Sci Food Saf 2018; 17:860-877. [DOI: 10.1111/1541-4337.12343] [Citation(s) in RCA: 285] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 01/31/2018] [Accepted: 02/01/2018] [Indexed: 11/28/2022]
Affiliation(s)
- Jia-Wei Han
- National Engineering Research Center for Information Technology in Agriculture; Room 1017, Building A, Beijing Nongke Masion, 11# Shuguang Huayuan Middle Road, Haidian District Beijing 100097 China
- National Engineering Laboratory for Agri-product Quality Traceability; Beijing Academy of Agricultural and Forestry Sciences; Beijing 100097 China
- Faculty of Information Technology; Beijing Univ. of Technology; Beijing 100124 China
| | - Luis Ruiz-Garcia
- Dept. de Ingeniería Agroforestal. E.T.S.I. Agronómica, Alimentaria y Biosistemas, Univ. Politécnica de Madrid; 28040 Spain
| | - Jian-Ping Qian
- National Engineering Research Center for Information Technology in Agriculture; Room 1017, Building A, Beijing Nongke Masion, 11# Shuguang Huayuan Middle Road, Haidian District Beijing 100097 China
- National Engineering Laboratory for Agri-product Quality Traceability; Beijing Academy of Agricultural and Forestry Sciences; Beijing 100097 China
| | - Xin-Ting Yang
- National Engineering Research Center for Information Technology in Agriculture; Room 1017, Building A, Beijing Nongke Masion, 11# Shuguang Huayuan Middle Road, Haidian District Beijing 100097 China
- National Engineering Laboratory for Agri-product Quality Traceability; Beijing Academy of Agricultural and Forestry Sciences; Beijing 100097 China
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