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Wu L, Tang H, Dai X, Chen X, Zhang J. Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework. Heliyon 2024; 10:e30340. [PMID: 38737241 PMCID: PMC11088275 DOI: 10.1016/j.heliyon.2024.e30340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/14/2024] Open
Abstract
This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.
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Affiliation(s)
- Linhai Wu
- Business School, Jiangnan University, Wuxi, 214122, China
- Food Safety Risk Management Research Institute, Jiangnan University, Wuxi, 214122, China
| | - Hejie Tang
- Business School, Jiangnan University, Wuxi, 214122, China
| | - Xiaoting Dai
- Business School, Jiangnan University, Wuxi, 214122, China
| | - Xiujuan Chen
- Business School, Jiangnan University, Wuxi, 214122, China
| | - Jingxiang Zhang
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China
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Simon A, Barradas NP, Jeynes C, Romolo FS. Addressing forensic science challenges with nuclear analytical techniques - A review. Forensic Sci Int 2024; 358:111767. [PMID: 37385904 DOI: 10.1016/j.forsciint.2023.111767] [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: 11/08/2022] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 07/01/2023]
Abstract
We review the application of Nuclear Analytical Techniques (NATs) to forensic problems for the first time. NATs include neutron activation analysis (NAA), carried out in nuclear reactors for elemental analysis; accelerator-based techniques, mainly Ion Beam Analysis (IBA) for elemental and molecular analysis; and Accelerator Mass Spectrometry (AMS) for dating of traces of forensic interest by "radiocarbon dating" and other related methods. Applications include analysis of drugs of abuse, food fraud, counterfeit medicine, gunshot residue, glass fragments, forgery of art objects and documents, and human material. In some applications only the NATs are able to provide relevant information for forensic purposes. This review not only includes a wide collection of forensic applications, but also illustrates the wide availability worldwide of NATs, opening up opportunities for an increased use of NATs in routine forensic casework.
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Affiliation(s)
- A Simon
- International Atomic Energy Agency, Vienna, Austria.
| | | | - C Jeynes
- University of Surrey Ion Beam Centre, Guildford, England, UK
| | - F S Romolo
- Università degli Studi di Bergamo, Bergamo, Italy
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Soon-Sinclair JM, Nyarugwe S, Jack L. Food fraud and mitigating strategies of UK food supply chain during COVID-19. Food Control 2023; 148:109670. [PMID: 36748095 PMCID: PMC9894533 DOI: 10.1016/j.foodcont.2023.109670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/06/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Covid-19 had shown the vulnerability of the food supply chain and fraudsters may take advantage of the pandemic whilst the population needed a continuous supply of safe and quality food. The lack of monitoring and policing in the food supply chain may encourage fraudsters to upscale their operations. Previous studies had warned of a surge in fraudulent products due to COVID-19. This raised the question on whether food fraud had increased during the pandemic? This study aims to investigate food fraud during COVID-19 and how the food supply chain develops mitigating strategies against fraudulent activities. A mixed-method approach including survey and semi-structured interviews were conducted among UK food businesses. Two hundred and two agri-food businesses responded to the survey and 15 semi-structured interviews were conducted. The majority of the food businesses did not experience an increase of food fraud activities during COVID-19. Two thematic domains and ten sub-themes were identified from the data set. There was a heightened sense of anticipation and preparation for increased fraudulent activities during the pandemic. The main risk mitigating strategies included horizon scanning; developing and maintaining supplier relationship and assurance; understanding product characteristics, testing capabilities, conducting vulnerability assessments and training. Practical and cost-effective strategies for small and medium food businesses were recommended. This is the first empirical study on food fraud and mitigating strategies of the UK food supply chain during the pandemic. Our findings provide evidence for informing the policies and practices of the food regulatory authorities as well as best practices to protect the UK food supply chain against food fraud during exogenous shocks like COVID-19.
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Affiliation(s)
- Jan Mei Soon-Sinclair
- Faculty of Allied-Health and Wellbeing, University of Central Lancashire, Preston, PR1 2HE, UK
| | - Shingai Nyarugwe
- Faculty of Allied-Health and Wellbeing, University of Central Lancashire, Preston, PR1 2HE, UK
| | - Lisa Jack
- School of Accounting, Economics and Finance, Faculty of Business and Law, University of Portsmouth, Portsmouth, PO1 3DE, UK
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DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview. Foods 2022; 11:foods11030258. [PMID: 35159410 PMCID: PMC8834242 DOI: 10.3390/foods11030258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.
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Brooks C, Parr L, Smith JM, Buchanan D, Snioch D, Hebishy E. A review of food fraud and food authenticity across the food supply chain, with an examination of the impact of the COVID-19 pandemic and Brexit on food industry. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108171] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021; 62:6605-6645. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Affiliation(s)
- Diego Galvan
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina, PR, Brazil
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Campo Mourão, PR, Brazil
| | - Carlos Adam Conte-Junior
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
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