The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Method of Reaching Consensus on Probability of Food Safety Based on the Integration of Finite Credible Data on Block Chain
Open accessAbstract
The use of new technologies to empower the social co-governance of food safety is a consensus in the theoretical and practical fields of food safety governance modernization. Based on the innovative features of the block chain such as non-tampering, consensus mechanism, and a smart contract, this paper proposes a new method to facilitate the achievement of the effect of social co-governance of food safety. Firstly, it provides a profound analysis of the causes of food safety problems and the drawbacks of traditional centralized supervision, and proposes a solution with decentralized features and the design process of smart contracts to realize the chaining and integration of finite credible data in the process of food circulation; secondly, it constructs a mathematical model through finite credible data on the chain and obtains the probabilistic consensus results of food safety through rigorous derivation; Finally, the scheme, model and algorithm are effectively practiced through the experimental environment of mature block chain platform and provincial food supervision platform. The example shows that the market failure problem in food safety can be greatly alleviated by the non-tampering feature of block chain; the consensus results formed by the credible data uploading, model construction and data derivation through smart contracts can greatly alleviate the problem of involution of government administrative supervision; and the social co-governance capacity of food safety can be improved through information sharing with the active synergy of multiple subjects of co-governance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“They tested their suggested two-stream technique on deepfakes, FaceSwap, Face2Face, NeuralTextures, and Celeb-DF datasets, and the results outperformed previous work.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as A. Yan, Yin-He, et al. (2021)
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The accuracy of cross-compression detection findings demonstrates that their approach is robust to compression factors.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as A. Yan, Yin-He, et al. (2021)
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The results demonstrate that their network has fewer parameters than other networks and outperforms existing state-of-the-art deepfake detection techniques on major deepfake data sets.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as L. Yan, Yin-He, et al. (2021)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Nakamoto, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3440802 · Cited by 14,677
Toward an ontology‐driven blockchain design for supply‐chain provenance
Kim, H. M., & Laskowski, M. (2018) · Intelligent Systems in Accounting, Finance and Management · doi:10.1002/isaf.1424 · Cited by 652 · cited 1×
“The combination with the food safety field is mainly to use the tamper-evident and traceability characteristics of block chain to realize a decentralized traceability system for the circulation information of the whole food chain [8], and some literature [9] constructed a framework for product traceability application…”
BackgroundManagement-Based Regulation: Prescribing Private Management to Achieve Public Goals
Coglianese, C., & Lazer, D. M. J. (2003) · Law & Society Review · doi:10.1046/j.0023-9216.2003.03703001.x · Cited by 437
Co-governing common goods: Interaction patterns of private and public actors
Tosun, J., Koos, S., & Shore, J. (2016) · Policy and Society · doi:10.1016/j.polsoc.2016.01.002 · Cited by 121
Towards an Ontology-Driven Blockchain Design for Supply Chain ProvenancePreprint
Kim, H., & Laskowski, M. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2828369 · Cited by 118
Rouvière, E., & Caswell, J. A. (2012) · Food Policy · doi:10.1016/j.foodpol.2012.02.009 · Cited by 88
Krom, M. D. (2008) · Sociologia Ruralis · doi:10.1111/j.1467-9523.2008.00474.x · Cited by 31
Opportunities for the Coregulation of Food Safety: Insights from the United Kingdom
Fearne, A. P., & Martínez, M. G. (2005) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.93485 · Cited by 15
Flagged first, then by citations
Research works printed but not linked: 17
- bian, 2021, Process wisdom and experience of food safety regulation in China, Journal of Food Safety Quality Inspection, 12, 1600
- yang, 2013, Food and drug safety focuses on supervision, Seeking Truth, 3
- liu, 2018, Comprehensive absorption of professional: The logic of food and drug safety regulatory system reform in the context of decentralization, J South China Normal Univ Social Sci Ed, 6, 100
- chen, 2017, Comparison and reference of foreign food safety social governance system, World Agriculture, 9, 176
- yinglian, 2016, Basic framework of national food safety strategy, China Soft Sci, 9, 18
- wang, 2019, Frontier hotspots and dynamic trends of food safety governance research in China, The Journal of Beijing Administrative College, 4, 35
- buterin, 2014, A next-generation smart contract and decentralized application platform
- shao, 2018, Blockchain technology: Architecture and progress, J Comput Sci, 41, 969
- cachin, 2016, Architecture of the hyperledger blockchain fabric, Proc Workshop Distrib Cryptocurrencies Consensus Ledgers (DCCL), 1
- cai, 2017, Research on blockchain-based application system development method, J Softw, 28, 1474
- yuan, 2018, Current status and outlook of blockchain consensus algorithm development, J Autom, 44, 2011
- bi, 2016, Local Implementation of Food and Drug Safety ‘Four Have Two Responsibilities’ Overview Consolidate the Foundation of Supervision
- wang, 2018, Application of big data in China’s food and drug wisdom supervision, China Food and Drug Administration, 5, 44
- he, 2018, A review of blockchain-based smart contract technologies and applications, Computer Research and Development, 55, 112
- kõlvart, 2016, Smart Contracts
- ye, 2018, Security detection model of block chain, Ruan Jian Xue Bao Journal of Software, 29, 1348
- sheng, 2018, A block chain smart contract-based approach to IoT data assetization, J Zhejiang Univ Eng Sci Ed, 52, 2150
Cited by
Sorted by citations
Items 101–123 of 123
Zarei, M., Fallah, H., Niknejad, Y., & Tari, D. B. (2023) · Water Science & Technology Water Supply · doi:10.2166/ws.2023.206 · Cited by 2
RetractedA Decentralized Blockchain-Enabled Immutability Mechanism Integrated with Multimodal Biometric
Kaur, V., Bhatt, D. P., Tharewal, S., & Tiwari, P. K. (2024) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon62832.2024.10982781 · Cited by 2
Liu, Q., Ding, T., Li, Y., Gao, Z., & Guo, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4357917 · Cited by 2
Xu, X., & Wang, L. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-26838-0 · Cited by 2
RetractedKadaei, S., Jafari, S., Jeyzanibrahimzade, E., Taher, M. E., Hashemi, M., Akar, P., & Akar, Ü. (2025) · Complexity · doi:10.1155/cplx/3963497 · Cited by 1
Cao, J., Cui, W., Luo, L., & Xie, G. (2026) · Sustainability · doi:10.3390/su18020615 · Cited by 1
SupTech Governance in Regulatory/Supervisory Government Agencies: A Systematic Literature Review
Mayasari, D., & Arman, A. A. (2022) · International Conference on Information Technology Systems and Innovation · doi:10.1109/icitsi56531.2022.9970863 · Cited by 1
Hua, H., Jinliang, W., Iqbal, W., Tang, Y. M., & Chau, K. Y. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-29486-6 · Cited by 1
RetractedJia, H., Abdalla, M. E. M., Idris, S. A., Rezapour, S., Shateyi, S., & Alsallami, S. A. M. (2023) · Results in Physics · doi:10.1016/j.rinp.2023.107143 · Cited by 1
Feng, Q., Ramli, R. M., & Liu, Y. (2022) · Journal of Electrical and Computer Engineering · doi:10.1155/2022/3797548 · Cited by 1
RetractedHuo, H., & Xu, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6139706 · Cited by 1
RetractedCao, Y. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/3922593
Li, X., & Chen, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1000153
Xiang, Q. (2023) · Water Science & Technology Water Supply · doi:10.2166/ws.2023.253
Noor, S., Alshehry, A. S., Khan, A., & Khan, I. (2023) · AIMS Mathematics · doi:10.3934/math.20221439
Deep Learning-Based Attack Prediction for Returns in Supply Chain Management Systems
Sai, K. K., Karthik, C. S., Chandralekha, M., & Salem, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2082-8_1
Greenwashing in the Fashion Industry: Does Attachment Help to Forgive?
Bagnato, G., Bargoni, A., Giachino, C., & Rizzo, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-59952-1_21
Susetyoko, R., Syarif, I., Fadliana, A., & Muffid, A. (2025) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1508/1/012009
Zhang, X. (2025) · British Food Journal · doi:10.1108/bfj-04-2025-0537
Blockchain in Government Services
ÇELİK, S. E. (2026) · Springer eBooks · doi:10.1007/978-3-031-76014-3_12-1
Blockchain for Healthcare Data Security
Kolli, Y. K., Kushala, K., Induru, V., Radhakrishnan, P., Ramar, V. A., & Premalatha, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0981-9.ch010
Karaman, F., & Çobanoğlu, F. (2026) · ADAM AKADEMİ Sosyal Bilimler Dergisi · doi:10.31679/adamakademi.1553438
Xiong, Z., Ye, C., & Yao, S. (2026) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2026.1947564
Metadata
- Authors
- Yan, L., Yin-He, S., Qian, Y., Zhi-Yu, S., Chun-Zi, W., & Zi-Yun, L.
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 123764-123776
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3197813564
- Cited by
- 123
- References
- 8
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 11 · 2023: 73 · 2022: 28
- FWCI
- 25.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Social Science Foundation of Shaanxi Province (2020F014)
- Special Project of Shaanxi Provincial Department of Education to Serve Local Areas (19JC015)
- Project of Xi’an Science and Technology University Talents Service Enterprise (GXYD7.7)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food Supply Chain Traceability
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food Safety and Hygiene
- Keywords
- food safety, blockchain, smart contracts, consensus algorithms, social co-governance, food supply chain, food safety governance, market failure, information sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
