SNP genotyping in sheep from northwest and east China for meat traceability

Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Abulikemu, Ba Tur; Luo, Ruiming; Li, Xiao; Xu, Xinglian; Li, Chunbao

doi:10.1007/s00003-017-1092-2

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…At the same time, the selected twelve most polymorphic SNPs were successfully used for meat traceability of Halal beef through meat and reserved blood sample comparison, which had the potential to further guarantee the safety of Halal beef in the Chinese market [58]. Wu et al [59] employed seven polymorphic SNPs to verify the origins of lamb in Northwest and East of China, and the results showed that the probability for two random individuals to have the same genotype was only 0.185% [59]. Besides individual identification, SSR and SNP markers can also be used for breed information confirmation.…”

Section: Dna Fingerprintingmentioning

confidence: 99%

Technologies in individual animal identification and meat products traceability

Zhao¹,

An²,

Jin³

et al. 2020

Biotechnology & Biotechnological Equipment

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An effective and trustworthy traceability system contributes to improving food quality and safety and responds to consumers' demand for food provenance information. Safe meat and its products are crucial to consumers and society. Livestock feeding regime and geographical origin are closely related to the properties and the safety of animal origin food, but the information is often invisible to consumers, which makes is easier to use fraudulent practices throughout the whole supply chain. Technologies and their proper use in traceability systems are important for the safety of animal origin foods. An essential component in an integrated traceability chain includes individual animal identification and trace back of related meat products. In this review, we examine the technologies for individual animal identification, including the radio frequency identification system and DNA fingerprinting. For meat products, traceability technologies focus on the chemical components fingerprinting, including measurement of stable isotope ratios, mineral element tracing and organic component fingerprinting. Also, future trends in food traceability systems need to be improved to promote the establishment of more efficient and trustworthy traceability systems to ensure food safety and quality up to standard.

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Section: Dna Fingerprintingmentioning

confidence: 99%

Technologies in individual animal identification and meat products traceability

Zhao¹,

An²,

Jin³

et al. 2020

Biotechnology & Biotechnological Equipment

View full text Add to dashboard Cite

show abstract

“…and Safety Act (USA), and the National Agriculture and Food Traceability System (Canada) [13,14]. To improve company supply chain management (SCM), research has focused on state traceability systems and their application for satisfying various agro-food or foodquality requirements, such as for vegetables [15,16], fruits [17][18][19], olive oil [20], aquaculture [21], meat [22], or beef [23,24].…”

Section: Introductionmentioning

confidence: 99%

Finding Traceability Granularity Influencing Factors using Rough Set Method: An Empirical Analysis on Vegetable Companies in Tianjin City, China

Qian¹,

Li²,

Geng³

et al. 2023

Preprint

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Evaluating the efficacy of the traceability systems (TS) plays an important role not only for planning system implementation before development, but also for analyzing system performance once the system is in use. In the present work, we evaluate the traceability granularity using a comprehensive and quantifiable model and try find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin city, China. Granularity indicators were collected mostly by the TS platform to ensure the objectivity of the data, and the granularity score was evaluated by using a TS granularity model. The results show a clear imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range [50,60] exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed by using a rough set method based on nine factors pre-selected by using a published method. The results show that the factor “number of TS operation staff” is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue &gt; Supply chain (SC) integration degree &gt; Cognition of TS &gt; Certification system &gt; Company sales &gt; Informationization management level &gt; System maintenance investment &gt; Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies.

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“…Several systems of government supervision, like the EU Rapid Alert System for Food and Feed (RASFF), the Food Modernization and Safety Act (USA), and the National Agriculture and Food Traceability System (Canada), have been implemented [13,14]. To improve company supply chain management (SCM), research has focused on state traceability systems and their application for satisfying the various requirements of agro-food or food quality, such as for vegetables [15,16], fruits [17][18][19], olive oil [20], aquaculture [21], meat [22], or beef [23,24].…”

Section: Introductionmentioning

confidence: 99%

Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China

Qian

Geng

et al. 2023

Foods

View full text Add to dashboard Cite

The effectiveness evaluation of the traceability system (TS) is a tool for enterprises to achieve the required traceability level. It plays an important role not only for planning system implementation before development but also for analyzing system performance once the system is in use. In the present work, we evaluate traceability granularity using a comprehensive and quantifiable model and try to find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin, China. We collect granularity indicators mostly through the TS platform to ensure the objectivity of the data and use the TS granularity model to evaluate the granularity score. The results show that there is an obvious imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range (50,60) exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed using a rough set method based on nine factors pre-selected using a published method. The results show that the factor “number of TS operation staff” is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue > Supply chain (SC) integration degree > Cognition of TS > Certification system > Company sales > Informationization management level > System maintenance investment > Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies.

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SNP genotyping in sheep from northwest and east China for meat traceability

Cited by 6 publications

References 19 publications

Technologies in individual animal identification and meat products traceability

Technologies in individual animal identification and meat products traceability

Finding Traceability Granularity Influencing Factors using Rough Set Method: An Empirical Analysis on Vegetable Companies in Tianjin City, China

Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China

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