Research on enhancing the efficiency of food safety sampling inspections in China based on Pareto's law.

Pareto's law efficiency of food safety sampling inspections food safety sampling strategy

Journal

Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334

Informations de publication

Date de publication:
04 Sep 2024
Historique:
revised: 30 07 2024
received: 30 03 2024
accepted: 04 08 2024
medline: 4 9 2024
pubmed: 4 9 2024
entrez: 4 9 2024
Statut: aheadofprint

Résumé

Food safety is pivotal for public welfare and directly impacts consumer health. Food safety sampling inspections (FSSIs) are essential in detecting unqualified food products and non-compliant manufacturers, which form an integral part of government regulatory frameworks. However, given the constraints on budgetary resources, improving the efficiency of food safety sampling inspections (EFSSIs) remains a considerable challenge in China's food quality and safety supervision. This study aims to apply Pareto's law, starting from the examination of food sample testing items and major hazard types, to theoretically analyze methods for improving the EFSSIs. Following the theoretical analysis, the research employs provincial food sampling data from China in 2022 to empirically validate the proposed improvement strategies. The research findings indicate that applying Pareto's law significantly reduces the number of items that should be tested for each food subcategory, effectively lowering testing costs for each batch of food samples. Theoretically, employing Pareto's law in sampling inspections can increase the EFSSIs to 2.78 times the current observed level. Furthermore, empirical validation using food sampling data confirms that EFSSIs can be improved to 2.12 times the existing level, consistent with theoretical predictions. Implementing Pareto's law in FSSIs facilitates the detection of more unqualified food products and non-compliant manufacturers without additional financial burden, significantly enhancing the EFSSIs. This approach provides an innovative strategy for government to bolster their food safety management efforts. © 2024 Society of Chemical Industry.

Sections du résumé

BACKGROUND BACKGROUND
Food safety is pivotal for public welfare and directly impacts consumer health. Food safety sampling inspections (FSSIs) are essential in detecting unqualified food products and non-compliant manufacturers, which form an integral part of government regulatory frameworks. However, given the constraints on budgetary resources, improving the efficiency of food safety sampling inspections (EFSSIs) remains a considerable challenge in China's food quality and safety supervision. This study aims to apply Pareto's law, starting from the examination of food sample testing items and major hazard types, to theoretically analyze methods for improving the EFSSIs. Following the theoretical analysis, the research employs provincial food sampling data from China in 2022 to empirically validate the proposed improvement strategies.
RESULTS RESULTS
The research findings indicate that applying Pareto's law significantly reduces the number of items that should be tested for each food subcategory, effectively lowering testing costs for each batch of food samples. Theoretically, employing Pareto's law in sampling inspections can increase the EFSSIs to 2.78 times the current observed level. Furthermore, empirical validation using food sampling data confirms that EFSSIs can be improved to 2.12 times the existing level, consistent with theoretical predictions.
CONCLUSION CONCLUSIONS
Implementing Pareto's law in FSSIs facilitates the detection of more unqualified food products and non-compliant manufacturers without additional financial burden, significantly enhancing the EFSSIs. This approach provides an innovative strategy for government to bolster their food safety management efforts. © 2024 Society of Chemical Industry.

Identifiants

pubmed: 39230201
doi: 10.1002/jsfa.13822
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Natural Science Foundation of China

Informations de copyright

© 2024 Society of Chemical Industry.

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Auteurs

Taiping Li (T)

College of Economics and Management, Nanjing Agricultural University, Nanjing, China.

Yun Luo (Y)

College of Economics and Management, Nanjing Agricultural University, Nanjing, China.

Tong Zhao (T)

College of Economics and Management, Nanjing Agricultural University, Nanjing, China.

Classifications MeSH