Suppr超能文献

动物源食品中β-内酰胺类抗生素检测提取方法的范围综述

Scoping Review of Extraction Methods for Detecting β-Lactam Antibiotics in Food Products of Animal Origin.

作者信息

Pacyńska Joanna, Niedzielski Przemysław

机构信息

Department of Analytical Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznań, 8 Uniwersytetu Poznańskiego Street, 61-614 Poznań, Poland.

Provincial Veterinary Inspectorate in Poznań, 250 Grunwaldzka Street, 60-166 Poznań, Poland.

出版信息

Molecules. 2025 Apr 27;30(9):1937. doi: 10.3390/molecules30091937.

Abstract

The widespread use of β-lactam antibiotics in veterinary medicine and food production has contributed to the rise of antibiotic-resistant bacteria, posing significant health risks to humans. This issue is recognized by various regulatory agencies, which settled maximum residue limits (MRLs) for antibiotics in animal-derived foods. To adhere to these regulations, sensitive and selective methods are required for monitoring antibiotic residues. Due to the critical importance of sample preparation in the analysis, numerous extraction techniques have been developed. This review focuses on various methodologies for extracting β-lactam antibiotics from different food matrices. The paper summarizes the procedures for the extraction of β-lactam antibiotics identified in the literature, indicating their detailed methodology. The summary may be useful for any laboratories preparing new applications for the determination of antibiotics in food. Research studies analyzed in the paper were collected from databases, such as Google Scholar, PubMed, and Scopus. After a close evaluation of about 200 articles (published between 2010 and 2024), 35 of them, which met the criteria, were included in the analysis.

摘要

β-内酰胺类抗生素在兽医学和食品生产中的广泛使用导致了抗生素耐药菌的增加,对人类健康构成了重大风险。这一问题得到了各监管机构的认可,它们设定了动物源性食品中抗生素的最大残留限量(MRLs)。为遵守这些规定,需要灵敏且具选择性的方法来监测抗生素残留。由于样品前处理在分析中至关重要,已开发出众多提取技术。本综述聚焦于从不同食品基质中提取β-内酰胺类抗生素的各种方法。本文总结了文献中鉴定出的β-内酰胺类抗生素提取程序,并说明了其详细方法。该总结可能对任何准备用于食品中抗生素测定新应用的实验室有用。本文分析的研究是从谷歌学术、PubMed和Scopus等数据库收集的。在对约200篇文章(2010年至2024年发表)进行仔细评估后,其中35篇符合标准的文章被纳入分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c0/12073301/e49a097885ad/molecules-30-01937-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验