Advancements and greenification potential of magnetic molecularly imprinted polymers for chromatographic analysis of veterinary drug residues in milk

分子印迹聚合物 兽药 兽药 药品 化学 色谱法 生物 药理学 医学 兽医学 生物化学 选择性 催化作用
作者
Saqib Farooq,Lizhou Xu,S. M. Rahmat Ullah,Jinhua Li,Jiyun Nie,Jianfeng Ping,Yibin Ying
出处
期刊:Comprehensive Reviews in Food Science and Food Safety [Wiley]
卷期号:23 (4) 被引量:13
标识
DOI:10.1111/1541-4337.13399
摘要

Abstract Milk, as a widely consumed nutrient‐rich food, is crucial for bone health, growth, and overall nutrition. The persistent application of veterinary drugs for controlling diseases and heightening milk yield has imparted substantial repercussions on human health and environmental ecosystems. Due to the high demand, fresh consumption, complex composition of milk, and the potential adverse impacts of drug residues, advanced greener analytical methods are necessitated. Among them, functional materials‐based analytical methods attract wide concerns. The magnetic molecularly imprinted polymers (MMIPs), as a kind of typical functional material, possess excellent greenification characteristics and potencies, and they are easily integrated into various detection technologies, which have offered green approaches toward analytes such as veterinary drugs in milk. Despite their increasing applications and great potential, MMIPs’ use in dairy matrices remains underexplored, especially regarding ecological sustainability. This work reviews recent advances in MMIPs’ synthesis and application as efficient sorbents for veterinary drug extraction in milk followed by chromatographic analysis. The uniqueness and effectiveness of MMIPs in real milk samples are evaluated, current limitations are addressed, and greenification opportunities are proposed. MMIPs show promise in revolutionizing green analytical procedures for veterinary drug detection, aligning with the environmental goals of modern food production systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
KSGGS完成签到,获得积分10
1秒前
3秒前
3秒前
3秒前
李健应助安辙采纳,获得10
3秒前
劉劉完成签到,获得积分10
3秒前
科研通AI6.4应助蓝胖子采纳,获得10
4秒前
5秒前
Jasper应助sky采纳,获得10
6秒前
7秒前
8秒前
微笑焱彬发布了新的文献求助10
8秒前
jzc完成签到,获得积分10
8秒前
邪王真眼完成签到 ,获得积分10
9秒前
9秒前
11秒前
文静的含蕾应助ddddddddddd采纳,获得10
11秒前
longer发布了新的文献求助10
11秒前
11秒前
香蕉觅云应助务实的雍采纳,获得10
12秒前
12秒前
Wenky完成签到 ,获得积分10
12秒前
lcm完成签到,获得积分20
12秒前
ccc发布了新的文献求助10
13秒前
believeachao完成签到,获得积分10
13秒前
Fish应助叁壹粑粑采纳,获得10
13秒前
14秒前
14秒前
lcm发布了新的文献求助10
14秒前
bastien发布了新的文献求助10
15秒前
科目三应助zz采纳,获得10
16秒前
yust完成签到,获得积分10
16秒前
11111111发布了新的文献求助10
16秒前
jinnm发布了新的文献求助10
17秒前
科研通AI6.4应助刘兆亮采纳,获得10
17秒前
嗯呐完成签到,获得积分10
18秒前
LTJ完成签到,获得积分10
18秒前
19秒前
Kyc发布了新的文献求助10
19秒前
可爱的函函应助czyimba采纳,获得10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
内視鏡的に摘除しえた十二指腸乳頭部腫瘍の2例 660
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Interpolation and Regression Models for the Chemical Engineer: Solving Numerical Problems 400
The Neuroscience of Language 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7687289
求助须知:如何正确求助?哪些是违规求助? 9250306
关于积分的说明 19961964
捐赠科研通 7260239
什么是DOI,文献DOI怎么找? 3289775
关于科研通互助平台的介绍 2446665
邀请新用户注册赠送积分活动 2294282