A sensitive chromatographic strip test for the rapid detection of enrofloxacin in chicken muscle

恩诺沙星 丹诺沙星 氟甲喹 培氟沙星 司帕沙星 色谱法 化学 洛美沙星 检出限 依诺沙星 恶喹酸 诺氟沙星 氧氟沙星 环丙沙星 抗生素 生物化学 萘啶酸
作者
Xuelan Chen,Hengyi Xu,Weihua Lai,Yuan Chen,Xiaohui Yang,Yonghua Xiong
出处
期刊:Food Additives & Contaminants: Part A [Taylor & Francis]
卷期号:: 1-9 被引量:6
标识
DOI:10.1080/19440049.2011.641509
摘要

Abstract A sensitive colloidal gold immunochromatography assay using a specific monoclonal antibody was developed for the rapid detection of enrofloxacin (ENR) residues in chicken muscles. Anti‐ENR antibodies with high sensitivity and specificity are generated by immunising BALB/c mice with well‐characterised ENR‐bovine serum albumin conjugate. An orthogonal L9(3)3 test was designed, and various parameters that influenced the assay performance were investigated and optimised. Under the optimised conditions, the cut‐off limits of semi‐quantitative test strips for ENR were found to be 3 ng/mL in phosphate‐buffered saline and 8 µg/kg in chicken muscle. The ENR test strips showed a 6% cross‐reactivity with ciprofloxacin, 3% with norfloxacin, less than 1% with ofloxacin and sarafloxacin and 0.1% with the other eight fluoroquinolones including enoxacin, difloxacin, danofloxacin, pefloxacin, lomefloxacin, sparfloxacin, oxolinic acid and flumequine. Consistent results are produced from the parallel analysis of ENR‐contaminated chicken muscle extracts using test strips and ELISA. Keywords: enrofloxacinmonoclonal antibodycolloidal goldimmunochromatography assaytest strip Acknowledgements The authors are thankful for the financial support from the National 863 Program of China (No. 2007AA10Z438) and industry‐university cooperation Program of Jiangxi province of China (No. GJJJ10009). Notes These authors contributed equally to this work. Additional informationNotes on contributorsHengyi Xu These authors contributed equally to this work.

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
故意的毛豆完成签到,获得积分10
1秒前
yajuan33发布了新的文献求助10
2秒前
Orange应助wxj采纳,获得10
2秒前
2秒前
Ava应助王博雅采纳,获得10
3秒前
ppx发布了新的文献求助10
4秒前
你真是太炫啦完成签到 ,获得积分10
4秒前
瑾sir完成签到,获得积分10
4秒前
5秒前
成就的蝉完成签到 ,获得积分10
5秒前
俭朴的甜瓜应助Kevin采纳,获得30
6秒前
7秒前
科研通AI6.3应助ly采纳,获得10
8秒前
cuarzn完成签到,获得积分20
8秒前
聪慧雪糕发布了新的文献求助10
8秒前
汉堡包应助蓝天采纳,获得10
10秒前
zj完成签到,获得积分10
10秒前
付艳发布了新的文献求助10
10秒前
cuarzn发布了新的文献求助30
10秒前
斯文败类应助1699Z采纳,获得10
10秒前
yajuan33完成签到,获得积分10
11秒前
yyds发布了新的文献求助10
13秒前
慕青应助熙熙攘攘采纳,获得10
14秒前
14秒前
racill应助ppx采纳,获得10
14秒前
Jose433完成签到 ,获得积分20
15秒前
wxj完成签到,获得积分20
15秒前
kkkwang2发布了新的文献求助10
16秒前
sun完成签到,获得积分20
16秒前
18秒前
Zlla1024发布了新的文献求助10
19秒前
Lucas应助复杂瑛采纳,获得10
19秒前
20秒前
20秒前
21秒前
22秒前
23秒前
深情安青应助jimmy采纳,获得10
23秒前
24秒前
赵一铭发布了新的文献求助10
25秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
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 7243200
求助须知:如何正确求助?哪些是违规求助? 8867526
关于积分的说明 18705744
捐赠科研通 6917411
什么是DOI,文献DOI怎么找? 3196524
关于科研通互助平台的介绍 2370105
邀请新用户注册赠送积分活动 2171177