摘要
通过纳米抗体活性口袋生物素化修饰实现识别活性提升,并以生物素化纳米抗体(Biotin-Nb2-12)作为识别元件,结合生物素-多聚辣根过氧化物酶标记的链霉亲和素(polyHRP-SA)亲和识别信号系统,建立了检测果蔬中百草枯的生物素-链霉亲和素酶联免疫分析方法(BA-ELISA)。经多参数系统优化,所建方法的检出限(LOD)达0.58 pg/mL,半数抑制浓度(IC_(50))为14.1 pg/mL,线性范围为1.7~116.2 pg/mL,且与功能及结构类似物无显著交叉反应。相比传统间接竞争酶联免疫分析方法(icELISA),该法的IC_(50)提高了85倍,抗体消耗量降低至1/8,在大白菜和雪梨样品中的平均加标回收率为94.5%~116%,可用于果蔬中百草枯的痕量检测。
Paraquat has high toxicity in humans and no specific antidote.It is essential to develop a rapid and sensitive paraquat residue detection technology.This study achieved enhanced recognition activity through biotinylation modification of the nanobody's active pocket.Using biotinylated nanobodies(Biotin-Nb2-12) as recognition elements,combined with a biotin-horseradish peroxidase-labeled streptavidin(polyHRP-SA) affinity recognition signal system,we established a biotin-streptavidin enzyme-linked immunosorbent assay(BA-ELISA) for determining paraquat.Under optimal working conditions,the limit of detection(LOD) of BA-ELISA was 0.58 pg/mL,half-inhibition concentration(IC_(50)) was 14.1 pg/mL,and the linear detection range was 1.7-116.2 pg/mL,with no significant cross-reactivity with structural and functional analogs.Compared to icELISA,the sensitivity of BA-ELISA was significantly improved by 85-fold and antibody consumption was reduced by 8-fold.The method was applied to detect paraquat with average recoveries of 94.5%-116% in cabbage and pear samples,demonstrating that this method can be used for trace detection of paraquat in fruits and vegetables.
作者
张咏仪
杨金易
曾道平
徐振林
王弘
田元新
孙远明
沈玉栋
ZHANG Yong-yi;YANG Jin-yi;ZENG Dao-ping;XU Zhen-lin;WANG Hong;TIAN Yuan-xin;SUN Yuan-ming;SHEN Yu-dong(Guangdong Provincial Key Laboratory of Food Quality and Safety,College of Food Science,South China Agricultural University,Guangzhou 510642,China;Guangdong Provincial Key Laboratory of New Drug Screening,School of Pharmaceutical Sciences,Southern Medical University,Guangzhou 510515,China;Wens Institute,Wens Foodstuff Groups Co.LTD.,Yunfu 527499,China)
出处
《分析测试学报》
CAS
CSCD
北大核心
2024年第12期1959-1964,共6页
Journal of Instrumental Analysis
基金
国家自然科学基金资助项目(32272400)
广东省基础与应用基础研究项目(2019B1515210025,2018B030314005)
广州市科技计划项目(202206010146)。
