呕吐毒素(deoxynivalenol,DON)对人类和动物的健康以及食品经济构成了极大的威胁.该研究建立一种基于金纳米花(gold nanoflowers,AuNFs)的免疫层析试纸条(immunochromatographic test strip,ICTS)快速检测玉米中的呕吐毒素的方法.采用种子生长法制备了 AuNFs,将其与抗DON单克隆抗体结合制备免疫层析试纸条中的金标探针,并对pH值、抗体量、上样缓冲液浓度、检测线(test-line,T线)浓度和金标探针量进行优化,最终用于玉米中的DON检测.经过优化实验条件,确定了最优pH值为7.5,最佳抗体添加量为1μL,最适上样缓冲液浓度为PBST+1％PVP K30,最好的T线质量浓度为0.1 mg/mL以及最佳探针量为3 μL.在最优反应体系下,实验结果可以通过肉眼观察进行定性分析,该方法检出限为20 μg/kg,灵敏度≥ 99％、假阳性率≤1％、假阴性率≤1％,且与现有标准方法检测结果一致,同时与其他真菌毒素无交叉反应.以AuNFs标记抗体为探针制备的ICTS灵敏度高,特异性强,可以实现玉米中DON的快速检测.
Detection of deoxynivalenol in corn using gold nanoflowers based immunochromatographic test strips
Deoxynivalenol(DON)poses a great threat to human and animal health,as well as the food economy.This study estab-lished a method for the rapid detection of DON in corn using a gold nanoflowers(AuNFs)-based immunochromatographic test strips(ICTS).AuNFs were prepared using a seed growth method and conjugated with anti-DON monoclonal antibodies to generate the gold-la-beled probes for immunochromatographic test strips.The pH,antibody volume,sample buffer concentration,T-line concentration,and the volume of gold-labeled probe were optimized.The developed method was subsequently used for detecting DON in corn.After optimizing the experimental conditions,the optimal pH was determined to be 7.5,the best antibody volume was 1 μL,the most suitable sample buffer concentration was PBST+1％PVP K30,the optimal T-line concentration was 0.1 mg/mL,and the volume of gold-labeled probe was 3 µL.Under the optimal conditions of the reaction system,the method achieved a detection limit of 20 μg/kg,with a sensitivity of 99％,a false positive rate of ≤1％,and a false negative rate of ≤1％.Additionally,the results were consistent with existing standard methods and show no cross-reactivity with other mycotoxins.The ICTS using the AuNFs-based immunochromatographic test strips exhibits high sensitivity and strong specificity,enabling rapid detection of DON in corn.
gold nanoflowersseed growth methodimmunochromatographic test stripsrapid detectiondeoxynivalenol
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维普
