摘要
黄曲霉毒素B_1(aflatoxin B_1,AFB_1)在自然界普遍存在,可污染多种粮食作物和饲料,给动物和人类健康造成严重威胁。为建立AFB_1高灵敏度的快速检测方法,本研究通过采用纳米金颗粒(Au nanoparticles,AuNPs)和辣根过氧化物酶(horseradish peroxidase,HRP)双标记AFB_1单克隆抗体,建立新型酶联免疫检测方法(HRP-AuNPs IC-ELISA),检测下限(IC10)为0.017ng/mL,检测区间(IC_(20)–IC_(80))为0.026–0.376ng/mL,半数抑制率(IC_(50))为0.099ng/mL,与黄曲霉毒素B2、G1、G2和M1的交叉反应率分别为2.7%、9.3%、2.1%和5.3%,与赭曲霉毒素A、伏马毒素B_1、桔青霉素、展青霉毒素和玉米赤霉烯酮几乎不存在交叉反应。在玉米和面粉样本中的加标回收率可达88.93–103.55%,与LC-MS/MS同时对天然样本中AFB_1含量进行检测,结果表明,两种方法相关性良好。本研究建立的HRP-AuNPs IC-ELISA耗时短且灵敏度高,可用于实际样本中AFB_1的快速定量检测与分析,也为其他霉菌毒素的精准检测技术开发提供参考。
Aflatoxin B1（AFB1）, a potent carcinogen, is one of the most toxic molecules occurring in natural world. Setting up a rapid and efficient method for detection of AFB1 would help guarantee the food safety and promote export of agricultural products. A novel enzyme linked immunosorbent assay（HRP-AuNPs IC-ELISA） using double-codified gold nanoparticles（AuNPs） labels modified with horseradish peroxidase（HRP）-conjugated anti-AFB1 was developed and applied in detection of AFB1 in cereal samples. Concentrations of the reagents and the reaction times were optimized to improve the performances of this analytical method. For the HRP-AuNPs IC-ELISA, the limit of detection was 0.017 ng/mL and the IC50 was 0.099 ng/mL. The linear working range was 0.026–0.376 ng/mL. The cross-reactivities with the aflatoxin B1 analogues（aflatoxin B2, aflatoxin G1, aflatoxin G2 and aflatoxin M1） were 2.7%, 9.3%, 2.1% and 5.3%, respectively. No cross-reactivity（0.01%） was observed with other mycotoxins（OTA, FB1, CIT, PAT and ZEN） which usually occur together in cereal samples. The recovery rates in spiked corn and wheat samples were 88.93%–103.55%, and the intra-day and inter-day relative standard deviations were both 10%. Parallel analysis of naturally contaminated cereal samples showed a good correlation between this novel HRP-AuNPs IC-ELISA and liquid chromatography-tandem mass spectrometry. This method provides a rapid, accurate, and highly sensitive method to determine the levels of AFB1 in food samples.
作者
章先
方云
付子贤
周一钊
方维焕
宋厚辉
ZHANG Xian;FANG Yun;FU Zi-Xian;ZHOU Yi-Zhao;FANG Wei-Huan;SONG Hou-Hui(College of Animal Science and Technology,Zhejiang A ＆ F University,Lin＇an,Zhejiang 312300,China;China-Australian Joint Laboratory for Animal Health Big Data Analytics,Lin＇an,Zhejiang 311300,China)
出处
《菌物学报》
CAS
CSCD
北大核心
2018年第11期1516-1524,共9页
Mycosystema
基金
浙江省自然科学基金(LQ17C170002)
浙江农林大学人才项目(2016FR025)
浙江省重点研发计划(2018C02041)
国家高新技术研究发展计划(863计划)(2012AA101602)~~
