首页> 美国卫生研究院文献>Toxins >Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize
【2h】

Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize

机译:结合电子鼻和侧向流免疫分析法(LFIA)快速检测玉米中的黄曲霉毒素和伏马菌素

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.
机译:这项研究的目的是评估电子鼻结合侧向流免疫测定法在玉米样品中快速黄曲霉毒素和伏马毒素的发生/共现检测中的潜在用途。为此,已经使用了161个玉米样品。低于规定的限值,根据为商业侧向流免疫测定法(LFIA)建立的用于确定霉菌毒素的检测范围,对单次污染和共同污染的样品进行分类。使用IBM SPSS Statistics 22,通过判别函数分析(DFA)程序评估了方法之间的对应性。进行了逐步变量选择,以选择电子鼻传感器以通过DFA对样品进行分类。由八变量DFA模型正确分类的黄曲霉毒素样本的总交叉检验交叉验证百分比为81%。由七变量DFA模型对伏马菌素正确分类的样品,省略了交叉验证的整体百分比为85%。通过九变量DFA模型正确分类的三类污染物(低于监管限值,单一污染,共同污染)的总遗漏交叉验证百分比为65%。因此,即使详尽的评估将需要更大的数据集来执行验证程序,电子鼻(e-nose)似乎也是一种有前途的快速/筛选方法,可用于检测玉米种仁中黄曲霉毒素,伏马菌素或两者的污染。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号