Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Via Trentacoste, 2, 20134 Milan, Italy.
ATPr&d S.r.l., Via Ca' Marzare, 3, Camisano Vicentino, 36043 Vicenza, Italy.
Toxins (Basel). 2018 Oct 16;10(10):416. doi: 10.3390/toxins10100416.
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.
本研究旨在评估电子鼻与侧向流动免疫分析(LFA)联合应用于快速检测玉米样品中黄曲霉毒素和伏马菌素存在/共现的潜力。为此,共使用了 161 个玉米样本。根据商业侧向流动免疫分析(LFA)检测霉菌毒素的检测范围,将低于监管限量、单一污染和共污染的样本进行分类。采用 IBM SPSS Statistics 22 软件中的判别函数分析(DFA)程序对方法间的相关性进行评估。采用逐步变量选择法,通过 DFA 对样本进行分类,选择电子鼻传感器。八变量 DFA 模型对黄曲霉毒素的样本总体留一交叉验证正确率为 81%。七变量 DFA 模型对伏马菌素的样本总体留一交叉验证正确率为 85%。九变量 DFA 模型对三类污染(低于监管限量、单一污染、共污染)的样本总体留一交叉验证正确率为 65%。因此,尽管详尽的评估需要更大的数据集来进行验证程序,但电子鼻(e-nose)似乎是一种很有前途的快速/筛选方法,可以检测玉米籽粒中黄曲霉毒素、伏马菌素或两者的污染。