Mycotoxin contamination in maize kernels: electronic nose as a screening tool for the industry

The aim of this study was to evaluate the potential use of an electronic nose (e-nose) for rapid mycotoxin detection in maize, with a focus on aflatoxin (AFLA) and fumonisin (FUM) occurrence and co-occurrence. Twenty-five maize samples were analysed by commercial lateral flow immunoassays (LFIAs) and classified as non-contaminated (NC), single-contaminated (SC), and co-contaminated (COC) by AFLA and FUM according to the detection ranges of LFIA kits. The same samples were analysed by a PEN3 e-nose equipped with 10 MOS sensors (Airsense Analytics GmbH). E-nose data were statistically analysed by Discriminant Function Analysis (DFA) (IBM SPSS 22.0 predictive analytics software). Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated (LOOCV) percentage of samples correctly classified by the quadrivariate DFA model for AFLA was 67%. The overall LOOCV percentage of samples correctly classified by the single-variate DFA model for FUM was 70%. To test the potential of the e-nose in detecting co-contaminated samples, a discriminant function including five e-nose sensors, was used. The overall LOOCV percentage of samples correctly classified for NC, SC, and COC classes was 65%. In the case of NC samples, the percentage of samples correctly classified was 77%, while it drops to 54% and 61%, for SC and COC samples, respectively. Results indicate that e-nose could be a promising rapid/screening method to detect single or co-contaminated maize kernels. However, e-nose is still far from replacing commercial rapid kit assays, which are quite well defined and broadly used