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.

Mycotoxin contamination in maize kernels: electronic nose as a screening tool for the industry / M. Ottoboni, M. Tretola, L. Pinotti, S. Gastaldello, V. Furlan, C. Maran, V. Dell'Orto, F. Cheli - In: Annual Meeting of the European Federation of Animal Science : book of abstractsPrima edizione. - [s.l] : Wageningen Academic Publishers, 2018 Aug. - ISBN 9789086863235. - pp. 325-325 (( Intervento presentato al 69. convegno Annual Meeting of the European Federation of Animal Science tenutosi a Dubrovnik nel 2018.

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

M. Ottoboni;M. Tretola;L. Pinotti;V. Dell'Orto;F. Cheli
2018

Abstract

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.
Settore AGR/18 - Nutrizione e Alimentazione Animale
http://www.eaap2018.org/
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/587968
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