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Cheli F., Campagnoli A., Pinotti L., Savoini G. and V Dell’Orto
Dept. of Veterinary Sciences and Technology for Food Safety,
University of Milan, Italy

The occurrence and control of mycotoxin in feed and food are items of great interest for researchers, producers, manufacturers and regulatory agencies. Reliability of measured levels of mycotoxins in feed and food is greatly affected by the collection of representative samples. Owing to the heterogeneous distribution of mycotoxins, the variability associated with a mycotoxin test procedure usually depends on the variability associated with the sampling plan.
In light of this the use/develop of screening methods without sample pretreatment that can detect mycotoxin in large number of individual samples can represent an important benefit for food and feed commodities safety assurance. In this scenario the aims of this study were to evaluate the potential of electronic nose (EN)  in detecting aflatoxins in corn containing samples. Thirty corn meal samples containing aflatoxins were submitted to ELISA assay for determination of total aflatoxins content.
 Afterwards the head space from 3g of each sample was analyzed by electronic nose. Samples were placed in 12ml glass vials hermetically sealed, and after a thermal desorption period, performed by EDU2 enricher/desorber unit (Air sense Analytics GmbH, Sherwin, Germany), were submitted to the 10 MOS (Metal Oxide Semiconductor) sensors of the PEN2 EN (Airsense Analytics GmbH, Schwerin, Germany). Each sample was evaluated three times. Data were submitted to Principal Component Analysis (PCA) as explorative approach.
Cross-validated Linear Discriminant Analysis (LDA) was adopted as classification model to make distinction from aflatoxins containing samples and aflatoxins free ones. Analysis was performed by SAS software (SAS Institute. Cary, NC, USA). In 24 corn samples ELISA assay quantify aflatoxins concentration in a range of 6ppb-100ppb, while 6 samples resulted under its detection limit (2 ppb). PCA analysis applied to EN data showed that the first two components were able to explain 98.04% of total data variability and also that sensors W1W and W5S were the most important to distinct between aflatoxins containing and free corn samples.
Furthermore EN results showed that MOS sensors array was related with the concentration of aflatoxins quantified by ELISA. Cross-validated LDA demonstrated the ability of EN in classification of positive samples from negative ones. Therefore it can be concluded that EN sensor array  can be used in grouping corn aflatoxins containing samples. Sample classification was in line with aflatoxins content measured by ELISA assay. With the aim to prove EN ability in a partial quantification of aflatoxins content further study will be designed in order to evaluate if sensors response signals are affected by mycotoxins per se or by mycotoxins associated volatiles from moulds metabolism.

Keywords:
Aflatoxins, Corn, Electronic Nose

Acknowledgements:
The authors wish to thank COMAZOO s.c.a.r.l. (Brescia, Italy) for its collaboration to this study.