An electronic nose supported by an artificial neural network for the rapid detection of aflatoxin B1 and fumonisins in maize

Mycotoxins pose a significant threat to the safety of food and its products. A rapid, reliable, and cheap method of testing for the most important regulated mycotoxins would be useful and time saving. This study aimed to evaluate the potential use of an electronic nose (e-nose) for rapid identification of mycotoxin contamination above legal limits in maize samples. A total of 316 maize samples were collect from a commercial field in Northern Italy from 2014 to 2018 and analyzed for contamination with aflatoxin B1 (AFB1) and fumonisins (FBs), both using a conventional method (HPLC-MS) and a portable e-nose AIR PEN 3 (Airsense Analytics GmbH, Schwerin, Germany) equipped with a 10-metal oxide sensor array. Artificial neural network (ANN), logistic regression (LR), and discriminant analysis (DA) were used to investigate whether the e-nose was capable of separating samples contaminated at levels above or below the legal limits, either for AFB1 or FBs. All the methodologies used showed high accuracy (>= 70%) in distinguishing maize grain contamination above or below the legal limit. Notably, ANN performed better than the other methods, with 78% and 77% accuracy for AFB1 and FBs, respectively. This was the first time that five years of data and three different statistical approaches have been adopted to check e-nose performance. Results suggest that the e-nose supported by ANN could be a rapid and reliable tool for the detection of AFB1 and FBs in maize.

Automated summary

The study evaluated the potential use of an electronic nose for rapid identification of mycotoxin contamination in maize samples and found that it had high accuracy in distinguishing contamination levels above or below legal limits. Artificial neural network (ANN) was the best method with 78% and 77% accuracy for AFB1 and FBs, respectively, indicating that the e-nose supported by ANN could be a rapid and reliable tool for mycotoxin detection.