Abstract

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.
Lingua originaleEnglish
pagine (da-a)107722-N/A
RivistaFood Control
Volume2021
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • Aspergillus flavus
  • Discriminant analysis
  • Fusarium verticillioides
  • Logistic regression
  • Machine learning
  • Mycotoxin

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