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Volume 14
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10.3390/toxins14090617
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Article

Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees

by
Marco Camardo Leggieri
1,
Marco Mazzoni
2,
Terenzio Bertuzzi
3,
Maurizio Moschini
3,
Aldo Prandini
3 and
Paola Battilani
1,*
1
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
2
Department of Livestock Population Genomics, University of Hohenheim, Garbenstraβe 17, 70599 Stuttgart, Germany
3
Department of Animal Science, Food, and Nutrition, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
*
Author to whom correspondence should be addressed.
Toxins 2022, 14(9), 617; https://doi.org/10.3390/toxins14090617
Submission received: 18 July 2022 / Revised: 26 August 2022 / Accepted: 1 September 2022 / Published: 3 September 2022
(This article belongs to the Section Mycotoxins)
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Abstract

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014–2015 and 2017–2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.
Keywords: e-nose; Fusarium graminearum; mycotoxin; machine learning; small grains; metal oxide sensors; DON e-nose; Fusarium graminearum; mycotoxin; machine learning; small grains; metal oxide sensors; DON

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MDPI and ACS Style

Camardo Leggieri, M.; Mazzoni, M.; Bertuzzi, T.; Moschini, M.; Prandini, A.; Battilani, P. Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees. Toxins 2022, 14, 617. https://doi.org/10.3390/toxins14090617

AMA Style

Camardo Leggieri M, Mazzoni M, Bertuzzi T, Moschini M, Prandini A, Battilani P. Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees. Toxins. 2022; 14(9):617. https://doi.org/10.3390/toxins14090617

Chicago/Turabian Style

Camardo Leggieri, Marco, Marco Mazzoni, Terenzio Bertuzzi, Maurizio Moschini, Aldo Prandini, and Paola Battilani. 2022. "Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees" Toxins 14, no. 9: 617. https://doi.org/10.3390/toxins14090617

APA Style

Camardo Leggieri, M., Mazzoni, M., Bertuzzi, T., Moschini, M., Prandini, A., & Battilani, P. (2022). Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees. Toxins, 14(9), 617. https://doi.org/10.3390/toxins14090617

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