Electronic nose for the rapid detection of deoxynivalenol in wheat using classification and regression trees

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dc.contributor.authorCamardo Leggieri, Marco
dc.contributor.authorMazzoni, Marco
dc.contributor.authorBertuzzi, Terenzio
dc.contributor.authorMoschini, Maurizio
dc.contributor.authorPrandini, Aldo
dc.contributor.authorBattilani, Paola
dc.date.accessioned2024-09-03T14:03:41Z
dc.date.available2024-09-03T14:03:41Z
dc.date.issued2022de
dc.description.abstractMycotoxin 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.en
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16554
dc.identifier.urihttps://doi.org/10.3390/toxins14090617
dc.language.isoengde
dc.rights.licensecc_byde
dc.source2072-6651de
dc.sourceToxins; Vol. 14, No. 9 (2022) 617de
dc.subjectE-nose
dc.subjectFusarium graminearum
dc.subjectMycotoxin
dc.subjectMachine learning
dc.subjectSmall grains
dc.subjectDON
dc.subject.ddc660
dc.titleElectronic nose for the rapid detection of deoxynivalenol in wheat using classification and regression treesen
dc.type.diniArticle
dcterms.bibliographicCitationToxins, 14 (2022), 9, 617. https://doi.org/10.3390/toxins14090617. ISSN: 2072-6651
dcterms.bibliographicCitation.issn2072-6651
dcterms.bibliographicCitation.issue9
dcterms.bibliographicCitation.journaltitleToxins
dcterms.bibliographicCitation.volume14
local.export.bibtex@article{Camardo Leggieri2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16554}, doi = {10.3390/toxins14090617}, author = {Camardo Leggieri, Marco and Mazzoni, Marco and Bertuzzi, Terenzio et al.}, title = {Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees}, journal = {Toxins}, year = {2022}, volume = {14}, number = {9}, }
local.export.bibtexAuthorCamardo Leggieri, Marco and Mazzoni, Marco and Bertuzzi, Terenzio et al.
local.export.bibtexKeyCamardo Leggieri2022
local.export.bibtexType@article

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