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AuthorKos, Gregordc.contributor.author
AuthorSieger, Markusdc.contributor.author
AuthorMcMullin, Daviddc.contributor.author
AuthorZahradnik, Célinedc.contributor.author
AuthorSulyok, Michaeldc.contributor.author
AuthorÖner, Tubadc.contributor.author
AuthorMizaikoff, Borisdc.contributor.author
AuthorKrska, Rudolfdc.contributor.author
Date of accession2018-04-06T13:13:46Zdc.date.accessioned
Available in OPARU since2018-04-06T13:13:46Zdc.date.available
Date of first publication2016dc.date.issued
AbstractThe rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B1 in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1 750 μg kg –1 and, for the first time, 77% of 92 peanut samples at 8 μg kg –1 of aflatoxin B1. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 μg kg –1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordMIRdc.subject
KeywordCerealsdc.subject
Dewey Decimal GroupDDC 540 / Chemistry & allied sciencesdc.subject.ddc
LCSHAflatoxinsdc.subject.lcsh
LCSHInfrared spectroscopydc.subject.lcsh
LCSHCorn; Contaminationdc.subject.lcsh
LCSHPeanuts; Contaminationdc.subject.lcsh
LCSHChemometricsdc.subject.lcsh
LCSHTrichothecenesdc.subject.lcsh
TitleA novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limitsdc.title
Resource typeWissenschaftlicher Artikeldc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-5883dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-5940-4dc.identifier.urn
FacultyFakultät für Naturwissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Analytische und Bioanalytische Chemieuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
Issueuulm.issue
DOI of original publication10.1080/19440049.2016.1217567dc.relation1.doi
Source - Title of sourceFood additives & contaminants: Part Asource.title
Source - Place of publicationTaylor & Francissource.publisher
Source - Volume33source.volume
Source - Issue10source.issue
Source - Year2016source.year
Source - From page1596source.fromPage
Source - To page1607source.toPage
Source - ISSN0265-203Xsource.identifier.issn
EU projectMYCOSPEC / Novel infrared spectroscopic tools for mycotoxin determination in foodstuffs for increased food safety / EC / FP7 / 314018uulm.projectEU


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