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AutorKos, Gregordc.contributor.author
AutorSieger, Markusdc.contributor.author
AutorMcMullin, Daviddc.contributor.author
AutorZahradnik, Célinedc.contributor.author
AutorSulyok, Michaeldc.contributor.author
AutorÖner, Tubadc.contributor.author
AutorMizaikoff, Borisdc.contributor.author
AutorKrska, Rudolfdc.contributor.author
Aufnahmedatum2018-04-06T13:13:46Zdc.date.accessioned
In OPARU verfügbar seit2018-04-06T13:13:46Zdc.date.available
Datum der Erstveröffentlichung2016dc.date.issued
ZusammenfassungThe 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
Spracheen_USdc.language.iso
Verbreitende StelleUniversität Ulmdc.publisher
LizenzStandarddc.rights
Link zum Lizenztexthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
SchlagwortMIRdc.subject
SchlagwortCerealsdc.subject
DDC-SachgruppeDDC 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
TitelA novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limitsdc.title
RessourcentypWissenschaftlicher Artikeldc.type
Version des DokumentsacceptedVersiondc.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
FakultätFakultät für Naturwissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Analytische und Bioanalytische Chemieuulm.affiliationSpecific
Peer-Reviewjauulm.peerReview
DCMI MedientypTextuulm.typeDCMI
KategoriePublikationenuulm.category
Heftuulm.issue
DOI der Originalpublikation10.1080/19440049.2016.1217567dc.relation1.doi
Quellenangabe - Titel der QuelleFood additives & contaminants: Part Asource.title
Quellenangabe - VerlagTaylor & Francissource.publisher
Quellenangabe - Band33source.volume
Quellenangabe - Heft10source.issue
Quellenangabe - Jahr2016source.year
Quellenangabe - von Seite1596source.fromPage
Quellenangabe - bis Seite1607source.toPage
Quellenangabe - ISSN0265-203Xsource.identifier.issn
EU-ProjektMYCOSPEC / Novel infrared spectroscopic tools for mycotoxin determination in foodstuffs for increased food safety / EC / FP7 / 314018uulm.projectEU


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