Author | Kos, Gregor | dc.contributor.author |
Author | Sieger, Markus | dc.contributor.author |
Author | McMullin, David | dc.contributor.author |
Author | Zahradnik, Céline | dc.contributor.author |
Author | Sulyok, Michael | dc.contributor.author |
Author | Öner, Tuba | dc.contributor.author |
Author | Mizaikoff, Boris | dc.contributor.author |
Author | Krska, Rudolf | dc.contributor.author |
Date of accession | 2018-04-06T13:13:46Z | dc.date.accessioned |
Available in OPARU since | 2018-04-06T13:13:46Z | dc.date.available |
Date of first publication | 2016 | dc.date.issued |
Abstract | The 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 |
Language | en_US | dc.language.iso |
Publisher | Universität Ulm | dc.publisher |
License | Standard | dc.rights |
Link to license text | https://oparu.uni-ulm.de/xmlui/license_v3 | dc.rights.uri |
Keyword | MIR | dc.subject |
Keyword | Cereals | dc.subject |
Dewey Decimal Group | DDC 540 / Chemistry & allied sciences | dc.subject.ddc |
LCSH | Aflatoxins | dc.subject.lcsh |
LCSH | Infrared spectroscopy | dc.subject.lcsh |
LCSH | Corn; Contamination | dc.subject.lcsh |
LCSH | Peanuts; Contamination | dc.subject.lcsh |
LCSH | Chemometrics | dc.subject.lcsh |
LCSH | Trichothecenes | dc.subject.lcsh |
Title | A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits | dc.title |
Resource type | Wissenschaftlicher Artikel | dc.type |
Version | acceptedVersion | dc.description.version |
DOI | http://dx.doi.org/10.18725/OPARU-5883 | dc.identifier.doi |
URN | http://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-5940-4 | dc.identifier.urn |
Faculty | Fakultät für Naturwissenschaften | uulm.affiliationGeneral |
Institution | Institut für Analytische und Bioanalytische Chemie | uulm.affiliationSpecific |
Peer review | ja | uulm.peerReview |
DCMI Type | Text | uulm.typeDCMI |
Category | Publikationen | uulm.category |
Issue | | uulm.issue |
DOI of original publication | 10.1080/19440049.2016.1217567 | dc.relation1.doi |
Source - Title of source | Food additives & contaminants: Part A | source.title |
Source - Place of publication | Taylor & Francis | source.publisher |
Source - Volume | 33 | source.volume |
Source - Issue | 10 | source.issue |
Source - Year | 2016 | source.year |
Source - From page | 1596 | source.fromPage |
Source - To page | 1607 | source.toPage |
Source - ISSN | 0265-203X | source.identifier.issn |
EU project uulm | MYCOSPEC / Novel infrared spectroscopic tools for mycotoxin determination in foodstuffs for increased food safety / EC / FP7 / 314018 | uulm.projectEU |
Bibliography | uulm | uulm.bibliographie |