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A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

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peer-reviewed

Erstveröffentlichung
2016
Authors
Kos, Gregor
Sieger, Markus
McMullin, David
Zahradnik, Céline
Sulyok, Michael
et al.
Wissenschaftlicher Artikel


Published in
Food additives & contaminants: Part A ; 33 (2016), 10. - S. 1596-1607. - ISSN 0265-203X
Link to original publication
https://dx.doi.org/10.1080/19440049.2016.1217567
Faculties
Fakultät für Naturwissenschaften
Institutions
Institut für Analytische und Bioanalytische Chemie
Document version
accepted version
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.
EU Project uulm
MYCOSPEC / Novel infrared spectroscopic tools for mycotoxin determination in foodstuffs for increased food safety / EC / FP7 / 314018
Subject headings
[LCSH]: Aflatoxins | Infrared spectroscopy | Corn; Contamination | Peanuts; Contamination | Chemometrics | Trichothecenes
[Free subject headings]: MIR | Cereals
[DDC subject group]: DDC 540 / Chemistry & allied sciences
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Standard
https://oparu.uni-ulm.de/xmlui/license_v3

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DOI & citation

Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-5883

Kos, Gregor et al. (2018): A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-5883
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