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dc.contributor.authorFemenias, Antoni
dc.contributor.authorBainotti, Maria Belén
dc.contributor.authorGatius Cortiella, Ferran
dc.contributor.authorRamos Girona, Antonio J.
dc.contributor.authorMarín Sillué, Sònia
dc.date.accessioned2020-12-22T10:52:08Z
dc.date.issued2020-11-28
dc.identifier.issn0963-9969
dc.identifier.urihttp://hdl.handle.net/10459.1/70136
dc.description.abstractThe spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. , and to replace commonly used time-consuming and destructive techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R2 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100 % of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4 % of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.
dc.description.sponsorshipThe authors are grateful to the University of Lleida (predoctoral grant), and to the Spanish Ministry of Science, Innovation and Universities (Project AGL2017-87755-R) for funding this work.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relationMINECO/PN2017-2020/AGL2017-87755-R
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1016/j.foodres.2020.109925
dc.relation.ispartofFood Research International, 2021, vol. 139, article 109925
dc.rightscc-by-nc-nd, (c) Elsevier, 2020
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.subjectHyperspectral imaging
dc.subjectDeoxynivalenol
dc.subjectSingle kernel
dc.subjectNear infrared
dc.subjectCereal sorting
dc.titleStandardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2020-12-22T10:52:08Z
dc.identifier.idgrec030713
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess
dc.identifier.doihttps://doi.org/10.1016/j.foodres.2020.109925
dc.date.embargoEndDate2021-11-28


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cc-by-nc-nd, (c) Elsevier, 2020
Except where otherwise noted, this item's license is described as cc-by-nc-nd, (c) Elsevier, 2020