Show simple item record

dc.contributor.authorFemenias, Antoni
dc.contributor.authorGatius Cortiella, Ferran
dc.contributor.authorRamos Girona, Antonio J.
dc.contributor.authorSanchís Almenar, Vicente
dc.contributor.authorMarín Sillué, Sònia
dc.date.accessioned2020-02-24T08:02:08Z
dc.date.available2020-12-23T23:15:20Z
dc.date.issued2019-12-23
dc.identifier.issn0956-7135
dc.identifier.urihttp://hdl.handle.net/10459.1/68087
dc.description.abstractNear infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace time-con- suming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively. The main objective of the present study was to standardise the HSI-NIR image acquisition method in naturally contaminated whole wheat kernels to obtain a high accuracy method to quantify and classify samples according to DON levels. To confirm the results, wheat samples were analysed by high performance liquid chromatography as the reference method to determine their DON levels. Hyperspectral images for single kernels and whole samples were obtained and spectral data were processed by multivariate analysis software. The initial work revealed that HSI-NIR was able to overcome kernel orientation, position and pixel selection. The subsequent developed Partial Least Squares (PLS) prediction achieved a RMSEP (Root Mean Square Error of Prediction) of 405 μg/kg and 1174 μg/kg for a cross-validated model and an independent set validated model, respectively. Moreover, the classification ac- curacy obtained by Linear Discriminant Analysis (LDA) was 62.7% for two categories depending on the EU maximum level (1250 μg/kg). Despite of the results are not accurate enough for DON quantification and sample classification, they can be considered a starting point for further improved protocols for DON management.
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/PN2013-2016/AGL2017-87755-R
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1016/j.foodcont.2019.107074
dc.relation.ispartofFood Control, 2020, vol. 111, article number 107074
dc.rightscc-by-nc-nd (c) Elsevier, 2019
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.subjectHyperspectral imaging
dc.subjectDeoxynivalenol
dc.subjectNear infrared
dc.subjectCereal sorting
dc.subjectContamination prediction
dc.titleStandardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2020-02-24T08:02:12Z
dc.identifier.idgrec029402
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1016/j.foodcont.2019.107074


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by-nc-nd (c) Elsevier, 2019
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Elsevier, 2019