Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples

The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol presence in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided...

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Detalhes bibliográficos
Autores: Femenias, Antoni, Gatius Cortiella, Ferran, Ramos Girona, Antonio J., Sanchís Almenar, Vicente, Marín Sillué, Sònia
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Recursos:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/70135
Acesso em linha:https://doi.org/10.1016/j.foodchem.2020.128206
http://hdl.handle.net/10459.1/70135
Access Level:acceso abierto
Palavra-chave:Hyperspectral imaging
Deoxynivalenol
Ergosterol
Near infrared
Cereal analysis
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spelling Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samplesFemenias, AntoniGatius Cortiella, FerranRamos Girona, Antonio J.Sanchís Almenar, VicenteMarín Sillué, SòniaHyperspectral imagingDeoxynivalenolErgosterolNear infraredCereal analysisThe present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol presence in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, in which one that was analysed for ergosterol and the other another that was analysed for DON by HPLC. Thise method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The pPrediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the DON EU legal limit for DON.The 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.Elsevier2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.foodchem.2020.128206http://hdl.handle.net/10459.1/70135reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-87755-RVersió postprint del document publicat a: https://doi.org/10.1016/j.foodchem.2020.128206Food Chemistry, 2021, vol. 341, part 2, article 128206cc-by-nc-nd, (c) Elsevier, 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/esoai:repositori.udl.cat:10459.1/701352026-06-24T12:42:17Z
dc.title.none.fl_str_mv Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
title Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
spellingShingle Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
Femenias, Antoni
Hyperspectral imaging
Deoxynivalenol
Ergosterol
Near infrared
Cereal analysis
title_short Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
title_full Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
title_fullStr Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
title_full_unstemmed Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
title_sort Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples
dc.creator.none.fl_str_mv Femenias, Antoni
Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
Sanchís Almenar, Vicente
Marín Sillué, Sònia
author Femenias, Antoni
author_facet Femenias, Antoni
Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
Sanchís Almenar, Vicente
Marín Sillué, Sònia
author_role author
author2 Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
Sanchís Almenar, Vicente
Marín Sillué, Sònia
author2_role author
author
author
author
dc.subject.none.fl_str_mv Hyperspectral imaging
Deoxynivalenol
Ergosterol
Near infrared
Cereal analysis
topic Hyperspectral imaging
Deoxynivalenol
Ergosterol
Near infrared
Cereal analysis
description The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol presence in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, in which one that was analysed for ergosterol and the other another that was analysed for DON by HPLC. Thise method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The pPrediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the DON EU legal limit for DON.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.foodchem.2020.128206
http://hdl.handle.net/10459.1/70135
url https://doi.org/10.1016/j.foodchem.2020.128206
http://hdl.handle.net/10459.1/70135
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-87755-R
Versió postprint del document publicat a: https://doi.org/10.1016/j.foodchem.2020.128206
Food Chemistry, 2021, vol. 341, part 2, article 128206
dc.rights.none.fl_str_mv cc-by-nc-nd, (c) Elsevier, 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/es
rights_invalid_str_mv cc-by-nc-nd, (c) Elsevier, 2020
http://creativecommons.org/licenses/by-nc-nd/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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