NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination

Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally...

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Autores: Borràs Vallverdú, Bernat, Marín Sillué, Sònia, Sanchís Almenar, Vicente, Gatius Cortiella, Ferran, Ramos Girona, Antonio J.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2024
País:España
Recursos:Universitat de Lleida (UdL)
Repositório:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/465348
Acesso em linha:https://doi.org/10.1002/jsfa.13388
https://hdl.handle.net/10459.1/465348
Access Level:Acceso aberto
Palavra-chave:Deoxynivalenol
Fumonisins
Maize
Mycotoxin
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spelling NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contaminationBorràs Vallverdú, BernatMarín Sillué, SòniaSanchís Almenar, VicenteGatius Cortiella, FerranRamos Girona, Antonio J.DeoxynivalenolFumonisinsMaizeMycotoxinMaize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method. RESULTS: In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB1 and FB2 in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg−1 and 2.344 (DON), 3.714 mg kg−1 and 2.018 (FB1) and 2.104 mg kg−1 and 2.301 (FB2). Regarding classification, European Union legal limits for DON and FB1 + FB2 were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB1 + FB2 classification model. CONCLUSION: NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.We are grateful to the Department of Crop and Forest Sciences of University of Lleida and to the IRTA's Sustainable Field Crops Program for providing maize samples. This work is part of the R + D + I project PID2020‐114836RB‐I00, financed by MCIN/AEI/10.13039/501100011033. B.B.‐V. has been funded by the FD predoctoral fellowship PRE2018‐085278 of the Spanish Ministry of Science, Innovation and Universities.John Wiley & Sons Ltd2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1002/jsfa.13388https://hdl.handle.net/10459.1/465348reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114836RB-I00Reproducció del document publicat a https://doi.org/10.1002/jsfa.13388Journal of the Science of Food and Agriculture, 2024, vol. 104, núm. 9, p. 5495-5503cc-by-nc (c) Borràs-Vallverdú et al., 2024Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/oai:repositori.udl.cat:10459.1/4653482026-06-24T12:42:17Z
dc.title.none.fl_str_mv NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
title NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
spellingShingle NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
Borràs Vallverdú, Bernat
Deoxynivalenol
Fumonisins
Maize
Mycotoxin
title_short NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
title_full NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
title_fullStr NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
title_full_unstemmed NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
title_sort NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination
dc.creator.none.fl_str_mv Borràs Vallverdú, Bernat
Marín Sillué, Sònia
Sanchís Almenar, Vicente
Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
author Borràs Vallverdú, Bernat
author_facet Borràs Vallverdú, Bernat
Marín Sillué, Sònia
Sanchís Almenar, Vicente
Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
author_role author
author2 Marín Sillué, Sònia
Sanchís Almenar, Vicente
Gatius Cortiella, Ferran
Ramos Girona, Antonio J.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Deoxynivalenol
Fumonisins
Maize
Mycotoxin
topic Deoxynivalenol
Fumonisins
Maize
Mycotoxin
description Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B1 (FB1) and B2 (FB2). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method. RESULTS: In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB1 and FB2 in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg−1 and 2.344 (DON), 3.714 mg kg−1 and 2.018 (FB1) and 2.104 mg kg−1 and 2.301 (FB2). Regarding classification, European Union legal limits for DON and FB1 + FB2 were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB1 + FB2 classification model. CONCLUSION: NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1002/jsfa.13388
https://hdl.handle.net/10459.1/465348
url https://doi.org/10.1002/jsfa.13388
https://hdl.handle.net/10459.1/465348
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 2017-2020/PID2020-114836RB-I00
Reproducció del document publicat a https://doi.org/10.1002/jsfa.13388
Journal of the Science of Food and Agriculture, 2024, vol. 104, núm. 9, p. 5495-5503
dc.rights.none.fl_str_mv cc-by-nc (c) Borràs-Vallverdú et al., 2024
Attribution-NonCommercial 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
rights_invalid_str_mv cc-by-nc (c) Borràs-Vallverdú et al., 2024
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
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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