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Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared S...

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Autores principales: Tyska, Denize, Mallmann, Adriano Olnei, Vidal, Juliano Kobs, de Almeida, Carlos Alberto Araújo, Gressler, Luciane Tourem, Mallmann, Carlos Augusto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790530/
https://www.ncbi.nlm.nih.gov/pubmed/33412558
http://dx.doi.org/10.1371/journal.pone.0244957
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author Tyska, Denize
Mallmann, Adriano Olnei
Vidal, Juliano Kobs
de Almeida, Carlos Alberto Araújo
Gressler, Luciane Tourem
Mallmann, Carlos Augusto
author_facet Tyska, Denize
Mallmann, Adriano Olnei
Vidal, Juliano Kobs
de Almeida, Carlos Alberto Araújo
Gressler, Luciane Tourem
Mallmann, Carlos Augusto
author_sort Tyska, Denize
collection PubMed
description Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B(1)+fumonisin B(2) (FB(1)+FB(2)), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB(1), one with 10 PCs for FB(2), and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R(2)), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB(1)+FB(2) and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.
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spelling pubmed-77905302021-01-27 Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR) Tyska, Denize Mallmann, Adriano Olnei Vidal, Juliano Kobs de Almeida, Carlos Alberto Araújo Gressler, Luciane Tourem Mallmann, Carlos Augusto PLoS One Research Article Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B(1)+fumonisin B(2) (FB(1)+FB(2)), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB(1), one with 10 PCs for FB(2), and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R(2)), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB(1)+FB(2) and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins. Public Library of Science 2021-01-07 /pmc/articles/PMC7790530/ /pubmed/33412558 http://dx.doi.org/10.1371/journal.pone.0244957 Text en © 2021 Tyska et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tyska, Denize
Mallmann, Adriano Olnei
Vidal, Juliano Kobs
de Almeida, Carlos Alberto Araújo
Gressler, Luciane Tourem
Mallmann, Carlos Augusto
Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title_full Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title_fullStr Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title_full_unstemmed Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title_short Multivariate method for prediction of fumonisins B(1) and B(2) and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)
title_sort multivariate method for prediction of fumonisins b(1) and b(2) and zearalenone in brazilian maize using near infrared spectroscopy (nir)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790530/
https://www.ncbi.nlm.nih.gov/pubmed/33412558
http://dx.doi.org/10.1371/journal.pone.0244957
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