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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-7790530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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