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dc.contributor.authorCONCEIÇÃO, R. R. P.
dc.contributor.authorQUEIROZ, V. A. V.
dc.contributor.authorMEDEIROS, E. P. de
dc.contributor.authorARAUJO, J. B. de
dc.contributor.authorSILVA, D. D. da
dc.contributor.authorMIGUEL, R. de A.
dc.contributor.authorSTOIANOFF, M. A. R.
dc.contributor.authorSIMEONE, M. L. F.
dc.date.accessioned2024-06-04T18:53:10Z-
dc.date.available2024-06-04T18:53:10Z-
dc.date.created2024-06-04
dc.date.issued2024
dc.identifier.citationBrazilian Journal of Biology, v. 84, e277974, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1164667-
dc.descriptionMaize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectFumonisina
dc.subjectAnálise não-destrutiva
dc.subjectImagem hiperespectral
dc.subjectInfravermelho próximo
dc.subjectMínimos quadrados parciais
dc.titleDetermination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.
dc.typeArtigo de periódico
dc.subject.thesagroZea Mays
dc.subject.thesagroMicotoxina
riaa.ainfo.id1164667
riaa.ainfo.lastupdate2024-06-04
dc.identifier.doihttps://doi.org/10.1590/1519-6984.277974
dc.contributor.institutionUNIVERSIDADE FEDERAL DE MINAS GERAIS; VALERIA APARECIDA VIEIRA QUEIROZ, CNPMS; EVERALDO PAULO DE MEDEIROS, CNPA; JOABSON BORGES DE ARAUJO, CNPA; DAGMA DIONISIA DA SILVA ARAUJO, CNPMS; RAFAEL DE ARAUJO MIGUEL, CNPMS; UNIVERSIDADE FEDERAL DE MINAS GERAIS; MARIA LUCIA FERREIRA SIMEONE, CNPMS.
Aparece en las colecciones:Artigo em periódico indexado (CNPMS)

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