Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.

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dc.contributorUNIVERSIDADE 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.
dc.creatorCONCEIÇÃO, R. R. P.
dc.creatorQUEIROZ, V. A. V.
dc.creatorMEDEIROS, E. P. de
dc.creatorARAUJO, J. B. de
dc.creatorSILVA, D. D. da
dc.creatorMIGUEL, R. de A.
dc.creatorSTOIANOFF, M. A. R.
dc.creatorSIMEONE, M. L. F.
dc.date2024-06-04T18:53:10Z
dc.date2024-06-04T18:53:10Z
dc.date2024-06-04
dc.date2024
dc.date.accessioned2026-07-07T05:26:29Z
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.identifierBrazilian Journal of Biology, v. 84, e277974, 2024.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1164667
dc.identifierhttps://doi.org/10.1590/1519-6984.277974
dc.identifier.urihttp://hdl.handle.net/123456789/491296
dc.languageeng
dc.rightsopenAccess
dc.subjectFumonisina
dc.subjectAnálise não-destrutiva
dc.subjectImagem hiperespectral
dc.subjectInfravermelho próximo
dc.subjectMínimos quadrados parciais
dc.subjectZea Mays
dc.subjectMicotoxina
dc.titleDetermination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.
dc.typeArtigo de periódico

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