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Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.
Conceição, R R P; Queiroz, V A V; Medeiros, E P; Araújo, J B; Araújo, D D S; Miguel, R A; Stoianoff, M A R; Simeone, M L F.
Afiliación
  • Conceição RRP; Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, MG, Brasil.
  • Queiroz VAV; Embrapa Milho e Sorgo, Sete Lagoas, MG, Brasil.
  • Medeiros EP; Embrapa Algodão, Campina Grande, PB, Brasil.
  • Araújo JB; Embrapa Algodão, Campina Grande, PB, Brasil.
  • Araújo DDS; Embrapa Milho e Sorgo, Sete Lagoas, MG, Brasil.
  • Miguel RA; Embrapa Milho e Sorgo, Sete Lagoas, MG, Brasil.
  • Stoianoff MAR; Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, MG, Brasil.
  • Simeone MLF; Embrapa Milho e Sorgo, Sete Lagoas, MG, Brasil.
Braz J Biol ; 84: e277974, 2024.
Article en En | MEDLINE | ID: mdl-38808784
ABSTRACT
Maize (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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectroscopía Infrarroja Corta / Zea mays / Fumonisinas / Imágenes Hiperespectrales Idioma: En Revista: Braz J Biol Asunto de la revista: BIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Brasil
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectroscopía Infrarroja Corta / Zea mays / Fumonisinas / Imágenes Hiperespectrales Idioma: En Revista: Braz J Biol Asunto de la revista: BIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Brasil