Autoria: CONCEIÇÃO, R. R. P.; QUEIROZ, V. A. V.; MEDEIROS, E. P. de; ARAUJO, J. B. de; SILVA, D. D. da; MIGUEL, R. de A.; STOIANOFF, M. A. R.; SIMEONE, M. L. F.

Resumo: 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.

Ano de publicação: 2024

Tipo de publicação: Artigo de periódico

Unidade: Embrapa Milho e Sorgo

Palavras-chave: Análise não-destrutiva, Fumonisina, Imagem hiperespectral, Infravermelho próximo, Micotoxina, Mínimos quadrados parciais, Zea Mays