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Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms
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Orientador(es)
Resumo(s)
Fumonisins occurrence in maize represents a significant global challenge, impacting economic stability and food
safety. This study evaluates the potential of near-infrared (NIR) spectroscopy combined with chemometric al-
gorithms to detect fumonisins in maize. For fumonisin B1 (FB1) and B2 (FB2) levels were developed predictive
NIR models using partial least squares (PLS) and artificial neural networks (ANN). PLS models demonstrated
strong correlation coefficient (R2) values of 0.90 (FB1), 0.98 (FB2), and 0.91 (FB1 + FB2) for calibration, with
ratio of prediction to deviation (RPD) values ranging 2.8–3.6. Similarly, ANN models showed good predictive
performance, particularly for FB1 + FB2, with R = 0.99, and the root means square error (RMSE) of 131 μg/kg
for calibration; and R = 0.95, RMSE = 656 μg/kg for validation.
These findings underscore the efficacy of NIR spectroscopy as a rapid, non-destructive tool for fumonisin
screening in maize, with chemometric algorithms enhancing model accuracy, offering a valuable method for
ensuring food safety.
Descrição
Palavras-chave
Maize Fumonisin B1 Fumonisin B2 Predictive models NIR spectroscopy Chemometrics Artificial neural network
Contexto Educativo
Citação
Editora
Elsevier