NIR-HSI as a tool to predict deoxynivalenol and fumonisins in maize kernels: a step forward in preventing mycotoxin contamination.

Background: Maize is frequently contaminated with deoxynivalenol (DON) and fumonisins B ₁ (FB ₁ ) and B ₂ (FB ₂ ). In the European Union, these mycotoxins are regulated in maize and maize-derived products. To comply with these regulations, industries require a fast, economic, safe, non-destructive and environmentally friendly analysis method.
Results: In the present study, near-infrared hyperspectral imaging (NIR-HSI) was used to develop regression and classification models for DON, FB ₁ and FB ₂ in maize kernels. The best regression models presented the following root mean square error of cross validation and ratio of performance to deviation values: 0.848 mg kg ^-1 and 2.344 (DON), 3.714 mg kg ^-1 and 2.018 (FB ₁ ) and 2.104 mg kg ^-1 and 2.301 (FB ₂ ). Regarding classification, European Union legal limits for DON and FB ₁  + FB ₂ were selected as thresholds to classify maize kernels as acceptable or not. The sensitivity and specificity were 0.778 and 1 for the best DON classification model and 0.607 and 0.938 for the best FB ₁  + FB ₂ classification model.
Conclusion: NIR-HSI can help reduce DON and fumonisins contamination in the maize food and feed chain. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
(© 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)