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

Abstract 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.