Near-Infrared Spectroscopy for Predicting Fumonisin and Deoxynivalenol in Maize: Development of Preliminary Chemometric Models

Fumonisins and deoxynivalenol (DON) are toxic secondary metabolites, produced by Fusarium species frequently contaminating maize, representing a critical challenge to food safety and human health. Conventional analytical methods, such as HPLC and ELISA, are accurate but time-consuming and require complex sample preparation. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive, and cost-effective alternative for mycotoxin screening. This study investigates the potential of NIR spectroscopy combined with chemometric techniques to detect and quantify fumonisins (primarily FB1 and FB2) and DON in maize. A total of 60 maize samples were analyzed with mean concentrations of 534 µg/kg for FB1, 208 µg/kg for FB2, and 130 µg/kg for DON. The highest cumulative contamination of FB1 + FB2 reached 3420 µg/kg. While 30% of the samples showed no detectable fumonisin contamination, DON was absent in 17% of the samples. The best performing predictive models were developed using second-derivative pre-processing of the NIR spectra. The NIR calibration model yielded coefficients of determination (R2) of 0.91 for FB1, 0.88 for FB2, and 0.92 for DON, with corresponding root mean square errors (RMSE) of 683, 282, and 115 µg/kg, respectively. These results demonstrate that NIR spectroscopy, particularly when integrated with multivariate analysis, is a promising tool for distinguishing contaminated from uncontaminated maize and estimating mycotoxin levels with reasonable accuracy. These findings support the application of NIR as a practical tool for routine screening and quality control in the maize supply chain.