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 that frequently contaminate maize, representing a critical challenge for 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 to 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 (RMSEs) 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 maize 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.