Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Abstract

Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods

“…For example, Tyska et al (2021) quantified the contamination level of total fumonisins, i.e., B1 + B2 and zearalenone, in 200 unknown maize samples, and no significant difference was observed in predicted values using NIR and reference values obtained by Liquid Chromatographic Coupled to Tandem Mass Spectrometry (LC-MS/MS). Kim et al (2023) developed a short-wave infrared (SWIR) method for screening fumonisin-contaminated milled maize, considering the European legal limit. However, these applications used ground samples.…”

“…In a recent study, Kim et al (2023) proposed a method for classifying single contaminated and co-contaminated aflatoxin and fumonisin in ground maize samples by applying hyperspectral imaging techniques, including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions combined with machine learning algorithms. SWIR imaging with the support vector machine model resulted in higher classification accuracies compared to VNIR models.…”

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review

“…In a similar study, Ref. [ 96 ] used three different imaging methods alongside ML classification models to test ground corn samples for the presence of aflatoxin and fumonisin, both as individual contaminants and in combination. Two classification models were used, partial least squares-discriminant analysis (PLS-DA) and SVM, using specific threshold values for each mycotoxin.…”