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

Authors :
Yong-Kyoung Kim
Insuck Baek
Kyung-Min Lee
Geonwoo Kim
Seyeon Kim
Sung-Youn Kim
Diane Chan
Timothy J. Herrman
Namkuk Kim
Moon S. Kim
Source :
Toxins, Vol 15, Iss 7, p 472 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
20726651
Volume :
15
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Toxins
Publication Type :
Academic Journal
Accession number :
edsdoj.35db8f27994c048822b516d50a5bf6
Document Type :
article
Full Text :
https://doi.org/10.3390/toxins15070472