|KIM, YONGKYOUNG - Us Forest Service (FS)|
|BAEK, INSUCK - Orise Fellow|
|LEE, KYUNG-MIN - Texas A&M University|
|Qin, Jianwei - Tony Qin|
|KIM, GEONWOO - Us Forest Service (FS)|
|SHIN, BYEUNG - National Academy Of Agricultural Science|
|HERMAN, TIMOTHY - Texas A&M University|
|CHO, SOON-KIL - National Academy Of Agricultural Science|
Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/9/2021
Publication Date: 8/10/2021
Citation: Kim, Y., Baek, I., Lee, K., Qin, J., Kim, G., Shin, B.K., Chan, D.E., Herman, T.J., Cho, S., Kim, M.S. 2021. Investigation of reflectance, fluorescence, and Raman hyperspectral imaging techniques for rapid detection of aflatoxins in ground maize. Food Control. 132:108479. https://doi.org/10.1016/j.foodcont.2021.108479.
Interpretive Summary: Food crops such as maize, peanuts, and tree nuts can be contaminated with aflatoxin, which is produced by certain fungi and is considered a carcinogen. Many countries have established maximum allowable limits for the presence of aflatoxin in foods and thus there is a great interest worldwide in developing rapid and nondestructive methods to screen high volumes of food products for aflaxtoxin contamination. This study investigated the development of classification models to use with four hyperspectral imaging methods—fluorescence by ultraviolet excitation, visible/near-infrared reflectance, short-wave infrared reflectance, and Raman—to detection naturally-occurring levels of aflatoxin contamination in samples of ground maize. The results demonstrated that effective classification models were possible with all four hyperspectral imaging methods, indicating great promise for the development of non-destructive imaging methods that can be used by regulatory agencies and processors for high volume sample screening of ground maize and other products that are vulnerable to contamination.
Technical Abstract: Aflatoxins, commonly found in corn and corn-derived products, can cause severe illness in animals and humans if consumed in significant amounts. Early detection is critical to preventing illness, but the most sensitive and effective of commonly used screening tools for aflatoxins are expensive and cumbersome methods based on chromatography or imunoassays that require technical expertise to perform. Multiple hyperspectral imaging techniques, including reflectance in the visible and near-infrared (VNIR) region and short-wave infrared (SWIR) region, fluorescence by 365 nm ultraviolet (UV) excitation, and Raman by 785 nm laser excitation, were used for detection of aflatoxin in ground maize. Four classification models based on linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines (QSVM) algorithms were developed for classification with each hyperspectral imaging mode. The multivariate classification models in combination with different preprocessing methods were applied for screening of maize samples naturally contaminated with aflatoxin. The classification accuracies for fluorescence with QSVM, VNIR with QSVM, SWIR with LSVM, and Raman with LSVM were 95.7%, 82.6%, 95.7%, and 87.0%, respectively, with no false-negative error at the cutoff of 10 µg/kg. The SWIR and fluorescence models showed slightly higher performance accuracies, suggesting that they may be more effective and efficient analytical tools for aflatoxin analysis in maize compared to conventional wet-chemical methods. These methods show promise as inexpensive, and easy-to-use screening tools for food safety, to rapidly detect aflatoxins in maize or other food ingredients intended for animal or human consumption.