Use of line-scan Raman hyperspectral imaging to identify corn kernels infected with Aspergillus flavus

Authors: TAO, FEIFEI - Mississippi State University; YAO, HAIBO - Mississippi State University; HRUSKA, ZUZANA - Mississippi State University; Rajasekaran, Kanniah - Rajah; Qin, Jianwei - Tony Qin; Kim, Moon

Submitted to: Journal of Cereal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/28/2021
Publication Date: 10/30/2021
Citation: Tao, F., Yao, H., Hruska, Z., Rajasekaran, K., Qin, J., Kim, M.S. 2021. Use of line-scan Raman hyperspectral imaging to identify corn kernels infected with Aspergillus flavus. Journal of Cereal Science. 102:103364. https://doi.org/10.1016/j.jcs.2021.103364.
DOI: https://doi.org/10.1016/j.jcs.2021.103364

Interpretive Summary: Aflatoxins are potent toxins produced by Aspergillus flavus and contamination in corn poses severe problems for the corn industry and public health. This collaborative research explored the use of Raman imaging technology, utilizing a 785 nm line laser, for non-invasive identification of corn kernels infected with aflatoxin producing mold. Raman spectroscopy is a non-destructive chemical analysis technique which provides detailed information about the chemical structure and behavior of molecular components within a given sample. The analysis is based on the interaction of light with the chemical bonds within a material, producing characteristic peaks. Nine hundred corn kernels after fungal treatments were analyzed using Raman imaging and different prediction models, which resulted in a prediction accuracy of 89.50%. These results demonstrate that Raman imaging technology is a useful, non-invasive method for identifying Aspergillus flavus infection in corn. This technology may further assist with the development of effective tools for the removal of aflatoxins from contaminated corn, and thus benefiting corn growers, processors, and consumers.

Technical Abstract: The potential of line-scan Raman hyperspectral imaging system equipped with a 785 nm line laser was examined for discrimination of healthy, AF36-inoculated and AF13-inoculated corn kernels in this study. The AF13 and AF36 strains were used as representatives of the aflatoxigenic and non-aflatoxigenic A. flavus fungal varieties. A total of 300 kernels were used with 3 treatments, namely, 100 kernels inoculated with the AF13 fungus, 100 kernels inoculated with the AF36 fungus, and 100 kernels inoculated with sterile distilled water as control. The kernels were incubated at 30 °C for 8 days, dried and surface wiped to remove exterior signs of mold. The kernels were imaged on both endosperm and germ sides over the wavenumber range of 103-2831 cm-1. The mean spectrum was extracted from the Raman image of each kernel, and preprocessed with adaptive iteratively reweighted penalized least squares, Savitzky-Golay smoothing and min-max normalization. Based upon the preprocessed group mean spectra, a total of 36 and 51 local Raman peaks were identified from the endosperm side and embryo area of germ side, respectively. With the spectral variables at the identified local peak locations as inputs of discriminant models, the 3-class principal component analysis-linear discriminant analysis models ran 50 random times, achieved mean overall prediction accuracies of 89.47% and 75.55% using the endosperm and embryo data, respectively. The corresponding standard deviations were 3.48% and 4.34%. The results demonstrate usefulness of the line-scan Raman imaging technology in differentiating healthy corn kernels and corn kernels infected with aflatoxigenic and non-aflatoxigenic fungi.