Raman imaging for detection of corn kernels infected with Aspergillus flavus: a preliminary study

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: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 5/18/2020
Publication Date: 5/18/2020
Citation: Tao, F., Yao, H., Hruska, Z., Rajasekaran, K., Qin, J., Kim, M. 2020. Raman imaging for detection of corn kernels infected with Aspergillus flavus: a preliminary study. Proceedings of SPIE., Vol. 11421, Sensing for Agriculture and Food Quality and Safety XII. 1142107. https://doi.org/10.1117/12.2558859.
DOI: https://doi.org/10.1117/12.2558859

Interpretive Summary:

Technical Abstract: The potential of line-scan Raman imaging system equipped with a 785 nm laser line was examined for discrimination of healthy, AF36-inoculated and AF13-inoculated corn kernels in this study. The AF36 and AF13 strains were used as representatives for the aflatoxigenic and non-aflatoxigenic Aspergillus 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 all incubated at 30 °C for 8 days and then dried and surface wiped to remove exterior signs of mold. The kernels were imaged from both endosperm and germ sides over the shift range of -678-2831 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 35 and 54 local Raman peaks were identified from the endosperm and germ sides, 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 (PCA-LDA) models ran 20 random times, achieved mean overall prediction accuracies of 91.13% and 75.00% using the endosperm- and germ-side data, respectively. The corresponding standard deviations were 3.36% and 4.12%. The results demonstrate the usefulness of the line-scan Raman imaging technology in classifying healthy corn kernels and corn kernels infected with aflatoxigenic and non-aflatoxigenic fungus.