Potential of near-infrared hyperspectral imaging in discriminating corn kernels infected with aflatoxigenic and non-aflatoxigenic Aspergillus flavus

Authors: TAO, FEIFEI - Mississippi State University; YAO, HAIBO - Mississippi State University; HRUSKA, ZUZANA - Mississippi State University; KINCAID, RUSSELL - Mississippi State University; Rajasekaran, Kanniah - Rajah; Bhatnagar, Deepak

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 4/30/2019
Publication Date: 4/30/2019
Citation: Tao, F., Yao, H., Hruska, Z., Kincaid, R., Rajasekaran, K., Bhatnagar, D. 2019. Potential of near-infrared hyperspectral imaging in discriminating corn kernels infected with aflatoxigenic and non-aflatoxigenic Aspergillus flavus. Proceedings of SPIE Vol. 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101603. https://doi.org/10.1117/12.2521654.
DOI: https://doi.org/10.1117/12.2521654

Interpretive Summary:

Technical Abstract: The potential of near infrared (NIR) hyperspectral imaging over the 900-2500 nm spectral range was examined for discrimination of corn kernels inoculated with aflatoxigenic and nontoxigenic fungus in this study. The two A. flavus strains, toxigenic AF13 and non-toxigneic AF36 were used for artificial inoculation on corn kernels. A total of 400 corn kernels were included, namely, 4 treatments of corn kernels with each treatment consisting of 100 kernels. Each treatment of 100 kernels were artificially inoculated with AF13 or AF36 fungus and incubated at 30 °C for 3 and 8 days, separately. The mean spectra were extracted from the collected NIR hyperspectral images for individual corn kernels, and then based on the mean spectra, the principal component analysis combined with linear discriminant analysis (PCA-LDA) method was employed to establish the classification models. The pairwise classification models were established by the PCA-LDA method to discriminate the AF36-inocualted and the AF13-inoculated kernels at different incubation days. All the overall accuracies obtained by the pairwise models were =98.0%. A common model which takes the AF13-inoculated kernels at different incubation days as one class and the AF36-inoculated kernels at different incubation days as the second class, achieved an overall accuracy of 99.0% for the prediction samples. This indicates a great potential of using NIR hyperspectral imaging to classify corn kernels infected by aflatoxigenic and nontoxigenic fungus, regardless of different infection times.