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

Feifei Tao; Haibo Yao; Zuzana Hruska; Russell Kincaid; Kanniah Rajasekaran; Deepak Bhatnagar

doi:10.1117/12.2521654

30 April 2019 Potential of near-infrared hyperspectral imaging in discriminating corn kernels infected with aflatoxigenic and non-aflatoxigenic Aspergillus flavus

Feifei Tao, Haibo Yao, Zuzana Hruska, Russell Kincaid, Kanniah Rajasekaran, Deepak Bhatnagar

Author Affiliations +

Proceedings Volume 11016, Sensing for Agriculture and Food Quality and Safety XI; 1101603 (2019) https://doi.org/10.1117/12.2521654
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

The potential of near infrared (NIR) hyperspectral imaging over the 900-2500 nm spectral range was examined for discrimination of artificially-inoculated corn kernels with aflatoxigenic and non-aflatoxigenic strains of Aspergillus flavus in this study. The two A. flavus strains, aflatoxigenic AF13 and non-aflatoxigenic AF36 were used for inoculation on corn kernels. Four treatments were included, with each treatment consisting of 100 kernels. Each treatment of 100 kernels were artificially inoculated with AF13 or AF36 strain 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-inoculated and the AF13-inoculated kernels at different incubation days. All the overall accuracies obtained by the pairwise models were ≥98.0%. A common model that 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 non-aflatoxigenic A. flavus regardless of infection time.

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Feifei Tao, Haibo Yao, Zuzana Hruska, Russell Kincaid, Kanniah Rajasekaran, and Deepak Bhatnagar "Potential of near-infrared hyperspectral imaging in discriminating corn kernels infected with aflatoxigenic and non-aflatoxigenic Aspergillus flavus", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101603 (30 April 2019); https://doi.org/10.1117/12.2521654

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