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The potential of near infrared hyperspectral imaging over the spectral range of 900 - 2500 nm was investigated for identification of aflatoxin contamination on corn kernels. A total of 600 kernels were used with 3 treatments, namely, 200 kernels inoculated with the AF13 fungus (aflatoxigenic), 200 kernels inoculated with the AF36 fungus (nonaflatoxigenic), and 200 kernels inoculated with sterile distilled water as control. One hundred kernels from each treatment were incubated at 30 °C for 5 and 8 days, separately, and then the kernels were dried and surface wiped to remove exterior signs of mold prior to imaging. The mean spectra including mean reflectance and absorbance, and the textural features consisting of contrast, correlation, energy and homogeneity, were extracted separately from the endosperm regions of single kernels. The partial least-squares discriminant analysis (PLS-DA) models were established using extracted mean spectra or textural features as individual inputs. The full spectral PLS-DA modeling results indicate that the mean spectra including both reflectance and absorbance spectra performed significantly better than using the textural features in identifying aflatoxin contamination on corn kernels. Using the mean reflectance and absorbance spectra between 925 and 2484 nm, the full spectral PLS-DA models achieved mean overall prediction accuracies of 88.3% and 86.3% when taking 20 ppb as the classification threshold. The corresponding means of overall prediction accuracies were 85.5% and 85.6% when 100 ppb was applied as the classification threshold. The extracted textural features were not found to be useful in identifying aflatoxin contamination.
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