Rapid and Nondestructive Method for Simultaneous Determination of Aflatoxigenic Fungus and Aflatoxin Contamination on Corn Kernels

Conventional methods for detecting aflatoxigenic fungus and aflatoxin contamination are generally time-consuming, sample-destructive, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening detection, real-time, and on-site analysis. Therefore, the potential of visible–near-infrared (Vis–NIR) spectroscopy over the 400–2500 nm spectral range was examined for determination of aflatoxigenic fungus infection and the corresponding aflatoxin contamination on corn kernels in a rapid and nondestructive manner. The two A. flavus strains, AF13 and AF38, were used to represent the aflatoxigenic fungus and nonaflatoxigenic fungus, respectively, for artificial inoculation on corn kernels. The partial least-squares discriminant analysis (PLS-DA) models based on different combinations of spectral range (I: 410–1070 nm; II: 1120–2470 nm), corn side (endosperm or germ side), spectral variable number (full spectra or selected variables), modeling approach (two-step or one-step), and classification threshold (20 or 100 ppb) were developed and their performances were compared. The first study focusing on detection of aflatoxigenic fungus-infected corn kernels showed that, in classifying the “control+AF38-inoculated” and AF13-inoculated corn kernels, the full spectral PLS-DA models using the preprocessed spectra over range II and one-step approach yielded more accurate prediction results than using the spectra over range I and the two-step approach. The advantage of the full spectral PLS-DA models established using one corn side than the other side were not consistent in the explored combination cases. The best full spectral PLS-DA model obtained was obtained using the germ-side spectra over range II with the one-step approach, which achieved an overall accuracy of 91.11%. The established CARS-PLSDA models performed better than the corresponding full-spectral PLS-DA models, with the better model achieved an overall accuracy of 97.78% in separating the AF13-inoculated corn kernels and the uninfected control and AF38-inoculated corn kernels. The second study focusing on the detection of aflatoxin-contaminated corn kernels showed that, based on the aflatoxin threshold of 20 and 100 ppb, the best overall accuracy in classifying the aflatoxin-contaminated and healthy corn kernels attained 86.67% and 84.44%, respectively, using the CARS-PLSDA models. The quantitative modeling results using partial least-squares regression (PLSR) obtained the correlation coefficient of prediction set (RP) of 0.91, which indicated the possibility of using Vis–NIR spectroscopy to quantify aflatoxin concentration in aflatoxigenic fungus-infected corn kernels.