A rapid and non-destructive method for simultaneous determination of aflatoxigenic fungus and aflatoxin contamination on corn kernels.

08:00 EDT 15th April 2019 | BioPortfolio

Summary of "A rapid and non-destructive 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 non-destructive 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 non-destructive manner. The two A. flavus strains, AF13 and AF38 were used to represent the aflatoxigenic fungus and non-aflatoxigenic fungus, respectively, for artificial inoculation on corn kernels. The partial least squares discriminant analysis (PLS-DA) models based on different combinations of spectral range (
410-1070 nm;
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 performance was 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 was 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 ppb 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 (R) of 0.91, indicating the possibility of using Vis-NIR spectroscopy to quantify aflatoxin concentration in aflatoxigenic fungus-infected corn kernels.


Journal Details

This article was published in the following journal.

Name: Journal of agricultural and food chemistry
ISSN: 1520-5118


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