A rapid and nondestructive method for simultaneous determination of aflatoxigenic fungus and aflatoxin contamination on corn kernels

Authors: TAO, FEIFEI - Mississippi State University; YAO, HAIBO - Mississippi State University; ZHU, FENGLE - Mississippi State University; HRUSKA, ZUZANA - Mississippi State University; Liu, Yongliang; Rajasekaran, Kanniah - Rajah; Bhatnagar, Deepak

Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2019
Publication Date: 4/15/2019
Citation: Tao, F., Yao, H., Zhu, F., Hruska, Z., Liu, Y., Rajasekaran, K., Bhatnagar, D. 2019. A rapid and nondestructive method for simultaneous determination of aflatoxigenic fungus and aflatoxin contamination on corn kernels. Journal of Agricultural and Food Chemistry. 67:5230-5239. https://doi.org/10.1021/acs.jafc.9b01044.
DOI: https://doi.org/10.1021/acs.jafc.9b01044

Interpretive Summary: 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 obtained classification results indicated that Vis/NIR spectroscopic technology combined with partial least squares discriminant analysis (PLS-DA) could be a useful tool for identifying corn kernels infected with aflatoxigenic fungus and/or contaminated with aflatoxins from sound kernels. The selected characteristic wavelengths further reduced the employed data dimensionality and simplified the complexity of computation. The correlation analysis between spectral absorbance and aflatoxin concentration indicated the usefulness of Vis/NIR spectroscopic technology in tracking aflatoxin production in aflatoxigenic fungus-infected corn kernels. These findings indicate that Vis/NIR spectroscopic technology could be an effective and practical approach for simultaneously detecting aflatoxigenic fungus and aflatoxin contamination of agricultural and food commodities, and thus reduce the risk of aflatoxins to ensure food and feed safety.

Technical Abstract: We utilized visible/near-infrared (Vis/NIR) spectroscopy over the 400-2500 nm spectral range to detect aflatoxigenic fungus infection and the corresponding aflatoxin contamination on corn kernels. A total of 180 corn kernels were used with 3 treatments included, namely, 60 kernels inoculated with an aflatoxigenic strain AF13, 60 kernels inoculated with nontoxigenic or atoxigenic strain AF38 , and 60 kernels inoculated with sterile distilled water served as negative control. After 8 days of incubation, the absorbance spectra acquired in reflectance mode were collected from the endosperm and germ sides of corn, separately, and the aflatoxin concentration of each corn kernel was determined using a reference method. The partial least squares discriminant analysis (PLS-DA) models based on different combinations of spectral range (410-1070 or 1120-2470 nm), corn side (endosperm side or germ side), spectral variable number (full spectra or selected variables), modeling approach (two-step or one-step) and classification threshold (20 ppb or 100 ppb) were all 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 simplified PLS-DA models established using the selected spectral variables by the competitive adaptive reweighted sampling (CARS) algorithm obtained comparable classification results to the full-spectral PLS-DA models. Generally, the models established using the endosperm-side spectra over the 1120-2470 nm performed the best among all combination cases, in identifying the AF13-inocualted corn kernels. The best overall accuracy achieved was 100.0% in classifying the fungus-infected and uninfected control corn kernels, 93.3% in classifying the AF38- and AF13-inoculated corn kernels, and 95.6% in classifying the “control+AF38-inoculated” and AF13-inoculated corn kernels. The second study focused on the detection of aflatoxin-contaminated corn kernels. 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 84.4% and 88.9%, respectively. The quantitative modeling results using partial least squares regression (PLSR) obtained the correlation coefficient of cross validation (RCV) of 0.85, indicating the potential of using Vis/NIR spectroscopy to quantify aflatoxin concentration of aflatoxigenic fungus-infected corn kernels.