Location: Food and Feed Safety ResearchTitle: Feasibility of using visible/near-infrared (Vis/NIR) spectroscopy to detect aflatoxigenic fungus and aflatoxin contamination on corn kernels
|TAO, FEIFEI - Mississippi State University|
|YAO, HAIBO - Mississippi State University|
|ZHU, FENGLE - Mississippi State University|
|HRUSKA, ZUZANA - Mississippi State University|
|LIU, YONGLIANG - Mississippi State University|
|Rajasekaran, Kanniah - Rajah|
Submitted to: American Society of Agricultural and Biological Engineers
Publication Type: Proceedings
Publication Acceptance Date: 8/12/2018
Publication Date: 8/12/2018
Citation: Tao, F., Yao, H., Zhu, F., Hruska, Z., Liu, Y., Rajasekaran, K., Bhatnagar, D. 2018. Feasibility of using visible/near-infrared (Vis/NIR) spectroscopy to detect aflatoxigenic fungus and aflatoxin contamination on corn kernels. Proceedings of the 2018 American Society of Agricultural and Biological Engineers Annual International Meeting. Paper No. 1801006. https://doi.org/10.13031/aim.201801006.
Technical Abstract: The demand for developing a rapid and non-destructive method for sensing aflatoxin contamination and/or aflatoxigenic fungal infection that is suitable to real-time and on-line detection has received significant attentions. In this study, we utilized the visible/near-infrared (Vis/NIR) spectroscopy over the spectral range of 400-2500 nm to detect AF13-inoculated corn kernels and the corresponding aflatoxin contamination. A total of 180 corn kernels were used with 3 treatments included, namely, 60 kernels inoculated with AF13 (aflatoxigenic) strain, 60 kernels inoculated with AF38 (non-aflatoxigenic) strain, and 60 kernels inoculated with sterile distilled water as control. Both chemometric methods of partial least squares discriminant analysis (PLS-DA) and principal component analysis combined with quadratic discriminant analysis (PCA-QDA) were employed to develop the classification models. The obtained results indicated the potential of Vis/NIR spectroscopy combined with appropriate chemometric methods in differentiating the AF13-inoculated corn kernels from the AF38-inoculated and control kernels, and identifying the corresponding aflatoxin contamination. The best overall accuracy in classifying the AF13-inoculated and “control+AF38-inoculated” corn kernels achieved 91.1% using the PLS-DA method. Specifically, the accuracy attained in identifying the AF13-inoculated corn kernels was 100.0% using this model. The best overall accuracy obtained in classifying the healthy and aflatoxin-contaminated corn kernels was over 82.0%, using either the threshold of 20 ppb or 100 ppb. The best accuracy in identifying the aflatoxin-contaminated corn kernels achieved 90.9% and 88.9%, with the classification threshold of 20 ppb and 100 ppb, respectively.