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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.