Non-destructive identification of the fungal disease has great importance as one of the challenges of the citrus post-harvest industry. In order to identify the fungal disease of Alternaria alternata, which occurred internally, the reflectance spectra were collected in 3 areas of the stem-end, equatorial and stylar-end of healthy and fungus-infected oranges using VIS-NIR spectrometer (400–1100 nm). Principal Component Analysis models were extracted from the preprocessed wavelengths after performing various preprocessors such as Savitzky-Gulay, standard normal variate and mean normalization. The classification capability of support vector machine and back-propagation neural network classifiers was evaluated in identifying appropriate preprocessors from the obtained principal components and identifying infected fruits using optimal wavelengths. The highest accuracy of the classification was obtained by the back-propagation neural network classifier using the optimal wavelengths of the stylar-end area. It included training, validation and testing stages for Thompson cultivar that was 94%, 90% and 93% and for Jaffa cultivar, was 97%, 91% and 97%, respectively. Appropriate classification results indicate the generalizability of the models developed to identify internal fungal infection of Alternaria alternata in orange fruit.
