Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest phase. The automation of this task is still a challenge for the citrus industry. In this study, the potential application of hyperspectral imaging, which combines conventional imaging and spectroscopy to simultaneously acquire both spatial and spectral information from an object, was evaluated for automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Hyperspectral images of sound and decayed navel oranges were acquired in the wavelength range of 325-1100 nm. Principal component analysis (PCA) was applied to a dataset comparing of average spectra from decayed and sound tissue to reduce the dimensionality of data and to observe the ability of visible near infrared (Vis-NIR) hyper-spectra to discriminate data from two classes. And, a mean normalization step is applied prior to PCA to reduce the effect of sample curvature on spectral profiles. In this case it was observed that sound and decayed spectra were separable along the resultant first principal component (PC1) axis, then, four wavelength images centered at 575, 698, 810 and 969 nm were selected as the characteristic wavelength images by analyzing the weight coefficients of PC1 in order to develop a fast classification method for establishing an on-line multispectral imaging system. Subsequently, a combination image, which obtained by multiplying the characteristic weight coefficients by corresponding to mean-normalized characteristic wavelength images of each orange sample, was calculated for determination of decayed fruits. Based on the obtained multispectral combination image, the technique of intensity slicing as one of the pseudo-color image processing methods is used to transform the combination image into a 2-D visual classification image that would enhance the contrast between sound and decayed classes. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudo-color image coupled with a simple thresholding method. For the investigated 210 naval orange samples including 80 sound fruits and 130 infected fruits, the total success rate is 100% for training set and 98.6% for test set with no false negatives, respectively, indicating that the proposed multispectral algorithm here is capable of detecting decay caused by penicillium digitatum in naval orange fruit using only four key wavelength images. The results from this study could be used for development of a non-destructive monitoring system for rapid detection of decayed citrus on the processing line. The idea behind the proposed algorithm can be extended to detect the non-visible damages of other fruit, such as slight bruise and chilling injury in apples. (C) 2016 Published by Elsevier B.V.