Abstract: Abstract: Bruise is one of the main defects of apple, which could be caused by impact or mechanical damage during harvest and handling stages. Early detection of slight bruises on apples is important for an automatic apple sorting system. A hyperspectral imaging system with the wavelength range of 450-1 000 nm was built for detecting bruises happened in half an hour on 'Akesu' apples. The hyperspectral imaging system was used as a powerful tool to determine the effective wavelengths that could be used for the detection of bruises on apples. Principal component analysis (PCA) is a very effective method for data dimension reduction and feature extraction of the hyperspectral data cube. However, too many wavelengths from entire spectrum data were usually used to perform the PCA operation. Therefore, the performance of PCA was degraded due to a lot of noises. In addition, too many effective wavelengths were also not effective to develop a multispectral system. In this study, segmented PCA was used to select the effective wavelengths. First, PCA was conducted on the three spectral ranges 450-780 nm, 450-1 000 nm and 780-1 000 nm. Then, the optimal wavelength region 780-1 000 nm for bruise detection was selected by visually contrasting and analyzing the obtained principal component (PC) images of the PCA on the three different wavelength regions. Two effective wavelengths 820 and 970 nm with weighing coefficients at peaks and valleys were determined using the loading coefficients of the PC2 image of PCA on 780-1 000 nm. The PC2 image obtained from PCA on two effective wavelengths 820 and 970 nm was used for bruise detection. First, the PC2 images were processed by applying the Gaussian blur filter. Then, a bruise detection algorithm based on the two effective wavelengths and a global threshold method was developed. Independent validation set of 25 intact and 25 bruised apples was used to evaluate the performance of the developed algorithm. Results show that 100% of the intact apples are correctly classified, 96% of the bruised apples are correctly recognized and the overall detection accuracy is 98%. The effective wavelengths in this study can lay a foundation for the development of a multispectral imaging system for bruise detection on apples.