Abstract: Abstract: Due to high protein, low fat, high vitamin and mineral content, beef is regarded as an important meat item which is consumed by human in regular basis. In addition, with the improvement of people's living standard, different quality parameters such as beef tenderness, water retention and other edible quality indicators have become important factors to consumers' satisfaction. Sensory evaluation and shear force measurement are primarily two methods for measurement of beef tenderness, which are now considered as traditional methods. However, with improvement in technology, optical methods are playing important role for rapid, real-time, non-destructive and accurate detection of meat quality parameters. Near-infrared method and hyperspectral imaging method are important methods for non-destructive measurement of beef tenderness. This study focused on the application of hyperspectral image analysis for non-destructive detection of beef tenderness at different points of the sample. Considering the measurement complexity due to complex beef structure as well as the disadvantage of near infrared spectrum for single point detection, this study made use of hyperspectral imaging system to obtain three-dimensional information from each sample. Fifty-six beef samples were obtained from the slaughter house Yuxiangyuan in Beijing and were cut into 3 cm thick slices, vacuum packaged and then stored in 4℃ environment before hyperspectral image acquisition. Reflectance spectrum was obtained for each sample from acquired images. Stepwise regression and genetic algorithm (GA) were used to identify the feature wavelengths, thus reducing the high dimensionality of the hyperspectral data. Eight feature wavelengths were selected, which were used to develop prediction model for measuring the shear force value at each point in the sample. By the principal component analysis (PCA), every sample could acquire 5 PCA images. Gray level co-occurrence matrix (GLCM) was applied to extract 8 main texture parameters from the characteristic band image and the 3 PCA images. Eight wavelengths were extracted from these 8 texture characteristic parameters respectively, with a total of 64 texture variables. Also, from five PCA images 40 texture variables were extracted. These parameters were used to establish a model based on linear discrimination analysis (LDA) and support vector machine (SVM) for beef tenderness. Because the number of principal components has a greater impact on the accuracy of discriminant model, before the training of SVM model, it first needed to select the appropriate number of principal components. Finally, three PCA images which included 95.37% information of every sample were chosen to extract texture parameters. With texture parameters of band images, the prediction set's accuracy of the discriminant model was 88.89%. There had some shortcomings that on one hand, the band images had great influence on the result, and on the other hand, some information may be missed with selected band images, so the texture features of the image of sample main component were used to establish classification model. After analysis and comparison, the accuracy of main component model was 94.44%, which was higher than that of band image model. In addition, the LDA method was more applied to establish classification model than SVM method. The results show that the image texture features can determine the distribution of beef tenderness. The results can provide the theoretical reference for the research on rapid non-destructive detection technique of beef tenderness.