A rapid and effective near infrared (NIR; 900-1700 nm) hyperspectral imaging method to determine intramuscular fat (IMF) content of pork at the last 6 ribs of the round rib end was developed and tested. Pattern analysis techniques including Gabor filter, grey-level co-occurrence matrix (GLCM), and wide line detector (WLD) were applied to process hyperspectral images into data from which spectral and image features, including texture-spectral, texture and line features were extracted. Among all spectral and image features of the 6 ribs' images, the first derivative of Gabor filtered mean spectra showed the strongest correlation with IMF content, and was therefore selected as the optimal feature for prediction of IMF content at any given rib number. Multiple linear regression (MLR) was exploited to build prediction models between IMF content at rib end and at 6 different rib locations. Leave-one-out cross validation was used to test the robustness of established models. The correlation coefficients of calibration, cross validation and full validation (R-e, R-ev, R-fv, respectively) were used to assess model accuracy. All six MLR models showed a good performance (R-c >= 0.90, R-cv >= 0.87, R-fv >= 0.81), with that for IMF content at the 2nd/3rd last rib showing the best predictive ability (R-c = 0.96, R-cv = 0.95, R-fv = 0.83). The Gabor filter-MLR models were applied to each pixel in an image to successfully derive distribution maps and visualize IMF content distribution in the loins. Results demonstrated that the conventional determination of IMF content of pork at different ribs along a single Longissimus dorsi could be replaced by pattern analysis technique-processed hyperspectral images of rib ends. (C) 2016 Published by Elsevier Ltd.