Prediction of pork marbling scores using pattern analysis techniques

Abstract

Marbling is an important technical quality attribute of pork. Its assessment usually corresponds to a subjective score being given by trained panelists based on the marbling standards charts. The purpose of this study was to investigate objective determination of pork marbling using pattern analysis techniques. A line pattern recognition technique called the wide line detector (WLD) and a texture extraction technique based on an improved grey-level co-occurrence matrix (GLCM) were employed and compared. Fifty three fresh pork loin chops from the longissimus dorsi (LD) muscle were collected and their marbling scores were assessed in a plant. Red-Green-Blue (RGB) images of these chops were acquired using a digital camera. The loin eye area was selected as the region of interest (ROI) of the pork images. Marbling was extracted from the ROI by either GLCM or WLD. Proportion of marbling (PM) obtained from WLD or image texture measurement from GLCM (GI) was formulated as indices of the marbling score. Linear regressions based on the PM and GI were carried out at the red, green, and blue channels as well as the combined RGB channels. The results of WLD and GLCM based models showed the effectiveness of pattern analysis techniques for pork marbling assessment. The comparison indicated that the WLD based models had stronger predictive ability for pork marbling score determination than GLCM. The green channel was demonstrated to have the best explanatory for pork marbling assessment no matter which pattern analysis technique used. High correlation coefficients of calibration and validation (Rc = 0.94, Rv = 0.94) of the WLD based linear model at the green channel strongly indicated the great potential of pattern analysis techniques especially the line pattern recognition methods for the accurate and real-time evaluation of pork marbling.