CORRELATING IMAGE TEXTURE FEATURES EXTRACTED BY FIVE DIFFERENT METHODS WITH THE TENDERNESS OF COOKED PORK HAM: A FEASIBILITY STUDY

Being one of the most important attributes that affect the eating quality of meat products, tenderness is still mostly evaluated using sensory panel and instrumental methods. It is desirable to develop a fast, non-destructive, accurate, and on-line technique for tenderness evaluation of meat products. As an objective, consistent, rapid, and automatic technique, computer vision could be employed to complete such a task. The relationships between tenderness and image texture features of pork ham were investigated in this study. Fifty observations were made, and shear force was measured as the indicator of tenderness. Five approaches were employed to characterize the image texture features of pork ham, including the common first-order gray-level statistics (FGLS), run length matrix (RLM), gray-level co-occurrence matrix (GLCM), fractal dimension (FD), and wavelet transform (WT) based method. After that, both simple correlation analysis and partial least squares regression (PLSR) analysis were carried out to study the relationships between the tenderness of pork ham and the extracted image texture features. It was found that the image texture features extracted using the WT-based method had the best relationships with the tenderness of pork ham. However, there were no significant correlations found between the tenderness of pork ham and the image texture features extracted by the traditional methods, including FGLS, RLM, and GLCM (P > 0.05).