Assessment of fresh pork color with color machine vision

Currently, fresh pork color is visually evaluated using either the Japanese Pork Color Standards (JPCS) or the National Pork Producers Council Pork Qu ality Standards (NPPC) as a reference. Although useful, visual evaluation o f meat color can vary with evaluator and may be quite expensive. In this st udy, three separate studies were used to compare the ability of color machi ne vision (CMV) and untrained panelists to evaluate pork color. Panels visu ally evaluated over 200 pork loin chops using either the JPCS or NPPC refer ence standards. Results from each panel were used to evaluate the ability o f the CMV to sort pork loin chops based on the same criteria. Representativ e samples, typical of each color class, were used to train neural-network-b ased image processing software. After training, the CMV system was used to evaluate quality classes of pork samples based on color distribution. Class ification by CMV was compared with the average panel score, rounded to the nearest integer. Training the CMV system using images of actual meat sample s resulted in a stronger correlation to panel scores than training with eit her set of artificial color standards. Agreement between the CMV system and the panels was as high as 90%. Agreement between individual panelists and the integer panel average (52 to 85%) was less than that observed for CMV c lassification. Finally, the on-line performance of CMV using a laboratory c onveyor system was simulated by repeatedly classifying 37 samples at a spee d of 1 sample per second. Collectively, these results demonstrate that CMV is a rapid and repeatable means of evaluating pork color.