Application of principal component analysis with instrumental analysis and sensory evaluation for assessment of chicken breast meat juiciness.

1. The objectives of this study were to use principal component analysis (PCA) to analyse the variability of the three instrumental and 14 descriptive sensory properties of chicken breast meat. The meat was cooked until the internal temperature reached 85°C and further cooked for 0, 20, and 40 min. The second objective was to identify the most critical variables for assessing meat juiciness.2. Cooking loss and moisture content exhibited high correlation with sensorial moisture release and mouth feel.3. The distribution of objects on the axes of the first two principal components (PCs) enabled the identification of three groups undergoing different cooking durations. The four major PCs explained 80.0% of the total variability.4. Cooking loss, moisture content, water-holding capacity, sensorial moisture release and mouth feel were demonstrated as the most effective variables for the first two PCs. PCA with instrumental and sensory analyses proved an effective procedure for systematically and comprehensively judging chicken meat juiciness.

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 Quality Standards (NPPC) as a reference. Although useful, visual evaluation of meat color can vary with evaluator and may be quite expensive. In this study, three separate studies were used to compare the ability of color machine vision (CMV) and untrained panelists to evaluate pork color. Panels visually evaluated over 200 pork loin chops using either the JPCS or NPPC reference standards. Results from each panel were used to evaluate the ability of the CMV to sort pork loin chops based on the same criteria. Representative samples, typical of each color class, were used to train neural-network-based image processing software. After training, the CMV system was used to evaluate quality classes of pork samples based on color distribution. Classification by CMV was compared with the average panel score, rounded to the nearest integer. Training the CMV system using images of actual meat samples resulted in a stronger correlation to panel scores than training with either 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 classification. Finally, the on-line performance of CMV using a laboratory conveyor system was simulated by repeatedly classifying 37 samples at a speed of 1 sample per second. Collectively, these results demonstrate that CMV is a rapid and repeatable means of evaluating pork color.