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Abstract
The pinking defect in cooked poultry white meat, which is associated with under- cooking, results in serious economic losses to retailers, processors, and producers of such products. This well documented pink discoloration is attributed to specifc in situ factors, such as pH, reducing conditions, and pigments' chemical state and reac- tivity. A simulation of the pink defect and the examination of quantifable changes associated with it can aid in developing alternative processing methods to eliminate potential for pinking. Samples were selected from three color groups (normal, lighter than normal, and darker than normal) of boneless, skinless, chicken breast muscles based on CIE L* color values. In situ changes were induced using sodium chloride, sodium tripolyphos- phate, sodium erythorbate, and sodium nitrite. The subjective pink threshold used in judging pink discoloration was established at CIE a* = 3.8. Muscles in all treatments were subjected to individual injections, followed by tumbling, cooking, and chilling. Both raw and cooked samples were analyzed for color (L*, a*, b*), re ectance spectra, pH, oxidation-reduction potential, and pigments. Simulation of the pink defect was achieved in eight of the sixteen treatment combinations in the light group, nine in the normal group, and ten in the dark group. The simulation was possible both with and without presence of sodium nitrite. The presence of only 1 ppm of sodium nitrite produced signifcant pinking of cooked meat in all three color groups. Pinking was signifcantly afected by lightness (CIE L*) of raw muscles, increased pH and increased reducing conditions. Induced pH, oxidation- reduction potential, metmyoglobin concentration, and nitrosopigment content of raw meat afected the dark color group most and the muscles from the light group the least. The logistic regression demonstrated its feasibility of using raw meat conditions for prediction of the pink defect. The model was able to account for more than 90% of variability using nitrosopigment, pH, and reducing conditions as the variables.