Rui Xiong

Changes in Broiler Breast Fillet Tenderness, Water-Holding Capacity, and Color Attributes during Long-Term Frozen Storage

Freezing is the most common and efficient way to maintain the quality of poultry products for long periods of time. However, tougher texture, discoloration, and drying have been reported as a result of long-term frozen storage. The impact of freezing on the tenderness, water-holding capacity, and color of broiler breast fillets was investigated for up to 8 mo. A total of 160 birds were deboned at either 2 or 6 h postmortem (PM). All deboned left fillets were frozen and stored at -18 degrees C for up to 8 mo, while the corresponding right fillets were assessed for texture approximately 24 h after deboning as a control measurement without any freezing treatment. Tenderness was measured by the Meullenet-Owens razor shear. Thaw loss, cooking loss, moisture content, color, and muscle shape profiles were also evaluated. No difference in tenderness was observed during the first 2 mo compared to the control (0 mo), but significantly decreased between 2 and 4 mo. The 8-mo-old fillets were the least tender, with a 31.5% increase in shear energy between 4 and 8 mo. Moisture content of cooked meat gradually decreased, showing a significant drop between 2 and 6 mo of storage, while thaw and cooking loss consistently increased over the entire storage period. The color of the frozen fillets tended to be darker, redder, and less yellow than the control, with increased storage duration. The results suggest that for optimal tenderness, frozen broiler breast fillets are best consumed within 2 mo of freezing.

Multivariate and Probabilistic Analyses of Sensory Science Problems: Meullenet/Multivariate

Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. Applied in approach and written for non-statisticians, the text is aimed at sensory scientists who deal mostly with descriptive analysis and consumer studies. Multivariate and Probabilistic Analyses of Sensory Science Problems offers simple, easy-to-understand explanations of difficult statistical concepts and provides an extensive list of case studies with step-by-step instructions for performing analyses and interpreting the results.