Prediction of sensory texture of broiler breast meat using instrumental spectral stress–strain analysis



This study was conducted to examine the potential use of the spectral stress–strain analysis (SSSA) in conjunction with Meullenet-Owens razor shear (MORS) in predicting sensory texture of broiler breast meat. Predictive models for the prediction of broiler breast meat texture were established using the MORS in combination with SSSA. Sensory texture characteristics of broiler breast meat deboned at nine different postmortem times and cooked by two different methods were evaluated by descriptive analysis and consumer testing. For the descriptive sensory attributes except moisture release, SSSA method was found to improve the prediction of water-cooked breast meat texture by 18.5% in average for full models (FM) or 11.1% in average for jack-knife optimised models, while the prediction of consumer sensory attributes except just-about-right juiciness was improved by 29.2–31.9% in average for FMs or 8.1–3.3% for jack-knife optimised models for both cooking methods, respectively. Overall, predictive models established by MORS in conjunction with SSSA could be successfully used to improve the estimation of broiler breast meat sensory texture.