Construction of Prediction Model for Sensory Score of Protein Sausages Based on TPA Model of Texture Analyzer and Its Application
In order to establish an objective and data-based method for evaluating comprehensive sensory scores of animal and plant double protein sausages,in this study,texture profile analysis(TPA)is used to determine 24 groups of samples with different formulas,and a prediction model for comprehensive sensory scores is established by stepwise regression analysis combined with sensory evaluation method,which is applied to response surface experiment to optimize the formula of double protein sausages.The results show that the prediction model for comprehensive sensory scores of animal and plant double protein sausages is Y=-68.110+0.003 × hardness+79.119 × elasticity-0.001 × chewiness-18.393 × resilience,with the correlation coefficient R2 of 0.902 and the adjusted determination coefficient RAdj2 of 0.875.The correlation coefficient R2 of the response surface regression model is 0.962,the adjusted determination coefficient RAdj2 is 0.924,and the predicted coefficient RPred2 is 0.861.The optimal formula of protein sausages is optimized as follows:drawing soy protein mass fraction is 74％,pork mass fraction is 16％,the ratio of soy protein isolate to peanut protein powder is 7∶3 and the ratio of pork fat to lean is 7∶3.The actual value of sensory score of the product prepared under such formula is 8.51,which has a small error with the theoretical predicted value(8.45).The prediction model for comprehensive sensory scores of animal and plant double protein sausages has a high fitting degree,and the actual value is in good agreement with the predicted value.The model is feasible in response surface experiment,which has provided a new idea for the objective and rapid evaluation of the sensory quality of new meat sausages.
animal and plant double protein sausagestexture profile analysiscomprehensive sensory scoreregression modelresponse surface experiment
万方数据
维普
